Claims:1. A system (100) for providing predictive information through interaction to a requesting entity (112), the system (100) comprising:
a computing device (102), configured to receive inputs from the requesting entity (112), and correspondingly generate a first set of data packets, wherein the requesting entity (112) is enabled to belong to any of the predetermined categories including a first (112A), second (112B) and third entity (112C);
a processing unit (104), operatively coupled with the computing device (102), wherein the processing unit (104) includes one or more processors associated with a memory, the memory storing instructions executable by the one or more processors to:
extract, from the first set of data packets, a second set of data packets pertaining to a first set of attributes of a query placed by the requesting entity (112);
determine, based on the first set of attributes, category of the requesting entity (112);
determine, from the second set of data packets, geographical location of the requesting entity (112);
receive, from a database operatively coupled to the one or more processors of the processing unit (104), a second set of attributes pertaining to the geographical location of the requesting entity (112), wherein the second set of attributes correspond to a third set of data packets;
identify, one or more first (112A), second (112B) and third (112C) entities located within a pre-determined range from the determined geographical location of the requesting entity (112), wherein the one or more first, second and third entities are associated with one or more registered user devices (110), wherein the one or more registered user devices are communicatively coupled to the computing device (102) through a secured communication network (106);
determine, a pattern of a third set of attributes pertaining to interactions between the computing device (102) and the identified one or more first (112A), second (112B) and third (112C) entities and correspondingly generate a fourth set of data packets, wherein said interactions correspond to one or more predetermined first time intervals preceding the time of reception of the query and including the current time, wherein previously determined third set of attributes pertaining to said time frame are received from the database;
analyze, variations in the third set of attributes pertaining to interactions between the computing device (102) and the identified one or more first (112A), second (112B), and third (112C) entities based on the fourth set of data packets;
predict, based on the analysis related to the fourth set of data packets and the third set of data packets, a fourth set of attributes pertaining to the query and correspondingly generate, a fifth set of data packets, wherein the predicted fourth set of attributes pertain to one or more predetermined second time interval following the time of reception of the query;
determine, relative degree of influence of the third, fourth and fifth set of data packets pertaining to the query and generate a sixth set of data packets corresponding to a response to the query, wherein the sixth set of data packets pertain to a weighted combination of the third, fourth and fifth set of data packets;
transmit, the sixth set of data packets to the registered user device (110) associated with the requesting entity (112) using the communication network (106);
update, information stored in the database with the third, fourth, fifth and sixth set of data packets.
2. The system (100) as claimed in claim 1, wherein the requesting entity (112) is required to provide authentication details to the computing device (102) for accessing the system (100), wherein the authentication details comprise any or a combination of first name, last name, contact number, email id, and an authentication key code.
3. The system (100) as claimed in claim 1, wherein the requesting entity (112) is facilitated to enroll with the system (100) in one or more categories corresponding to the authentication details, wherein enrollment includes a first time registration and subsequent sign-in events for accessing the system (100).
4. The system (100) as claimed in claim 1, wherein the computing device (102) is enabled to provide customized response to the query placed by the requesting entity (112), wherein the response is determined based on relative influence of any or a combination of factors including personalized information, location of the requesting entity, current and historical patterns of interactions between the system and multiple entities around the location, social factors, upcoming events, climatic changes and forecast of outcomes pertaining to the query.
5. The system (100) as claimed in claim 1, wherein the computing device (102) is enabled to broadcast information to the registered user devices (110) associated with the first (112A), second (112B) and third (112C) entities, wherein the information pertains to any or a combination of predicted changes in the second set of attributes pertaining to said geographical location, predicted changes in patterns corresponding to the third set of attributes of the interactions in said location, and predicted changes in the fourth set of attributes pertaining to a set of frequently placed queries.
6. The system (100) as claimed in claim 1, wherein the processing unit (104) is facilitated to generate a set of alert signals pertaining to previous interactions between the requesting entity (112) and the computing device (102), wherein the alert signals are enabled to be transmitted to the registered user device (110) associated with the requesting entity (112) through any or a combination of electronic mails, messages and automated calls.
7. The system (100) as claimed in claim 1, wherein the processing unit (104) is facilitated to periodically update information related to the second set of attributes pertaining to one or more geographical locations associated with the registered entities (112), wherein the updated information is used to reconfigure previous responses to one or more queries and wherein the reconfigured responses are transmitted to the corresponding requesting entities (112).
8. The system (100) as claimed in claim 1, wherein each of the first (112A), second (112B) and third (112C) entities, communicatively coupled to the computing device (102) are associated with a set of features, wherein information shared by each of the entities with the system (100) is facilitated to be accessed by the requesting entity (112), based on the set of features.
9. A method (400) for providing predictive information through interaction to a requesting entity (112), the method comprising:
receiving, through a computing device (102), inputs from a requesting entity (112), and correspondingly generating a first set of data packets;
extracting, at a processing unit (104), operatively coupled to the computing device (102), a second set of data packets, pertaining to a first set of attributes of a query placed by the requesting entity (112)S, from the first set of data packