DESC:PREAMBLE OF THE DESCRIPTION- COMPLETE
The following specification particularly describes the invention and the manner in which it is to be performed.

SYSTEMS AND METHODS FOR GENERATING USER-PERSONA BASED GEO-SPATIAL INSIGHTS AND RECOMMENDATIONS FOR ASSETS
CROSS REFERENCE
This Application is based upon and derives the benefit of Indian Provisional Application Number 202341050990 filed on July 28, 2023, the contents of which are incorporated herein by reference.
FIELD OF INVENTION
Embodiments of the present disclosure relate to asset management systems and more particularly relate to systems and methods for generating user-persona based geo-spatial insights and recommendations for assets.
BACKGROUND
Typically, the real estate market is characterized by a multitude of online listing platforms, each providing access to a vast number of assets. These platforms function as repositories for extensive datasets encompassing a significant volume of property information. However, the act of searching for a suitable property across these numerous platforms is known to be a cumbersome and time-consuming process. This challenge is further intensified by several significant difficulties.
Conventionally, some of the existing real estate platforms employ a filter-based search methodology to generate an outcome of an ideal property based on specific parameters fed by a potential buyer. However, such existing filter-based search methodology in the conventionally available real estate platforms inhibit the potential buyers from identifying locations that precisely align with their emotional aspects of requirements. Furthermore, the existing sub-filtering search methodology is configured to prioritize limited properties, potentially limiting the exposure of the potential buyer a spectrum of other relevant and available properties. Specifically, existing filter-based methods struggle to identify locations that precisely match user requirements. They might exclude Geographically Relevant Options (GROs) that fall outside the defined filter boundaries. These methods prioritize properties that strictly adhere to such filters. This can limit a user's exposure to a wider range of potentially suitable properties even if they fall slightly outside the specified parameters.
Conventional platforms have voluminous datasets with state and parameterized access and retrieval systems. This significantly curtails the efficacy of a divergent contextual search input by limiting the results to a fixed and linear output of dataset. Further, such existing platforms have limited access to data from critical third-party sources and can lead to the creation of data silos within the platform. This hinders the platform’s ability to incorporate comprehensive/multivariant information that could provide a more holistic search results. Moreover, significant inconsistencies can be observed in data quality across various existing platforms. The presence of inaccurate or irrelevant information can impede the search process and lead to misleading results for users.
Hence, there is a need for an advanced systems and methods for generating user-persona based geo-spatial insights and recommendations for assets, in order to address the aforementioned issues.

SUMMARY
This summary is provided to introduce a selection of concepts, in a simple manner, which is further described in the detailed description of the disclosure. This summary is neither intended to identify key or essential inventive concepts of the subject matter nor to determine the scope of the disclosure.
In accordance with an embodiment of the present disclosure, systems for generating user-persona based geo-spatial insights and recommendations for assets are disclosed. The system receives a request for determining one or more desired assets for an end user from a user device. The request includes one or more user preferences, and user personal information. Further, the system identifies the type of the end user based on a user profile associated with the end user. The user profile is determined based on the received request. Further, the system predicts one or more human emotion parameters associated with the end user based on the received request and the identified type of the end user using an artificial intelligence-based user persona model. The one or more human emotion parameters corresponds to a user-persona data of the end user. Further, the system generates a user-specific persona code for the end user based on the predicted one or more human emotion parameters. Further, the system determines at least one of a geo-spatial data, and spatial-temporal data for the received request based on a plurality of external data sources, and a pre-stored asset table. Further, the system generates a spatial-temporal chromatic identifier for the end user based on at least one of the determined geo-spatial data and the spatial-temporal data. The spatial-temporal chromatic identifier includes at least one of insights attribute, spatial attributes, and foresights attributes corresponding to one or more desired assets. Further, the system correlates the user-specific persona code with the generated spatial-temporal chromatic identifier using an artificial intelligence-based data orchestration model . Further, the system predicts the one or more desired assets suitable for the end user based on the correlated user-specific persona code with the spatial-temporal chromatic identifier. Furthermore, the system predicts dynamic pricing values for each of the predicted one or more desired assets using an artificial intelligence-based pricing model. Furthermore, the system outputs the one or more desired assets and the predicted dynamic pricing values for each of the determined one or more desired assets on a user interface of a user interface.
Further, in accordance with an embodiment of the present disclosure, methods for generating user-persona based geo-spatial insights and recommendations for assets are disclosed. The method includes receiving a request for determining one or more desired assets for an end user from a user device. The request includes one or more user preferences, and user personal information. Further, the method includes identifying the type of the end user based on a user profile associated with the end user. The user profile is determined based on the received request. Further, the method includes predicting one or more human emotion parameters associated with the end user based on the received request and the identified type of the end user using an artificial intelligence-based user persona model. The one or more human emotion parameters corresponds to a user-persona data of the end user. Further, the method includes generating a user-specific persona code for the end user based on the identified one or more human emotion parameters. Further, the method includes determining at least one of a geo-spatial data, and spatial-temporal data in real-time for the received request based on a plurality of external data sources, and a pre-stored asset table. Further, the method includes generating a spatial-temporal chromatic identifier for the end user based on at least one of the determined geo-spatial data and the spatial-temporal data. The spatial-temporal chromatic identifier includes at least one of insights attribute, spatial attributes, and foresights attributes corresponding to one or more desired assets.
Further, the method includes correlating the user-specific persona code with the generated spatial-temporal chromatic identifier using an artificial intelligence-based data orchestration model. Further, the method includes predicting the one or more desired assets suitable for the end user based on the correlated user-specific persona code with the spatial-temporal chromatic identifier. Further, the method includes predicting dynamic pricing values for each of the predicted one or more desired assets using an artificial intelligence-based pricing model and outputting the one or more desired assets and the predicted dynamic pricing values for each of the determined one or more desired assets on a user interface.
To further clarify the advantages and features of the present disclosure, a more particular description of the disclosure will follow by reference to specific embodiments thereof, which are illustrated in the appended figures. It is to be appreciated that these figures depict only typical embodiments of the disclosure and are therefore not to be considered limiting in scope. The disclosure will be described and explained with additional specificity and detail with the appended figures.
BRIEF DESCRIPTION OF DRAWINGS
The disclosure will be described and explained with additional specificity and detail with the accompanying figures in which:
FIG. 1 illustrates a block diagram representation of an exemplary computing system for generating user-persona based geo-spatial insights and recommendations for assets, in accordance with an embodiment of the present disclosure;
FIG. 2 illustrates a block diagram representation of an exemplary computing unit, such as those shown in FIG. 1, for generating user-persona based geo-spatial insights and recommendations for assets, in accordance with an embodiment of the present disclosure;
FIG. 3 illustrates a block diagram representation of an exemplary computing unit for generating user-persona based geo-spatial insights and recommendations for assets, in accordance with another embodiment of the present disclosure;
FIG. 4 illustrates a process flow diagram representation of an exemplary method for generating user-persona based geo-spatial insights and recommendations for assets, in accordance with an embodiment of the present disclosure;
FIG. 5 illustrates a schematic diagram representation of an exemplary computing system for generating user-persona based geo-spatial insights and recommendations for assets, in accordance with another embodiment of the present disclosure; and
FIG. 6A-B are snapshot representations of an example graphical user interface screens for generating user-persona based geo-spatial insights and recommendations for assets, in accordance with another embodiment of the present disclosure.
Further, those skilled in the art will appreciate that elements in the figures are illustrated for simplicity and may not have necessarily been drawn to scale. Furthermore, in terms of the construction of the device, one or more components of the device may have been represented in the figures by conventional symbols, and the figures may show only those specific details that are pertinent to understanding the embodiments of the present disclosure so as not to obscure the figures with details that will be readily apparent to those skilled in the art having the benefit of the description herein.

DETAILED DESCRIPTION OF THE DISCLOSURE
For the purpose of promoting an understanding of the principles of the disclosure, reference will now be made to the embodiment illustrated in the figures and specific language will be used to describe them. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended. Such alterations and further modifications in the illustrated system, and such further applications of the principles of the disclosure as would normally occur to those skilled in the art are to be construed as being within the scope of the present disclosure. It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the disclosure and are not intended to be restrictive thereof.
In the present document, the word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any embodiment or implementation of the present subject matter described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.
The terms “comprise,” “comprising,” or any other variations thereof, are intended to cover a non-exclusive inclusion, such that one or more devices or sub-systems or elements or structures or components preceded by “comprises…. a” does not, without more constraints, preclude the existence of other devices, sub-systems, additional sub-modules. Appearances of the phrase “in an embodiment,” “in another embodiment” and similar language throughout this specification may, but not necessarily do, all refer to the same embodiment.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this disclosure belongs. The system, methods, and examples provided herein are only illustrative and not intended to be limiting.
A computer system (standalone, client or server computer system) is configured by an application may constitute a “module” (or “subsystem”) that is configured and operated to perform certain operations. In one embodiment, the “module” or “subsystem” may be implemented mechanically or electronically, so a module includes dedicated circuitry or logic that is permanently configured (within a special-purpose processor) to perform certain operations. In another embodiment, a “module” or “subsystem” may also comprise programmable logic or circuitry (as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software to perform certain operations.
Accordingly, the term “module” or “subsystem” should be understood to encompass a tangible entity, be that an entity that is physically constructed permanently configured (hardwired) or temporarily configured (programmed) to operate in a certain manner and/or to perform certain operations described herein.
Embodiments of the present disclosure may include systems for generating user-persona based geo-spatial insights and recommendations for assets. Further, the computing system receives a request for determining one or more desired assets for an end user from a user device. The request includes one or more user preferences, and user personal information. Further, the computing system identifies the type of the end user based on a user profile associated with the end user. The user profile is determined based on the received request. Further, the computing system predicts one or more human emotion parameters associated with the end user based on the received request and the identified type of the end user using an artificial intelligence-based user persona model. The one or more human emotion parameters corresponds to a user-persona data of the end user. Further, the computing system generates a user-specific persona code for the end user based on the predicted one or more human emotion parameters. Further, the computing system determines at least one of a geo-spatial data, and spatial-temporal data in real-time for the received request based on a plurality of external data sources, and a pre-stored asset table. Further, the computing system generates a spatial-temporal chromatic identifier for the end user based on at least one of the determined geo-spatial data and the spatial-temporal data. The spatial-temporal chromatic identifier includes at least one of insights attribute, spatial attributes, and foresights attributes corresponding to one or more desired assets. Further, the computing system correlates the user-specific persona code with the generated spatial-temporal chromatic identifier using an artificial intelligence-based data orchestration model. Further, the computing system predicts the one or more desired assets suitable for the end user based on the correlated user-specific persona code with the spatial-temporal chromatic identifier. Furthermore, the computing system predicts dynamic pricing values for each of the predicted one or more desired assets using an artificial intelligence-based pricing model. Furthermore, the computing system outputs the one or more desired assets and the predicted dynamic pricing values for each of the determined one or more desired assets on a user interface.
Further, in accordance with an embodiment of the present disclosure, methods for generating user-persona based geo-spatial insights and recommendations for assets are disclosed. The method includes receiving a request for determining one or more desired assets for an end user from a user device. The request includes one or more user preferences, and user personal information. Further, the method includes identifying the type of the end user based on a user profile associated with the end user. The user profile is determined based on the received request. Further, the method includes predicting one or more human emotion parameters associated with the end user based on the received request and the identified type of the end user using an artificial intelligence-based user persona model. The one or more human emotion parameters corresponds to a user-persona data of the end user. Further, the method includes generating a user-specific persona code for the end user based on the predicted one or more human emotion parameters. Further, the method includes determining at least one of a geo-spatial data, and spatial-temporal data in real-time for the received request based on a plurality of external data sources, and a pre-stored asset table. Further, the method includes generating a spatial-temporal chromatic identifier for the end user based on at least one of the determined geo-spatial data and the spatial-temporal data. The spatial-temporal chromatic identifier includes at least one of insights attributes, spatial attributes, and foresights attributes corresponding to one or more desired assets.
Further, the method includes correlating the user-specific persona code with the generated spatial-temporal chromatic identifier using an artificial intelligence-based data orchestration model. Further, the method includes predicting the one or more desired assets suitable for the end user based on the correlated user-specific persona code with the spatial-temporal chromatic identifier. Further, the method includes predicting dynamic pricing values for each of the predicted one or more desired assets using an artificial intelligence-based pricing model and outputting the one or more desired assets and the predicted dynamic pricing values for each of the determined one or more desired assets on a user interface.
Referring now to the drawings, and more particularly to FIG. 1 through FIG. 6A-B, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments, and these embodiments are described in the context of the following exemplary system and/or method.
FIG. 1 illustrates a block diagram representation of an exemplary computing system 100 for generating user-persona based geo-spatial insights and recommendations for assets, in accordance with an embodiment of the present disclosure. According to FIG. 1, the computing system 100 for generating user-persona based geo-spatial insights and recommendations for assets may include one or more user devices 102-1, …, 102-N (collectively referred herein as one or more user devices 102) associated with one or more end users. The one or more user devices 102 are communicatively coupled to a computing unit 104 via a network 110. Further, the computing system 100 may include a database 108 communicatively coupled to the computing unit 104 and the one or more user devices 102 via the network 110.
In an exemplary embodiment of the present disclosure, the one or more user devices 102 may include, but not limited to, smart phones, laptop computers, desktop computers, tablet computers, wearable devices, smart watches, and the like. Further, the one or more end users may include, but not limited to, property owners, property buyers, property leasers, property lessee, property renters, a financier or lender, a builder, developers, a real estate agent, a surveyor, a project manager, an architect, individuals, organizations or entities, and the like. Further, the computing system 100 for generating user-persona based geo-spatial insights and recommendations for assets may be accessed by the one or more users from the one or more user devices 102 by using for example, but not limited to, web application, mobile application, or the like.
Further, the network 110 may include, but not limited to, physical networks such as Local Area Network (LAN), Wide Area Network (WAN), Metropolitan Area Network (MAN), Personal Area Network (PAN), wireless networks such as Cellular Networks, Satellite Networks, Wireless Fidelity (Wi-Fi) Networks, and the like.
Further, the database 108 may be configured to store, manage, organize, distribute, and protect data relevant to the system 100 for generating user-persona based geo-spatial insights and recommendations for assets. Furthermore, the computing unit 104 of the computing system 100 for generating user-persona based geo-spatial insights and recommendations for assets may include plurality of modules 106 configured to be executed by one or more hardware processors (not shown). Further, the computing system 100 may include one or more external data sources 112-1, …, 112-N (collectively referred herein as one or more external data sources 112) communicatively coupled to, but not limited to, the one or more user devices 102, the computing unit 104, and the database 108 via the network 110. Further, the one or more external data sources 112 may include, but not limited to, housing regulatory authorities, state or central revenue departments, regional transport offices, local municipal bodies, and the like.
In some embodiments, the computing unit 104 may include a cloud interface, cloud hardware and OS, a cloud computing platform, and a database. The cloud interface enables communication between the cloud computing platform and the user device 102. Also, the cloud interface enables communication between the cloud computing platform and the web application. The cloud hardware and OS may include one or more servers on which an operating system is installed and including one or more processing units, one or more storage devices for storing data, and other peripherals required for providing cloud computing functionality. The cloud computing platform is a platform which implements functionalities such as data storage, data analysis, data processing, data communication on the cloud hardware and OS via APIs and algorithms and delivers the aforementioned cloud services. The cloud computing platform may include a combination of dedicated hardware and software built on top of the cloud hardware and OS. As used herein, “cloud computing environment” refers to a processing environment comprising configurable computing physical and logical assets, for example, networks, servers, storage, applications, services, and the like and data distributed over the cloud platform. The cloud computing environment or the computing system 100 provides on -demand network access to a shared pool of the configurable computing physical and logical assets. The server may include one or more servers on which the OS is installed. The servers may comprise one or more processors, one or more storage devices, such as, memory units, for storing data and machine -readable instructions for example, applications and application programming interfaces (APIs), and other peripherals required for providing cloud computing functionality.
In some embodiments, the computing unit 104 may be a remote server, a web server, an edge server, or a blockchain node in a blockchain network.
Further, the computing unit 104 receives a request for determining one or more desired assets for an end user from a user device 102. The request includes one or more user preferences, and user personal information. Further, the computing unit 104 identifies the type of the end user based on a user profile associated with the end user. The user profile is determined based on the received request. Further, the computing unit 104 predicts one or more human emotion parameters associated with the end user based on the received request and the identified type of the end user using an artificial intelligence-based user persona model . The persona model uses pre-trained and assisted learning based LLM (Large Language Model) to derive a behavior identifier by using extrinsic personality from the Hat-based technique (pre-profiling the end user). The model learns from the user's interactions and interpretations to create empathetic and inferential output. The system will interact in a multi-modal manner during the interaction to interpret the user’s intentions and emotions using deep-learning-based approaches.
Based on the pre-profiled behavior identifier, the system will generate a spatial-temporal chromatic identifier, which will be used in the recommendation of asset to the user. For example, the “Risk Averse” behavioral identifier associated with personality parameters such as “risk taking, adventurous,…and the like”, then the computing unit 104 accordingly provide personalized information on the “High growth potential” assets.
The one or more human emotion parameters corresponds to a user-persona data of the end user. Further, the computing unit 104 generates a user-specific persona code for the end user based on the predicted one or more human emotion parameters.
The computing unit 104 is configured to render techniques to capture human emotions and translate them into the color-based persona chromatic value.in order to generate and match the geospatial data, the temporal data, along with the metadata for the hyper- identification of properties. For example, human emotion such as “upcoming properties in around great neighborhood,” may be translated into the color-based persona chromatic value. by understanding the user’s emotions and expectations like time period (how soon?) for “upcoming” and parameters for “great neighborhood.” The color-based persona chromatic value is used for hyper-identification of properties, that is; properties whose color shades/value are in the closer range of “human emotion” color-coded shades/value will be shown to the end user, as part of the search criteria.
Further, the computing unit 104 determines at least one of a geo-spatial data, and spatial-temporal data in real-time for the received request based on a plurality of external data sources 112, and a pre-stored asset table. Further, the computing unit 104 generates a spatial-temporal chromatic identifier for the end user based on at least one of the determined geo-spatial data and the spatial-temporal da ta. The spatial-temporal chromatic identifier includes at least one of insights attribute, spatial attributes, and foresights attributes corresponding to one or more desired assets. The spatial-temporal chromatic identifier is mapped to discrete assets and descriptions to create a multilevel combinatorial definition of an asset in terms of various spatial-temporal attributes like “Legality,” “Locality,” “Centrality,” and “Utility.” The chromatic identifier is a hexadecimal-based unique chromatic value comprising four part 64-bit codes. ANN (Artificial Neural Network) is used which works on multiple layers that contain weighted combinations in terms of Blue-Green infrastructures, connectivity, land use/category, lineaments (ground water), public utilities, upcoming highway and infrastructures, walkability to important Poi (Point of Interests). The network “learns” by changing the weights of each combination before generating the unique code for spatial classifiers (“Legality,” “Locality,” “Centrality,” “Utility”), which is expressed in a unique format.
The advantage of using hexadecimal code is that large data can be represented using fewer digits. This way every asset can be uniquely represented in spatial-temporal system and hyper-personalized discovery criteria to match user persona.
Further, the computing unit 104 correlates the user-specific persona code with the generated spatial-temporal chromatic identifier using an artificial intelligence-based data orchestration model. The data orchestration model may be for example, but not limited to, Deep Learning AI model (Graph Neural Networks algorithm) for establishing the relationship between persona code (representing a particular trait) to set of data layers. For example, when it detects a persona code highlighting “Risk Aversion” then the data orchestrator model may recommend assets with low-risk exposure. Further, the computing unit 104 predicts the one or more desired assets suitable for the end user based on the correlated user-specific persona code with the spatial-temporal chromatic identifier . Furthermore, the computing unit 104 predicts dynamic pricing values for each of the predicted one or more desired assets using an artificial intelligence-based pricing model. In an example embodiment, the predicted dynamic pricing is validated with market data, which contains both historical and recent transactions. Furthermore, the computing unit 104 outputs the one or more desired assets and the predicted dynamic pricing values for each of the determined one or more desired assets on a user interface.
Those of ordinary skilled in the art will appreciate that the hardware depicted in FIG .1 may vary for particular implementations. For example, other peripheral devices such as an optical disk drive and the like, Local Area Network (LAN), Wide Area Network (WAN), Wireless (for example, Wi-Fi) adapter, graphics adapter, disk controller, input/output (1/0) adapter also may be used in addition or in place of the hardware depicted. The depicted example is provided for the purpose of explanation only and is not meant to imply architectural limitations with respect to the present disclosure.
Those skilled in the art will recognize that, for simplicity and clarity, the full structure and operation of all data processing systems suitable for use with the present disclosure is not being depicted or described herein. Instead, only so much of a computing unit 104 as is unique to the present disclosure or necessary for an understanding of the present disclosure is depicted and described. The remainder of the construction and operation of the computing unit 104 may conform to any of the various current implementation and practices known in the art.
FIG. 2 illustrates a block diagram representation of an exemplary computing unit 104, such as those shown in FIG. 1, for generating user-persona based geo-spatial insights and recommendations for assets, in accordance with an embodiment of the present disclosure. Further, the computing unit 104 may include one or more hardware processors 202, a memory 204 and a storage unit 206.
Further, the one or more hardware processors 202, the memory 204 and the storage unit 206 are communicatively coupled through a system bus 208 or any similar mechanism. Further, the memory 204 may include a plurality of modules 106 in the form of programmable instructions executable by the one or more hardware processors 202. Further, the plurality of modules 106 may include a request receiver module 210, an intelligent data pipeline module 212, a user persona prediction module 214, a chromatic identifier module 216, an Artificial Intelligence-based data orchestration module 218, an Artificial-Intelligence based assets and pricing prediction module 220, and a data output module 222.
Further, the request receiver module 210 may be configured to receive a request for determining one or more desired assets for one or more end users from one or more user devices 102-1, …, 102-N. The request may include one or more user preferences, and user personal information. Further, the one or more desired assets may include, but not limited to, land, residential properties, commercial properties, and agricultural properties. Further, the one or more user preferences may include filters, semantic search attributes, user persona, user likes, and historical data of the end user. Furthermore, the semantic search attributes may include, but not limited to, a safe neighborhood, and good investment potential.
Further, the request receiver module 210 may be configured to identify the type of the end user based on a user profile associated with the end user. The user profile is determined based on the received request. Further, in identifying the type of the end user based on the user profile associated with the end user, the request receiver module 210 may be configured to parse the received request for identifying a context of the received request. Further, the request receiver module 210 may be configured to identify the user profile associated with the end user based on the identified context and the user personal information. Furthermore, the request receiver module 210 may be configured to identify the type of the end user and one or more privileges associated with the end user based on the identified user profile. Further, the end user may include, but not limited to, property owners, property buyers, property leasers, property lessee, property renters, a financier or lender, a builder, developers, a real estate agent, a surveyor, a project manager, an architect, individuals, organizations or entities, and the like. Further, the one or more privileges may include, but not limited to, data access, functionality access, and the like. Further, the data access may include ability to view or modify specific types of data such as, for example, but not limited to, the real estate agent may view all listings of assets, while the landowners may only view own listing, and the like. Further, the functionality access may include permission to perform certain actions within the system such as, for example, but not limited to, the real estate agent may initiate contact with buyers, while landowners may manage rental agreements, and the like.
Further, the intelligent data pipeline module 212 may be configured to generate data related to, but not limited to, public transport, schools, restaurants, entertainment arenas, sports complexes, hospitals, upcoming infrastructure, Agricultural Produce Market Committees (APMC), warehouses, cold storages, banks, and the like. Further, the intelligent data pipeline module 212 may be configured to generate geospatial data based on one or more external data sources 112.
Further, the user persona prediction module 214 may be configured to predict one or more human emotion parameters associated with the end user based on the received request and the identified type of the end user. The one or more human emotion parameters corresponds to a user-persona data of the end user. The user persona data may include, but not limited to, demographics, interests, preferences, past behavior, and the like. Further, in predicting the one or more human emotion parameters associated with the end user, the user persona prediction module 212 may be configured to evaluate the one or more user preferences specified in the received request by the end user based on user specific historical data. Further, the user persona prediction module 212 may be configured to determine user behavioral data, and user emotional data associated with the end user at a real-time from a one or more external data sources 112 and a data stored on the one or more user devices 102, pre-profiling techniques, applications used by the end user. Further, the user behavioral data may include, but not limited to, a browser history, a cache data, a watch data, a user purchase history, user social media profiles, and user activities across the applications and the like. Further, the watch data may include, but not limited to, Activity data, application usage data, Screen interaction time, and the like. Further, the user purchase history may include, but not limited to, products purchased, purchase frequency, purchase history, purchase platform, return and exchange history, and the like. Further, the user social media profiles may include, but are not limited to, a public profile information, social connections, content creation, an engagement data, and the like. Further, the public profile information may include but not limited to, name, location, interests, demographics, and the like. Further, the social connections may include but not limited to, friends, followers, groups, and the like. Further, the content creation may include but not limited to, posts, photos, videos shared, liked, and posted by the user, and the like. Further, the engagement data may include but not limited to, Likes, comments, shares received on the user's content, and the like. Further, the user persona prediction module 212 may be configured to map the evaluated one or more user preferences specified in the received request by the end user with each of the determined the user behavioral data, the user emotional data, the data stored on the user device 102, the applications used by the end user, the browser history, the cache data, the watch data, the user purchase history, the user social media profiles, and the user activities across the applications using the artificial-intelligence based user persona model . The AI based user persona model may be a large language model (LLM). Further, the user persona prediction module 212 may be configured to predict the one or more human emotion parameters associated with the end user based on the mapping.
Further, the chromatic identifier module 216 may be configured to generate a user-specific persona code for the end user based on the predicted one or more human emotion parameters. In generating the user-specific persona code for the end user based on the predicted one or more human emotion parameters, the chromatic identifier module 216 may be configured to classify the end user into one of user persona category based on the predicted one or more human emotion parameters. The user persona category may include one of the interest level categories, a personality level category, a user temper tendency category, an emotional level category, and a behavior level category. Further, the interest level category may refer to users grouped based on specific interests and preferences. Further, the personality level category may refer to users grouped based on their personality traits. Further, the user temper tendency category may refer to users grouped based on tendency to react in certain ways. Further, the emotional level category may refer to users grouped based on dominant emotional state when interacting with a product or service. Further, the behavior level category may refer to users grouped based on actions and how users interact with a product or service. Further, the chromatic identifier module 216 may be configured to generate user-specific persona code for the end user. Further, the user-specific persona code may be based on the predicted one or more human emotion parameters. Further, the generation of user-specific persona code may include identifying metadata elements. Further, the metadata elements may include forming a metadata layer for each of the one or more human emotion parameters based on classification category. Further, the chromatic identifier module 216 may be configured to determine dependent layers corresponding to the identified metadata elements. Further, the dependent layers may include, but not limited to, the Spatial layer, and temporal chromatic layer. Further, the spatial layer may refer to spatial arrangement of elements within an image or video. Further, the spatial layer may include, but not limited to, Object detection and localization, Depth information, and the like. Further, the temporal chromatic layer may refer to temporal (time-based) changes in color within an image or video. Further, the temporal chromatic layer may include, but not limited to, Motion analysis, Dominant color changes, Color segmentation, and the like. Further, the chromatic identifier module 216 may be configured to determine an order of a metadata layer in three-Dimensional (3D) space relative to the determined dependent layers based on the identified metadata elements and generating a three-dimensional link layer in three-dimensional (3D) space based on the determined order of the metadata relative to the determined dependent layers. Further, the chromatic identifier module 216 may be configured to selectively display the identified metadata elements based on the user’s persona to match an interest level and personalities of the end user. Further, the chromatic identifier module 216 may be configured to generate a user persona score for the end user based on the generated 3D link layer. Furthermore, the chromatic identifier module 216 may be configured to generate the user-specific persona code for the end user based on the generated user persona score. Further, the generated user code is assigned a color-code based on the user persona score and the generated user persona code is a hexadecimal color code Further, the hexadecimal based code may represent plurality of user characteristics such as, but not limited to, risk aversion, engagement level, personality traits, and the like.
Further, the chromatic identifier module 216 may be configured to determine, but not limited to, geo-spatial data, and spatial-temporal data in real-time for the received request based on a plurality of external data sources 112, and a pre-stored asset table. Further, the geo-spatial data may include, but not limited to, data related to amenities, accessibility, public transport, roads, nearby hospitals, nearby schools, total population within a predefined radius, terrain data, administrative boundaries of properties, point of interests, neighborhood data, utility data, transport networks, environment data, land use maps, asset data, and the like. Further, the spatial-temporal data may include, but not limited to, weather patterns throughout a year, traffic conditions at different times of a day, stock prices over a specific period, historical land-use data for a particular location, upcoming infrastructure data, upcoming rules and regulations for the particular location, and the like.
Further, the chromatic identifier module 216 may be configured to generate a spatial-temporal chromatic identifier for the end user based on at least one of the determined geo-spatial data and the spatial-temporal data. The spatial-temporal chromatic identifier may include at least one of insights attributes, spatial attributes, and foresights attributes corresponding to one or more desired assets. Further, the chromatic identifier module 216 may be configured to determine the insight attributes corresponding to the one or more desired assets based on the geo-spatial data and the spatial-temporal data. The insight attributes may include, but not limited to, a property data, a legality data, and locality data. Further, the assets data may include, but not limited to, price, area, orientation, developer, amenities, and the like. Further, the legality data may include, but not limited to, land records, ownership, litigations and legal restrictions, land holding capacity (i.e., maximum ceiling limit on area of land an individual can hold), Floor Space Index (FSI), width of the road and availability of the infrastructure (utilities), restriction on height for properties near airport, and number of lanes. The road width may be determined based on the classification (Major, Minor, Tertiary) and number of lanes information, which will be used while deriving the FSI.
In an example, the land records are digitized to assess the lineage (ownership) and reduce the risk of land disputes by analyzing various transactions. The AI model may be trained to understand the local context and the legal framework. For example, in Andhra Pradesh anyone is allowed to purchase agricultural land regardless of their profession. However, there is a maximum ceiling limit of land area. The family unit may hold or purchase a maximum of 10 Acres in Class A (irrigated land) and 54 Acres in Class K (includes dry and non-irrigated).
The land use/land category (LULC) information layer may be used along with approval authority to assess the conformance adherence. For example, the properties near airport will have height restrictions. Every city in India has its own norm for FSI (Floor Space Index), which depends on various factors like location, land use category, width of the road and availability of the infrastructure (utilities).
Further, the locality data may include various parameters of blue-green infrastructures such as green space, traffic, and transportation. Further, the parameters of blue-green infrastructure for residential property may include, but not limited to, land use coverage, public utilities such as water, gas pipe, road width, Air Quality Index (AQI) heat map, parks (green cover), seismic zone, soil map, and lineaments (ground water - borewell). Further, the parameters of blue-green infrastructure for farmland may include, but not limited to, soil map, land use (Agri Vs non-Agri ratio), water bodies, lineaments (ground water - borewell), green canopy (vegetation index - NDVI), Digital Elevation Map (DEM), and rain fall. Further, the parameters of blue-green infrastructure for commercial property may include, but not limited to, land use (commercial) coverage, connectivity through road/highway infrastructure, utilities such as power substations, and smart city infrastructure.
Further, the generation of the spatial-temporal chromatic identifier for the end user based on the geo-spatial data and the spatial-temporal data may include determining the spatial attributes corresponding to the one or more desired assets based on the geo-spatial data and the spatial-temporal data. The spatial attributes may include, but are not limited to, a locality data, a centrality data, and a utility data. Further, the centrality data may be obtained through a plurality of isochrone (based on Time) maps from multiple Points of Interests (POIs). Further, the centrality parameters for a residential property may include, but not limited to, transportation networks (airports, metros, bus, jetty), hospitals, malls/shopping complexes, restaurants, and upcoming infrastructures. Further, the centrality parameters for a farmland may include, but not limited to, Connectivity (nearby expressway), veterinary hospitals, nearby attractions, nearby Agricultural Produce Market Committees (APMCs), and upcoming infrastructure. Further, the centrality parameters for a commercial property may include, but not limited to, nearby industrial/logistics parks, connectivity (freight corridors), warehouses, and cold storages.
Further, the chromatic identifier module 216 may be configured to determine the foresight attributes corresponding to the one or more desired assets based on the geo-spatial data and the spatial-temporal data. The foresight attributes may include an investment potential data, a growth potential data, and a risk data.
Further, the chromatic identifier module 216 may be configured to generate the spatial-temporal chromatic identifier for the end user based on the determined insight attributes, the spatial attributes, and the foresight attributes.
Further, the Artificial Intelligence-based data orchestration module 218 may be configured to correlate the end user-specific persona code with the generated spatial-temporal chromatic identifier. Further, the Artificial Intelligence-based data orchestration module 218 may be configured to orchestrate data based on the spatial computation of the chromatic identifier module 216. Further, the Artificial Intelligence-based data orchestration module 218 may be configured to determine a golden ratio for the one or more assets based on the determined locality index. The golden ratio may define the chromatic value of the locality. Further, the chromatic value may refer to the purity or saturation of a color. A high chromatic value may indicate a vibrant, intense color, while a low value may indicate a dull or greyish color. Further, the golden ratio formula is derived by an Artificial-Intelligence based module as given in example equation (1) below, but not limited to:
Ratio= (?_(i=1)^(i=k)¦w_i a_i)/(?_i¦A),i=1,2,….,k (contextual areas);A=Total area,w=weightage………………equation (1)

Further, the Artificial-Intelligence based assets and pricing prediction module 220 may be configured to predict the one or more desired assets suitable for the end user based on the correlated user-specific persona code with the spatial-temporal chromatic identifier. Further, the Artificial-Intelligence based assets and pricing prediction module 220 may be configured to predict dynamic pricing values for each of the predicted one or more desired assets. Further, the Artificial-Intelligence based assets and pricing prediction module 220 may be configured to predict user preferred amenities, user preferred locations, user preferred neighborhood, point of interest, and user preferred pricing for the end user based on the correlated user-specific persona code with the spatial-temporal chromatic identifier.
Further, the data output module 222 may be configured to output the one or more desired assets and the predicted dynamic pricing values for each of the determined one or more desired assets on a user interface. Further, the one or more desired assets may include, but not limited to, schools, hospitals, restaurants, entertainment arenas, banking institutes, sports complexes, and the like. Further, the user interface may include, but not limited to, Graphical User Interface (GUI), Command Line Interface (CLI), Menu-Driven Interface, Touchscreen Interface, Virtual Reality (VR) and Augmented Reality (AR) Interfaces, and the like.
Further, in an embodiment of the present disclosure, the one or more hardware processors 202, as used herein, means any type of computational circuit, such as, but not limited to, a microprocessor unit, microcontroller, complex instruction set computing microprocessor unit, reduced instruction set computing microprocessor unit, very long instruction word microprocessor unit, explicitly parallel instruction computing microprocessor unit, graphics processing unit, digital signal processing unit, or any other type of processing circuit. Further, the one or more hardware processors 202 may also include embedded controllers, such as generic or programmable logic devices or arrays, application specific integrated circuits, single-chip computers, and the like.
Further, the memory 204 may be non-transitory volatile memory and non-volatile memory. The memory 204 may be coupled for communication with the one or more hardware processors 202, such as being a computer-readable storage medium. Further, the one or more hardware processors 202 may execute machine-readable instructions and/or source code stored in the memory 204. A variety of machine-readable instructions may be stored in and accessed from the memory 204. Further, the memory 204 may include any suitable elements for storing data and machine-readable instructions, such as read only memory, random access memory, erasable programmable read only memory, electrically erasable programmable read only memory, a hard drive, a removable media drive for handling compact disks, digital video disks, diskettes, magnetic tape cartridges, memory cards, and the like. In the present embodiment, the memory 204 includes the plurality of modules 110 stored in the form of machine-readable instructions on any of the above-mentioned storage media and may be in communication with and executed by the one or more hardware processors 202.
Further, the storage unit 206 may be a cloud storage. The storage unit 206 may store the website data, properties, rules, and the like.

EXAMPLE SCENARIO

This example scenario demonstrates an example system 100 for recommending real estate properties based on a user's emotional state and preferences. Further, in the example, user may search for properties on a platform, specifying filters such as, size, budget, and preferred location (quiet neighborhood). Further, in the example the system 100 may analyze user request and browsing history (interest level category). Further, the system 100 may access user’s social media profiles (emotional level category) to analyze users’ recently expressed excitement about buying a home. Further, the system 100 may predict user persona, potentially identifying user as eager (emotional level) and budget-conscious (personality level). Further, the system 100 may be configured to translate user persona into a "user-specific persona code" (a color code based on her emotional state and preferences). Further, the system 100 may be configured to external data sources such as, but not limited to, property listings and public records to identify suitable properties based on Sarah's filters and location preference. Further, the system 100 may be configured to generate a "spatial-temporal chromatic identifier" for each property. Further, the spatial-temporal chromatic identifier may include insight attributes such as, but not limited to, property details like price, legality, and locality data (including green spaces and amenities). Further, the spatial-temporal chromatic identifier may include spatial attributes such as, but not limited to, Centrality to points of interest like hospitals, transportation hubs and the like. Further, the spatial-temporal chromatic identifier may include foresight attributes such as, but not limited to, investment potential, growth potential, risk data, and the like.
Further, the system 100 may correlate user’s user-specific persona code with the spatial-temporal chromatic identifier of each property. Further, the system 100 may utilize an AI module to determine a "golden ratio" for each property based on a locality index (considering factors like green spaces and infrastructure). This ratio defines the "chromatic value" (suitability) of the location for user. Further, the system 100 may predict properties most suitable for user based on the correlation between user’s persona code and the properties' chromatic identifiers. Further, the system 100 may predict dynamic pricing values for each recommended property. Further, the system 100 may be configured to recommend properties in quieter neighborhoods with good access to amenities, potentially at slightly lower prices due to user’s budget-conscious persona. Further, the system 100 may display a list of recommended properties for user, highlighting features that align with user preferences (quiet neighborhood, amenities) and emotional state (eagerness to buy). Further, the system 100 may showcase dynamic pricing for each property, potentially offering user options within the budget of the user.

FIG. 3 illustrates a block diagram representation of an exemplary computing unit 104 for generating user-persona based geo-spatial insights and recommendations for assets, in accordance with another embodiment of the present disclosure. The plurality of modules 106 may include, but not limited to, a request receiver module 210, an intelligent data pipeline module 212, a user persona prediction module 214, a chromatic identifier module 216, an Artificial Intelligence based data orchestration module 218, an Artificial Intelligence based assets and pricing prediction module 220, and a data output module 222.
Further, the request receiver module 210 may be configured to receive one or more real estate preferences from one or more end users. Further, the references received from the one or more end users may be received by determining filters, semantic search attributes, user persona, and the like. Further, the semantic search attributes may include preferences such as, but not limited to, safe neighborhood, good investment potential, and the like.
Further, the intelligent data pipeline module 212 may be configured to generate data such as, but not limited to, accessibility, public transport, roads, nearby hospitals, nearby schools, total population within a predefined radius, terrain details, and the like. Further, the intelligent data pipeline module 212 may be configured to generate combinatorial data. The intelligent data pipeline module 212 is further configured to generate geospatial data based on various external geospatial data sources including government agencies. Further, the intelligent data pipeline module 212 may be configured to receive multilevel combinatorial data. The multilevel combinatorial data may include geospatial data and temporal data. Further, the geospatial data may include the information that describes objects, events, or other features with a property on the surface of the earth, and the like. Further, the temporal data may include data which changes over a period. Further, the temporal data may include examples such as, but not limited to, the addition of a new metro station in a particular locality, and the like. Further, the intelligent data pipeline module 212 may be configured to synchronize with the temporal data and incorporate necessary updates according to the temporal data. Further, the intelligent data pipeline module 212 may be configured to update a pricing of one or more desired assets in the particular locality.
Further, the chromatic identifier module 216 may be configured to determine a spatial-temporal chromatic identifier based on the received multilevel combinatorial data. The multilevel combinatorial data may be determined through received geospatial data and temporal data. The multilevel combinatorial data may include a geospatial data layer. Further, the geospatial data may include data such as, but not limited to, an administrative boundary of location, point of interest, neighborhood, utility and transport networks, environment, land use maps, and the like. Further, the geospatial data may include data such as, but not limited to, such as property address, area, amenities, and the like. Further, the chromatic identifier module 216 may be configured to generate a hexadecimal-based unique chromatic value including combination of 64-bit codes. The geospatial data associated with the property data may include user-specified parameters that enable determining of a color-based unique chromatic value to be associated with the property data. Further, a first 64-bit code may include first metadata related to the insights attribute. The insights attribute may include, but not limited to property data, legal data, neighborhood data, and the like. Further, the second 64-bit code may include a second metadata related to Spatial attributes. The spatial attributes may include, but are not limited to, locality data, centrality data, utility data, and the like. Further, the third 64-bit code may include third metadata related to foresights attributes. The foresights attributes may include, but not limited to, investment potential data, growth potential data, and risk data. The combined code is used to uniquely identify a property in spatial- temporal way.
For Example:

Layer-group ID Hex color code Layer-group ID Hex color code Layer-group ID Hex color code
The system and method of the present invention facilitates the enhancement of a search engine. The results from the search engine are curated/identified, based on a chromatic value of a lens set by the end user by the way of filters, the semantic search attributes, and the user persona. The filters and the semantic search attributes may include the safe neighborhood, the good investment potential, and the like.
Further, the user persona prediction module 214 may be configured to determine the order of the metadata layer in 3-Dimensional (3D) space and selectively display the metadata elements based on the user persona to match the interest level and personality of the user. Further, the user persona prediction module 214 may be configured to generate a color-based persona chromatic value. Further, the user persona prediction module 214 may be configured to determine techniques to capture human emotions and translate them into the color-based persona chromatic value in order to generate data such as, but not limited to, geospatial data, the temporal data, the metadata for the hyper-identification of properties, and the like. Further, the user persona prediction module 214 may be configured to identify the properties of the color based unique chromatic code associated with the one or more preferences. Further, the one or more preferences may be associated with one or more human emotions. The human emotions may include, but are not limited to, search parameters of necessary such as good neighborhood, good amenities, good connectivity, and the like. Further, the color-based persona chromatic value may be used for identification of assets. The identification may include assets whose color shades/value are in the closer range of human emotion color-coded shades/value.
Further, the chromatic identifier module 216 may be configured to determine the value of a property in a particular locality based on the geospatial data received. The spatial-temporal data may include, but not limited to, price, area, location, landmarks, upcoming infrastructure projects, and the like. Further, the chromatic identifier module 216 may be configured to determine the end user preferences such as, but not limited to, locality, budget, amenities, choice of property, point of interest, neighborhood preferences, and the like.
Further, the chromatic identifier module 216 may be configured to generate a color based unique chromatic value for one or more preferences received from the one or more end users. Further, the color based unique chromatic value may include plurality of preferences associated with multiple color combinations of the color code. Further, the spatial-temporal chromatic identifier may be higher in value for real estate properties in close proximity of necessities such as, but not limited to, schools, hospitals, restaurants, entertainment arenas, banking institutes, sports complexes, and the like. Further, the chromatic identifier module 216 may be configured to establish property prices, market guidance value, real estate recommendations, and the like. Further, the chromatic identifier module 216 may be configured to provide end users a self-aware property framework using hexadecimal, color based chromatic codes. Further, the chromatic identifier module 216 may be configured to enable more accurate assets and price prediction model.
Further, the Artificial-Intelligence based data orchestration module 218 may be configured to moderate and couple various data streams such as, but not limited to, human emotional vectors, spatial-temporal chromatic data layers, market/social media related data streams, context, and the like to compute chromatic data for various geo locations on the map. Further, Artificial-Intelligence based data orchestration module 218 may be configured to orchestrate data based on hexadecimal, color based unique chromatic values generated by the identifier module 216, the user persona prediction module 214 and the data received by the user persona prediction module 214. The user persona prediction module 214 may be configured to determine the context of the inputs received by the end user. Further, the user persona prediction module 214 may include information related to the role of the individual accessing the real estate recommendation computing system. Further, the user persona prediction module 214 may be configured to determine the end user to be, but not limited to, property owners, property buyers, property leasers, property lessee, property renters, a financier or lender, a builder, a real estate agent, a surveyor, a project manager, an architect, and the like. Furthermore, the user persona prediction module 214 may be configured to determine multiple roles. The multiple roles may include, but are not limited to, buyer, property developer, and property resident. Furthermore, the user persona prediction module 214 may be configured to distinguish property into residential property, agricultural property, industrial property, commercial property, and the like.
Further, the AI-based data orchestration module 218 may be configured to process data received from the request receiver module 210, the chromatic identifier module 216, and the user persona prediction module 214. Further, the AI-based data orchestration module 218 may be configured to provide data to the artificial-intelligence based assets and pricing prediction module 220. The artificial-intelligence based assets and pricing prediction module 220 may be configured to process the data cumulatively to generate auto-layer grouping and 3D rendering. For example, the end user is categorized as being risk averse the potential risks are auto layered. The auto-layer grouping includes grouping potential risks such as rainfall, contour map and flood data to generate suggestions of suitable properties with minimum risk in every aspect. Further, the chromatic identifier module 216 is configured to receive multilevel combinatorial data. The multilevel combinatorial data is identified through the geospatial data and temporal data. The geospatial data corresponds to the information that describes objects, events, or other features with a property on the surface of the earth. The temporal data corresponds to data, where changes over time or temporal aspects are of prime significance. For example, if there is a social feed on “New Metro station” in a new locality, then the chromatic identifier module 216 ascertains the veracity of information by syndicating information from various sources before making a decision to update the relevant deep insight metadata layer, thereby alerting the nearby properties to revise the property prices and dynamically updates their “point of interests”.
Additionally, the deep insight generator subsystem is configured to generate dynamic pricing of various real-estate properties thereby providing up-to-date information.
Further, the artificial-intelligence based assets and pricing prediction module 220 may be configured to determine the order of a metadata layer in 3-Dimensional (3D) space and selectively display metadata elements based on the user persona to match the interest level, personalities, and context of the end users based on the generated spatial-temporal chromatic identifier. For example, for the end user with risk averse persona, the metadata elements associated with risk factors may be placed at the topmost layers . Further, the real estate recommendation algorithm may determine dependent/associated layers with the particular metadata elements and group them into logical grouping to form a “3D Link Layer”. For example, the real estate recommendation algorithm may determine that the “flood data layer” is associated with “risk metadata” to form a “Link Layer” in 3D space above spatial features associated with the land region, at the metadata altitudes indicated for the respective layers.
Further, the data output module 222 may be configured to output the auto-layer grouped, 3D rendered, deep insight-based recommendations of real estate by the data generated by the artificial-intelligence based assets and pricing prediction module 220 on a graphical user interface corresponding to one or more user devices 102 of the end users.
FIG. 4 illustrates a process flow diagram representation of an exemplary method 400 for generating user-persona based geo-spatial insights and recommendations for assets, in accordance with an embodiment of the present disclosure. As illustrated in FIG. 4, the following steps may be implemented. At step 402, the method 400 includes receiving, by the processor 202, a request for determining one or more desired assets for an end user from a user device. The request includes one or more user preferences, and user personal information. At step 404, the method 400 includes identifying, by the processor 202, the type of the end user based on a user profile associated with the end user. The user profile is determined based on the received request. At step 406, the method 400 includes predicting, by the processor 202, the one or more human emotion parameters associated with the end user based on the received request and the identified type of the end user using an artificial intelligence-based user persona model. The one or more human emotion parameters corresponds to a user-persona data of the end user. At step 408, the method 400 includes generating, by the processor 202, a user-specific persona code for the end user based on the predicted one or more human emotion parameters. At step 410, the method 400 includes determining, by the processor 202, at least one of a geo-spatial data, and spatial-temporal data in real-time for the received request based on a plurality of external data sources, and a pre-stored asset table. At step 412, the method 400 includes generating, by the processor 202, a spatial-temporal chromatic identifier for the end user based on at least one of the determined geo-spatial data and the spatial-temporal data. At step 414, the method 400 includes correlating, by the processor 202, the user-specific persona code with the generated spatial-temporal chromatic identifier using an artificial intelligence-based data orchestration model. At step 416, the method 400 includes predicting, by the processor 202, the one or more desired assets suitable for the end user based on the correlated user-specific persona code with the spatial-temporal chromatic identifier. At step 418, the method 400 includes predicting, by the processor 202, dynamic pricing values for each of the predicted one or more desired assets using an artificial intelligence-based pricing model. Predicting property market price is very challenging and complex. The price prediction cannot be performed just by looking at the historical data. Techniques such as Long Short-Term Memory (LSTM) neural network and autoregressive integrated moving average (ARIMA) models are used to discover complex patterns and understand temporal (time-series) data while estimating the property price. It is required to establish correlations between the property price and other variabilities like infrastructure, upcoming projects, neighborhood analysis, urban development (change in land use and categories), government policies. At step 420, the method 400 includes outputting, by the processor 202, the one or more desired assets and the predicted dynamic pricing values for each of the determined one or more desired assets on a user interface.
Further, the method 400 may include one or more desired assets such as, but not limited to, real-estate properties including land, residential properties, commercial properties, and agricultural properties. Further, the method 400 may include one or more user preferences such as, but not limited to, filters, semantic search attributes, user persona, user likes, and historical data of the end user. The semantic search attributes may include, but not limited to, a safe neighborhood, and good investment potential. Further, the method 400 may include the geo-spatial data. The geo-spatial data may include data related to, but not limited to, amenities, accessibility, public transport, roads, nearby hospitals, nearby schools, total population within a predefined radius, terrain data, administrative boundaries of properties, point of interests, neighborhood data, utility data, transport networks, environment data, land use maps, and property data. Further, the method 400 may include the spatial temporal data. The spatial temporal data may include, but not limited to, weather patterns throughout a year, traffic conditions at different times of a day, stock prices over a specific period, historical land-use data for a particular location, upcoming infrastructure data, and upcoming rules and regulations for the particular location. The spatial-temporal chromatic identifier is mapped to discrete properties and descriptions to create a multilevel combinatorial definition of property value in terms of at least one of: the price, the area, the location, the nearby points of interests, the geospatial parameters, the upcoming infra projects, the landmarks, and the like.
Additionally, the spatial-temporal chromatic identifier is used to represent multi-level combinatorial persona definition and user preferences such as preferred search locality, budget, property amenities, choice of property, point of interest, neighborhood preferences, and the like. For Example, a color-based unique chromatic value is generated, wherein “a layer name” corresponds to “a point of interest,” “a value” corresponds to “a hospital,” and “a time stamp” corresponds to “a date.” In an exemplary embodiment, a hexadecimal value is generated based on the “point of interest,” “hospital,” and “date.” Similarly, the color-based persona chromatic value. is generated based on a multi-level combinatorial persona definition. Thereby aiding in the generation of the spatial-temporal chromatic identifier.
The spatial-temporal chromatic identifier enables real-estate properties to ascertain their unique advantages and become more self-aware. For example, the spatial-temporal chromatic identifier will be higher for real estate properties with good “locality” with respect to various points of interest such as schools, hospitals, neighborhood parameters, and the like. The spatial-temporal chromatic identifier will be further used in establishing the property prices and market guidance value. The Geospatial recommendation and insights-based computing system 100 comprises of real estate recommendation algorithm/module to provide user-specific real estate information. The property developers can leverage the real estate recommendation algorithm utilized by the present invention. The real estate recommendation algorithm/ module is based on a self-aware property framework using hexadecimal, color-based chromatic codes. Further, the real estate recommendation algorithm/module enables the building of a dynamic pricing engine and a more accurate price prediction model. In an exemplary embodiment, the real estate recommendation algorithm/module uses Deep Learning AI model (Graph Neural Networks algorithm) for establishing the relationship between persona code (representing a particular trait) to set of data layers. For example, when it detects a persona code highlighting “Risk Aversion” then the data orchestrator will recommend asset with low-risk exposure (authenticity of property titles/documents, low exposure to environment related risks like AQI - Air Quality Index, Flood, earthquake zone and the like).
Further, in identifying the type of the end user based on the user profile associated with the end user, the method 400 includes parsing, by the processor 202, the received request for identifying a context of the received request. Further, the method 400 includes identifying the user profile associated with the end user based on the identified context and the user personal information and identifying the type of the end user and one or more privileges associated with the end user based on the identified user profile.
Further, in predicting the one or more human emotion parameters associated with the end user based on the received request and the identified type of the end user, the method 400 includes evaluating, by the processor 202, the one or more user preferences specified in the received request by the end user based on user specific historical data, determining user behavioral data, and user emotional data associated with the end user at a real-time from a plurality of external data sources and a data stored on the user device, pre-profiling techniques , applications used by the end user, a browser history, a cache data, a watch data, a user purchase history, user social media profiles, and user activities across the applications.
Furthermore, in predicting the one or more human emotion parameters associated with the end user based on the received request and the identified type of the end user, the method 400 includes mapping the evaluated one or more user preferences specified in the received request by the end user with each of the determined the user behavioral data, the user emotional data, the data stored on the user device, the applications used by the end user, the browser history, the cache data, the watch data, the user purchase history, the user social media profiles, and the user activities across the applications using the artificial-intelligence based user persona model, and predicting the one or more human emotion parameters associated with the end user based on the mapping.
Further, in generating the user-specific persona code for the end user based on the predicted one or more human emotion parameters, the method 400 includes, but not limited to, classifying the end user into one of user persona category based on the predicted one or more human emotion parameters. The user persona category may include, but not limited to interest level category, an emotional level category, and a behavior level category. Further, in generating the user-specific persona code for the end user based on the predicted one or more human emotion parameters, the method 400 includes, but not limited to, identifying metadata elements forming a metadata layer for each of the one or more human emotion parameters based on classification category, determining dependent layers corresponding to the identified metadata elements, determining an order of a metadata layer in three-Dimensional (3D) space relative to the determined dependent layers based on the identified metadata elements, generating a three-dimensional link layer in three-dimensional (3D) space based on the determined order of the metadata relative to the determined dependent layers, selectively displaying the identified metadata elements based on the user’s persona to match an interest level and personalities of the end user, generating a user persona score for the end user based on the generated 3D link layer, and generating the user-specific persona code for the end user based on the generated user persona score. The generated user persona code may be assigned a color-code based on the user persona score. The generated user persona code is a hexadecimal based color code.
Further, in generating the spatial-temporal chromatic identifier for the end user based on the geo-spatial data and the spatial-temporal data, the method 400 includes, but not limited to, determining the insight attributes corresponding to the one or more desired assets based on the geo-spatial data and the spatial-temporal data. The insight attributes may include a property data, a legality data, locality data, and the like, determining the spatial attributes corresponding to the one or more desired assets based on the geo-spatial data and the spatial-temporal data. The spatial attributes may include a locality data, a centrality data, a utility data, and the like. Further, in generating the spatial-temporal chromatic identifier for the end user based on the geo-spatial data and the spatial-temporal data, the method 400 includes determining the foresight attributes corresponding to the one or more desired assets based on the geo-spatial data and the spatial-temporal data. The foresight attributes may include investment potential data, a growth potential data, a risk data, and the like. Further, in generating the spatial-temporal chromatic identifier for the end user based on the geo-spatial data and the spatial-temporal data, the method 400 includes generating the spatial-temporal chromatic identifier for the end user based on the determined insight attributes, the spatial attributes, and the foresight attributes.
Further, in the determining the legality data, the method 400 includes, but not limited to, determining ownership lineage of one or more assets by analyzing digitized land records of the one or more assets, identifying potential land disputes associated with the one or more assets based on the determined ownership lineage, a historical data of the one or more assets and a data derived from external data sources, training an artificial intelligence (AI) model (large language models LLMs – with deep learning) with identified potential land disputes and determined ownership lineage to analyze local legal frameworks and context of the one or more assets, evaluating a legal conformance of the one or more assets with the local legal frameworks and the context based on a land use category and a property location using the trained AI model, and computing a legality score for the one or more assets based on the evaluation, the identified potential land disputes and the determined ownership lineage.
Further, in determining the locality data, the method 400 includes, but not limited to, determining a locality index based on blue-green infrastructure parameters for a plurality of types of assets. The blue-green infrastructure parameters may include green space percentage, traffic density, transportation infrastructure, public utilities, a road width, an air quality index (AQI), a seismic zone classification, a soil map, groundwater availability, and the like. The plurality of types of assets may include a residential asset, an agricultural asset, a commercial asset, and the like. Further, in determination of the legality, the method 400 may include computing a golden ratio for the one or more assets based on the determined locality index using the artificial intelligence-based (supervised learning model) data orchestration model.
Further, in determining the centrality data, the method 400 includes, but not limited to, generating isochrone maps based on time from a plurality of points of interest (POIs). The plurality of points of interest are determined based on the type of the assets and computing a centrality score for the one or more assets based on the generated isochrone maps.
Further, the method 400 may include, but not limited to, determining, by the processor 202, Environmental, Social, and Governance (ESG) factors corresponding to the one or more assets, computing a ESG score for each of the ESG factors using the artificial intelligence-based data orchestration model, and generating the spatial-temporal chromatic identifier for the end user based on a legality score, a golden ratio, a centrality score and the computed ESG score. In an example embodiment, these scores are generated using ANN (Artificial Neural Network) which works on multiple layers that contain weighted combinations in terms of Blue-Green infrastructures, connectivity, land use/category, lineaments (ground water), public utilities, upcoming highway and infrastructures, walkability to important PoI (Point of Interests) and the like. The network “learns” by changing the weights of each combination before generating the unique code for spatial classifiers (“Legality,” “Locality,” “Centrality,” “Utility”). Further, the ESG score may be determined by calculating the chromatic identifier. The higher the chromatic identifier, the higher the ESG score. Further, the ESG score may be determined by better Air Quality Index (AQI), water (rain harvesting) and wastage (recycling units) infrastructure, number of electric charging stations, smart lighting, smart roads, Solar micro-grid, Transport network and last mile connectivity, Smart buildings, traffic congestion on roads, and the like.
Further, in predicting the one or more desired assets suitable for the end user based on the correlated user-specific persona code with the spatial-temporal chromatic identifier, the method 400 includes, but not limited to, predicting user preferred amenities, user preferred locations, user preferred neighborhood, point of interest, and user preferred pricing for the end user based on the correlated user-specific persona code with the spatial-temporal chromatic identifier. Further, the method 400 may include, predicting the one or more desired assets for the end user based on the predicted user preferred amenities, the user preferred locations, the user preferred neighborhood, the point of interest, and the user preferred pricing. Further, the method 400 may include validating, by the processor 202 the predicted one or more desired assets by determining potential risks associated with the one or more desired assets using an artificial intelligence-based risk model . In an example embodiment, the AI based risk model may go through the transaction pattern to ascertain any risks in the property lineage and title. The model studies the spatial contours and establish the risk on flooding. The potential risks may include, but are not limited to, title risk, legal risk, natural disasters such as flood and earthquake, and the like. Furthermore, Further, the method 400 may include computing a relevancy score for each of the predicted one or more desired assets based on results of validation The relevancy score is relative to the user-specific persona code and the spatial-temporal chromatic identifier. Further, the method 400 may include identifying at least one most suitable asset for the end user based on the highest relevancy score.
Further, in generating one or more recommendations for the end user based on the predicted one or more desired assets suitable for the end user, the method 400 includes predicting dynamic pricing value of the one or more desired assets. The one or more recommendations may include, but not limited to, ranked one or more desired assets, an explanation for each ranking, and user ratings.
Further, in generating one or more AI driven insights for the predicted one or more desired assets, the method 400 includes predicting dynamic pricing value, and generating an asset evaluation report for the received request based on the generated one or more AI drive insights. The asset evaluation report may include predictive maintenance requirements, market trend analysis, comparable property analysis, rental income potential, renovation Roi analysis, risk assessment, lifestyle compatibility analysis and the like.
In some examples, the predictive maintenance requirements may include leveraging historical data and sensor information (if available) to predict potential maintenance needs for the property, allowing for proactive budgeting and risk mitigation. Further, the market trend analysis may include identifying emerging trends in the local market that could impact the asset's value, such as upcoming infrastructure projects or changes in demographics. The comparable property analysis may include providing a comprehensive analysis of comparable properties in the vicinity, highlighting key features and recent sale prices to inform pricing strategies. The rental income potential may include estimating the potential rental income for the property based on market data and property characteristics, aiding in investment decisions. The renovation ROI Analysis may include analyzing the potential return on investment (ROI) for various renovation projects, enabling users to make data-driven decisions about property upgrades. The risk assessment may include identifying potential risks associated with the property, such as environmental hazards or flood zones, providing valuable information for informed decision-making. The lifestyle compatibility analysis may include using user-provided preferences and property data to assess the property's alignment with the user's desired lifestyle, promoting well-matched property selections.
In some example embodiments, the report may be customized based on user’s need and requirements. In some cases, the user may specify the parameters or components to be included in the report and may regenerate the report.
The method 400 may be implemented in any suitable hardware, software, firmware, or combination thereof.
FIG. 5 illustrates a schematic diagram representation of an exemplary system 500 for generating user-persona based geo-spatial insights and recommendations for assets, in accordance with another embodiment of the present disclosure. The system 500 may include an exemplary geospatial recommendation and insights-based computing system which may be configured to receive, data preferences from one or more end users. The data preferences may include, but are not limited to, requests for properties within ninety lacs with nearby schools and hospitals, and the like. The data receiving module 210 may be configured to receive this input data from the end user. The data receiving module 210 may include plurality of filters such as search filters that can be applied to the input data. Further, the search filter may include data such as properties near within . Further, the data received by the data receiving module 210 is further utilized by the AI based data orchestration module 218. The user persona prediction module 214, may include information related to the role of the individual assessing the geospatial recommendation and insights-based computing system 100. Further, the user persona prediction module 214, may include information on type of the end user such as, but not limited to, asset owners, asset buyers, asset leaser’s asset lessee, asset renters, a financier or lender, a builder, a real estate agent, a surveyor, a project manager, an architect, and the like. Further, the property may include, but not limited to, residential property, agricultural property, industrial property, commercial property, and the like. Further, the Artificial Intelligence based data orchestration module 218 may be configured to orchestrate the received input data based on the chromatic identifier module 216, and the user persona prediction module 214. Further, the intelligent data pipeline module 212 may be configured to generate data related to plurality of amenities such as, but not limited to, accessibility, public transport, roads, nearby hospitals, nearby schools, the total population within a predefined radius, terrain details, and the like. Further, the intelligent data pipeline module 212 may be configured to generate the multilevel combinatorial data based on the inputs received by one or more external data sources 112. Further, the one or more external data sources 112 may include government agencies, and the like. Further, the intelligent data pipeline module 212 may be configured to enable real-estate properties to ascertain their unique advantages and become more self-aware, by generating the geospatial data. Further, the geospatial data may include asset data such as, but not limited to, price, area, orientation, amenities, and the like. Further, the geospatial data associated with the asset data include user- specified parameters that enable determining of a color-based unique chromatic value (i.e., hexadecimal triplets) to be associated with the asset data. Further, the output of the chromatic identifier module 216 may be a hexadecimal-based unique chromatic value including three 64-bit codes. The first 64-bit code may include the first metadata related to the insights attribute. The insights attributes further include property data, legal data, and neighborhood data. The second 64-bit code includes a second metadata related to Spatial attributes. The spatial attribute may include, but not limited to, locality data, centrality data, and utility data. The third 64-bit code may include third metadata related to foresights attributes. The foresights attribute may include, but not limited to, investment potential data, growth potential data, and risk data. Further, the user persona prediction module 214 may be configured to utilize an Artificial Intelligence/ Machine Learning (AI/ML) based learning model to evaluate the user preferences/persona. The AI/ML model may be configured to train the user persona prediction module 214.
Further, the user persona prediction module 214 may be configured to evaluate the specific preferences of the user based on historical data. The specific user preferences may include user-specific safety parameters such as, but not limited to, safety from street dogs, burglars, chain snatchers, flooding, frequent power cut-off, frequency of water supply, and the like. Further, the user persona prediction module 214 may be configured to predict the user-specific amenities preferred by the user. Further, the user persona prediction module 214 may be configured to be trained by the AI/ML model. The chromatic identifier module 216 may be configured to generate a hexadecimal-based persona chromatic code based on the data derived from the user persona prediction module 214. Further, the persona chromatic code may be <#FFDEA9#B7AAF8> indicating that the user is . Furthermore, the AI-based data orchestration module 218 may be configured to process data received from the intelligent data pipeline module 212, the chromatic identifier module 216 and the user persona prediction module 214 and send the data to the Artificial Intelligence based assets and pricing prediction module 220. Further, Artificial Intelligence based assets and pricing prediction module 220 may be configured to process the data cumulatively to generate auto-layer grouping and 3D rendering. For example, the user is categorized as being risk averse and the potential risks are auto layered. The auto-layer grouping may include grouping potential risks such as, but not limited to, rainfall, contour map, flood data, and the like. Further, the data output module may be configured to illustrate the auto-layer grouped, 3D rendered, deep insight-based output data on a Graphical User Interface (GUI) of the one or more end users. Additionally, the 3D rendered output data may include a color code to further illustrate the user preferences in an elaborate manner.
In one example scenario, users interact with the system 500 through a data receiving module 210 to specify their property preferences (location, price range, and the like.). The user persona prediction module 214 analyzes this information and potentially additional user data (not shown) to predict the user's persona. This persona considers factors such as the user's role (buyer, agent) and property type (residential, commercial). An AI/ML model may be used in this prediction process. The chromatic identifier module 216 generates a unique code based on the user's persona. This code (e.g., #FFDEA9#B7AAF8) might represent a combination of color values reflecting the user's risk tolerance and preferences (risk-averse and impulsive in this example). The AI-based data orchestration module 218 combines the following data sets user preferences from the data receiving module 210, user persona data the user persona prediction module 214, geospatial data with categorized attributes and property chromatic code from the chromatic identifier module 216. The AI-based assets and pricing prediction module 220 analyzes the combined data to generate insights and recommendations tailored to the user's persona. This may involve Auto-layering potential risks (e.g., flood data) based on the user's risk profile, highlighting properties with features that align with the user's preferences (e.g., schools nearby for families), and identifying properties with strong investment potential based on foresight attributes.
Further, the AI-based assets and pricing prediction module 220 accesses a geospatial database 108 containing information about properties within the user's specified area. This data may include property details (price, area, amenities) and geospatial coordinates.

The data output module 222 presents the results to the user through a graphical user interface (GUI). This may include a 3D rendering of the property data with color coding that reflects the user's preferences (derived from the property chromatic code), overlays highlighting potential risks or areas of interest based on the auto-layered data, and interactive visualizations and reports summarizing the insights and recommendations.
FIG. 6A-B are snapshot representations of an example graphical user interface screens for generating user-persona based geo-spatial insights and recommendations for assets, in accordance with another embodiment of the present disclosure. FIG. 6A depicts chromatic identifier cluster results display. This shows property clusters for the computed chromatic code. The results are generated based on utility, locality, and centrality sections. FIG. 6B depicts a chromatic contextual heatmap based on user profile and selection. This shows property results for computed chromatic code,

The present invention relates to a system and method to provide chromatic lens framework-based hyper-identifiable, deep insights pertaining to user context with three-dimensional (3D) visualization of real estate information. A chromatic lens framework-based computing system is configured to compute spatial-temporal data for the hyper-discovery/identification and recommendation of at least one of: real estate properties, location, geospatial insights, and the like based on a persona. The persona identification involves marketing and user experience (UX) design to create realistic representations of target users/customers. The user persona is a fictional character that represents a particular segment of users and is created through research, data analysis, and user testing. The chromatic lens framework-based computing system further comprises a plurality of subsystems thereby facilitating real estate recommendations to an end user.
The present invention relates to an advanced system and method for computing spatial-temporal data to provide, chromatic lens framework- based hyper-identifiable, user-specific real estate information with 3D (three dimensional) visualization of deep insight.
The present invention curates search results based on the end user's requirements. The user requirements include taking into account the end users’ emotions and other relevant factors, thereby simplifying the process of searching and purchasing the best real estate properties. Additionally, the present invention ensures that the recommendations are tailored to the needs of the individual user. Moreover, the present invention offers an effective and streamlined approach to real estate property search, with a focus on meeting the needs of the clients or the users.
The written description describes the subject matter herein to enable any person skilled in the art to make and use the embodiments. The scope of the subject matter embodiments is defined by the claims and may include other modifications that occur to those skilled in the art. Such other modifications are intended to be within the scope of the claims if they have similar elements that do not differ from the literal language of the claims or if they include equivalent elements with insubstantial differences from the literal language of the claims.
The embodiments herein can include hardware and software elements. The embodiments that are implemented in software include but are not limited to, firmware, resident software, microcode, and the like. The functions performed by various modules described herein may be implemented in other modules or combinations of other modules. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that can include, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Examples of a computer-readable medium include a semiconductor or solid-state memory, magnetic tape, a removable computer diskette, a random-access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Current examples of optical disks include compact disk-read only memory (CD-ROM), compact disk-read/write (CD-R/W) and DVD.
Input/output (I/O) devices (including but not limited to keyboards, displays, pointing devices, and the like.) can be coupled to the system either directly or through intervening I/O controllers. Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters.
A representative hardware environment for practicing the embodiments may include a hardware configuration of an information handling/computer system in accordance with the embodiments herein. The system herein includes at least one processor or central processing unit (CPU). The CPUs are interconnected via system bus 208 to various devices such as a random-access memory (RAM), read-only memory (ROM), and an input/output (I/O) adapter. The I/O adapter can connect to peripheral devices, such as disk units and tape drives, or other program storage devices that are readable by the system. The system can read the inventive instructions on the program storage devices and follow these instructions to execute the methodology of the embodiments herein.
The system further includes a user interface adapter that connects a keyboard, mouse, speaker, microphone, and/or other user interface-devices such as a touch screen device (not shown) to the bus to gather user input. Additionally, a communication adapter connects the bus to a data processing network, and a display adapter connects the bus to a display device which may be embodied as an output device such as a monitor, printer, or transmitter, for example.
A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary, a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention. When a single device or article is described herein, it will be apparent that more than one device/article (whether or not they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it will be apparent that a single device/article may be used in place of the more than one device or article, or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself.
The illustrated steps are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, and the like., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments. Also, the words "comprising," "having," "containing," and "including," and other similar forms are intended to be equivalent in meaning and be open-ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items. It must also be noted that as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural references unless the context clearly dictates otherwise.
Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the embodiments of the present invention are intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

,CLAIMS:WE CLAIM:

1. A system for generating user-persona based geo-spatial insights and recommendations for assets, the system comprising:
a processor;
a memory coupled to the processor, wherein the memory comprises processor-executable instructions, which on execution, cause the processor to:
receive a request for determining one or more desired assets for an end user from a user device, wherein the request comprises one or more user preferences, and user personal information;
identify the type of the end user based on a user profile associated with the end user, wherein the user profile is determined based on the received request;
predict one or more human emotion parameters associated with the end user based on the received request and the identified type of the end user using an artificial-intelligence based user persona model, wherein the one or more human emotion parameters corresponds to a user-persona data of the end user;
generate a user-specific persona code for the end user based on the predicted one or more human emotion parameters;
determine at least one of a geo-spatial data, and spatial-temporal data in real-time for the received request based on a plurality of external data sources, and a pre-stored asset table;
generate a spatial-temporal chromatic identifier for the end user based on at least one of the determined geo-spatial data and the spatial-temporal data, wherein the spatial-temporal chromatic identifier comprises at least one of insights attribute, spatial attributes, and foresights attributes corresponding to one or more desired assets;
correlate the user-specific persona code with the generated spatial-temporal chromatic identifier using an artificial intelligence-based data orchestration model;
predict the one or more desired assets suitable for the end user based on the correlated user-specific persona code with the spatial-temporal chromatic identifier;
predict dynamic pricing values for each of the predicted one or more desired assets using an artificial intelligence-based pricing model; and
output the one or more desired assets and the predicted dynamic pricing values for each of the determined one or more desired assets on a user interface of a user interface.
2. The system as claimed in claim 1, wherein the one or more desired assets comprise real-estate properties comprising land, residential properties, commercial properties, and agricultural properties.
3. The system as claimed in claim 1, wherein the one or more user preferences comprise filters, semantic search attributes, user persona, user likes, and historical data of the end user, and wherein the semantic search attributes comprise at least one of: a safe neighborhood, and good investment potential.
4. The system as claimed in claim 1, wherein the geo-spatial data comprises data related to amenities, accessibility, public transport, roads, nearby hospitals, nearby schools, total population within a predefined radius, terrain data, administrative boundaries of properties, point of interests, neighborhood data, utility data, transport networks, environment data, land use maps, and property data.
5. The system as claimed in claim 1, wherein the spatial temporal data comprises weather patterns throughout a year, traffic conditions at different times of a day, stock prices over a specific period, historical land-use data for a particular location, upcoming infrastructure data, and upcoming rules and regulations for the particular location.
6. The system as claimed in claim 1, wherein in identifying the type of the end user based on the user profile associated with the end user, the processor is configured to:
parse the received request for identifying a context of the received request;
identify the user profile associated with the end user based on the identified context and the user personal information; and
identify the type of the end user and one or more privileges associated with the end user based on the identified user profile.
7. The system as claimed in claim 1, wherein in predicting the one or more human emotion parameters associated with the end user based on the received request and the identified type of the end user, the processor is configured to:
evaluate the one or more user preferences specified in the received request by the end user based on user specific historical data;
determine user behavioral data, and user emotional data associated with the end user at a real-time from a plurality of external data sources and a data stored on the user device, pre-profiling techniques, applications used by the end user, a browser history, a cache data, a watch data, a user purchase history, user social media profiles, and user activities across the applications;
map the evaluated one or more user preferences specified in the received request by the end user with each of the determined the user behavioral data, the user emotional data, the data stored on the user device, the applications used by the end user, the browser history, the cache data, the watch data, the user purchase history, the user social media profiles, and the user activities across the applications using the artificial-intelligence based user persona model; and
predict the one or more human emotion parameters associated with the end user based on the mapping, wherein the one or more human emotion parameters comprises.
8. The system as claimed in claim 1, wherein in generating the user-specific persona code for the end user based on the predicted one or more human emotion parameters, the processor is configured to:
classify the end user into one of user persona category based on the predicted one or more human emotion parameters, wherein the user persona category comprises one of an interest level category, a personality level category, a user temper tendency category, an emotional level category, and a behavior level category;
identify metadata elements forming a metadata layer for each of the one or more human emotion parameters based on classification category;
determine dependent layers corresponding to the identified metadata elements;
determine an order of a metadata layer in three-Dimensional (3D) space relative to the determined dependent layers based on the identified metadata elements;
generate a three-dimensional link layer in three-dimensional (3D) space based on the determined order of the metadata relative to the determined dependent layers;
selectively display the identified metadata elements based on the user’s persona to match an interest level and personalities of the end user;
generate a user persona score for the end user based on the generated 3D link layer; and
generate the user-specific persona code for the end user based on the generated user persona score, wherein the generated user persona code is assigned a color-code based on the user persona score and wherein the generated user persona code is a hexadecimal based color code.
9. The system as claimed in claim 1, wherein in generating the spatial-temporal chromatic identifier for the end user based on the geo-spatial data and the spatial-temporal data the processor is configured to:
determine the insight attributes corresponding to the one or more desired assets based on the geo-spatial data and the spatial-temporal data, wherein the insight attributes comprise a property data, a legality data, and locality data;
determine the spatial attributes corresponding to the one or more desired assets based on the geo-spatial data and the spatial-temporal data, wherein the spatial attributes comprise a locality data, a centrality data, and a utility data;
determine the foresight attributes corresponding to the one or more desired assets based on the geo-spatial data and the spatial-temporal data, wherein the foresight attributes comprise an investment potential data, a growth potential data, and a risk data; and
generate the spatial-temporal chromatic identifier for the end user based on the determined insight attributes, the spatial attributes, and the foresight attributes.
10. The system as claimed in claim 9, wherein to determine the legality data the processor is configured to:
determine ownership lineage of one or more assets by analyzing digitized land records of the one or more assets;
identify, potential land disputes associated with the one or more assets based on the determined ownership lineage, a historical data of the one or more assets and a data derived from external data sources;
train an artificial intelligence (AI) model with identified potential land disputes and determined ownership lineage to analyze local legal frameworks and context of the one or more assets;
evaluate a legal conformance of the one or more assets with the local legal frameworks and the context based on a land use category and a property location using the trained AI model; and
compute a legality score for the one or more assets based on the evaluation, the identified potential land disputes, and the determined ownership lineage.
11. The system as claimed in claim 9, wherein to determine the locality data, the processor is configured to:
determine a locality index based on blue-green infrastructure parameters for a plurality of types of assets, wherein the blue-green infrastructure parameters comprise green space percentage, traffic density, transportation infrastructure, public utilities, a road width, an air quality index (AQI), a seismic zone classification, a soil map, and groundwater availability and wherein the plurality of types of assets comprise a residential asset, an agricultural asset, and a commercial asset; and
compute a golden ratio for the one or more assets based on the determined locality index using the artificial intelligence-based data orchestration model.
12. The system as claimed in claim 9, wherein to determine the centrality data, the processor is configured to:
generate isochrone maps based on time from a plurality of points of interest (PoIs), wherein the plurality of points of interest are determined based on the type of the assets; and
compute a centrality score for the one or more assets based on the generated isochrone maps.
13. The system as claimed in claim 9, wherein the processor is configured to:
determine Environmental, Social, and Governance (ESG) factors corresponding to the one or more assets;
compute a ESG score for each of the ESG factors using the artificial intelligence-based data orchestration model; and
generate the spatial-temporal chromatic identifier for the end user based on a legality score, a golden ratio, a centrality score and the computed ESG score.
14. The system as claimed in claim 1, wherein in predicting the one or more desired assets suitable for the end user based on the correlated user-specific persona code with the spatial-temporal chromatic identifier, the processor is configured to:
predict user preferred amenities, user preferred locations, user preferred neighborhood, point of interest, and user preferred pricing for the end user based on the correlated user-specific persona code with the spatial-temporal chromatic identifier;
predict the one or more desired assets for the end user based on the predicted user preferred amenities, the user preferred locations, the user preferred neighborhood, the point of interest, and the user preferred pricing;
validate the predicted one or more desired assets by determining potential risks associated with the one or more desired assets using an artificial intelligence-based risk model, wherein the potential risks comprise title risk, legal risk, natural disasters like flood and earthquake;
compute a relevancy score for each of the predicted one or more desired assets based on results of validation, wherein the relevancy score is relative to the user-specific persona code and the spatial-temporal chromatic identifier; and
identify at least one best suitable asset for the end user based on the highest relevancy score.
15. The system as claimed in claim 1, the processor is configured to:
generate one or more recommendations for the end user based on the predicted one or more desired assets suitable for the end user and the predicted dynamic pricing value of the one or more desired assets, wherein the one or more recommendations comprise ranked one or more desired assets, an explanation for each ranking, and user ratings.
16. The system as claimed in claim 1, the processor is configured to:
generate one or more AI driven insights for the predicted one or more desired assets and the predicted dynamic pricing value; and
generate an asset evaluation report for the received request based on the generated one or more AI drive insights.

17. A method for generating user-persona based geo-spatial insights and recommendations for assets, the method comprising:
receiving, by a processor, a request for determining one or more desired assets for an end user from a user device, wherein the request comprises one or more user preferences, and user personal information;
identifying, by the processor, the type of the end user based on a user profile associated with the end user, wherein the user profile is determined based on the received request;
predicting, by the processor, one or more human emotion parameters associated with the end user based on the received request and the identified type of the end user using an artificial-intelligence based user persona model, wherein the one or more human emotion parameters corresponds to a user-persona data of the end user;
generating, by the processor, a user-specific persona code for the end user based on the predicted one or more human emotion parameters;
determining, by the processor, at least one of a geo-spatial data, and spatial-temporal data in real-time for the received request based on a plurality of external data sources, and a pre-stored asset table;
generating, by the processor, a spatial-temporal chromatic identifier for the end user based on at least one of the determined geo-spatial data and the spatial-temporal data, wherein the spatial-temporal chromatic identifier comprises at least one of insights attribute, spatial attributes, and foresights attributes corresponding to one or more desired assets;
correlating, by the processor, the user-specific persona code with the generated spatial-temporal chromatic identifier using an artificial intelligence-based data orchestration model;
predicting, by the processor, the one or more desired assets suitable for the end user based on the correlated user-specific persona code with the spatial-temporal chromatic identifier;
predicting, by the processor, dynamic pricing values for each of the predicted one or more desired assets using an artificial intelligence-based pricing model; and
outputting, by the processor, the one or more desired assets and the predicted dynamic pricing values for each of the determined one or more desired assets on a user interface of a user interface.
18. The method as claimed in claim 17, wherein the one or more desired assets comprise real-estate properties comprising land, residential properties, commercial properties, and agricultural properties.
19. The method as claimed in claim 17, wherein the one or more user preferences comprise filters, semantic search attributes, user persona, user likes, and historical data of the end user, and wherein the semantic search attributes comprise at least one of: a safe neighborhood, and good investment potential.
20. The method as claimed in claim 1, wherein the geo-spatial data comprises data related to amenities, accessibility, public transport, roads, nearby hospitals, nearby schools, total population within a predefined radius, terrain data, administrative boundaries of properties, point of interests, neighborhood data, utility data, transport networks, environment data, land use maps, and property data.

21. The method as claimed in claim 17, wherein the spatial temporal data comprises weather patterns throughout a year, traffic conditions at different times of a day, stock prices over a specific period, historical land-use data for a particular location, upcoming infrastructure data, and upcoming rules and regulations for the particular location.
22. The method as claimed in claim 17, wherein identifying the type of the end user based on the user profile associated with the end user comprises:
parsing, by the processor, the received request for identifying a context of the received request;
identifying, by the processor, the user profile associated with the end user based on the identified context and the user personal information; and
identifying, by the processor, the type of the end user and one or more privileges associated with the end user based on the identified user profile.
23. The method as claimed in claim 17, wherein predicting the one or more human emotion parameters associated with the end user based on the received request and the identified type of the end user comprises:
evaluating, by the processor, the one or more user preferences specified in the received request by the end user based on user specific historical data;
determining, by the processor, user behavioral data, and user emotional data associated with the end user at a real-time from a plurality of external data sources and a data stored on the user device, pre-profiling techniques , applications used by the end user, a browser history, a cache data, a watch data, a user purchase history, user social media profiles, and user activities across the applications;
mapping, by the processor, the evaluated one or more user preferences specified in the received request by the end user with each of the determined the user behavioral data, the user emotional data, the data stored on the user device, the applications used by the end user, the browser history, the cache data, the watch data, the user purchase history, the user social media profiles, and the user activities across the applications using the artificial-intelligence based user persona model; and
predicting, by the processor, the one or more human emotion parameters associated with the end user based on the mapping, wherein the one or more human emotion parameters comprises.
24. The method as claimed in claim 17, wherein generating the user-specific persona code for the end user based on the predicted one or more human emotion parameters comprises:
classifying, by the processor, the end user into one of user persona category based on the predicted one or more human emotion parameters, wherein the user persona category comprises one of an interest level category, a personality level category, a user temper tendency category, an emotional level category, and a behavior level category;
identifying, by the processor, metadata elements forming a metadata layer for each of the one or more human emotion parameters based on classification category;
determining, by the processor, dependent layers corresponding to the identified metadata elements;
determining, by the processor, an order of a metadata layer in three-Dimensional (3D) space relative to the determined dependent layers based on the identified metadata elements;
generating, by the processor, a three-dimensional link layer in three-dimensional (3D) space based on the determined order of the metadata relative to the determined dependent layers; selectively displaying, by the processor, the identified metadata elements based on the user’s persona to match an interest level and personalities of the end user;
generating, by the processor, a user persona score for the end user based on the generated 3D link layer; and
generating, by the processor, the user-specific persona code for the end user based on the generated user persona score, wherein the generated user persona code is assigned a color-code based on the user persona score and wherein the generated user persona code is a hexadecimal based color code.

25. The method as claimed in claim 17, wherein generating the spatial-temporal chromatic identifier for the end user based on the geo-spatial data and the spatial-temporal data comprises:
determining, by the processor, the insight attributes corresponding to the one or more desired assets based on the geo-spatial data and the spatial-temporal data, wherein the insight attributes comprise a property data, a legality data, and locality data;
determining, by the processor, the spatial attributes corresponding to the one or more desired assets based on the geo-spatial data and the spatial-temporal data, wherein the spatial attributes comprise a locality data, a centrality data, and a utility data;
determining, by the processor, the foresight attributes corresponding to the one or more desired assets based on the geo-spatial data and the spatial-temporal data, wherein the foresight attributes comprise an investment potential data, a growth potential data, and a risk data; and
generating, by the processor, the spatial-temporal chromatic identifier for the end user based on the determined insight attributes, the spatial attributes, and the foresight attributes.
26. The method as claimed in claim 25, wherein the legality data is determined by:
determining, by the processor, ownership lineage of one or more assets by analyzing digitized land records of the one or more assets;
identifying, by the processor, potential land disputes associated with the one or more assets based on the determined ownership lineage, a historical data of the one or more assets and a data derived from external data sources;
training, by the processor, an artificial intelligence (AI) model (with identified potential land disputes and determined ownership lineage to analyze local legal frameworks and context of the one or more assets;
evaluating, by the processor, a legal conformance of the one or more assets with the local legal frameworks and the context based on a land use category and a property location using the trained AI model; and
computing, by the processor, a legality score for the one or more assets based on the evaluation, the identified potential land disputes, and the determined ownership lineage.
27. The method as claimed in claim 25, wherein the locality data is determined by:
determining, by the processor, a locality index based on blue-green infrastructure parameters for a plurality of types of assets, wherein the blue-green infrastructure parameters comprise green space percentage, traffic density, transportation infrastructure, public utilities, a road width, an air quality index (AQI), a seismic zone classification, a soil map, and groundwater availability and wherein the plurality of types of assets comprise a residential asset, an agricultural asset, and a commercial asset; and
computing, by the processor, a golden ratio for the one or more assets based on the determined locality index using the artificial intelligence-based (supervised learning model) data orchestration model.
28. The method as claimed in claim 25, wherein the centrality data is determined by:
generating, by the processor, isochrone maps based on time from a plurality of points of interest (PoIs), wherein the plurality of points of interest are determined based on the type of the assets; and
computing, by the processor, a centrality score for the one or more assets based on the generated isochrone maps.
29. The method as claimed in claim 25, further comprising:
determining, by the processor, Environmental, Social, and Governance (ESG) factors corresponding to the one or more assets;
computing, by the processor, a ESG score for each of the ESG factors using the artificial intelligence-based data orchestration model; and
generating, by the processor, the spatial-temporal chromatic identifier for the end user based on a legality score, a golden ratio, a centrality score and the computed ESG score.
30. The method as claimed in claim 17, wherein predicting the one or more desired assets suitable for the end user based on the correlated user-specific persona code with the spatial-temporal chromatic identifier comprises:
predicting, by the processor, user preferred amenities, user preferred locations, user preferred neighborhood, point of interest, and user preferred pricing for the end user based on the correlated user-specific persona code with the spatial-temporal chromatic identifier;
predicting, by the processor, the one or more desired assets for the end user based on the predicted user preferred amenities, the user preferred locations, the user preferred neighborhood, the point of interest, and the user preferred pricing;
validating, by the processor, the predicted one or more desired assets by determining potential risks associated with the one or more desired assets using an artificial intelligence-based risk model;
computing, by the processor, a relevancy score for each of the predicted one or more desired assets based on results of validation, wherein the relevancy score is relative to the user-specific persona code and the spatial-temporal chromatic identifier; and
identifying, by the processor, at least one best suitable asset for the end user based on a highest relevancy score.
31. The method as claimed in claim 17, further comprising:
generating, by the processor, one or more recommendations for the end user based on the predicted one or more desired assets suitable for the end user and the predicted dynamic pricing value of the one or more desired assets, wherein the one or more recommendations comprise ranked one or more desired assets, an explanation for each ranking, and user ratings.
32. The method as claimed in claim 17, further comprising:
generating, by the processor, one or more AI driven insights for the predicted one or more desired assets and the predicted dynamic pricing value; and
generating, by the processor, an asset evaluation report for the received request based on the generated one or more AI drive insights.

Dated this 24th day of July 2024

Sanath MV
Patent Agent (IN/PA-5004)
Agent for the Applicant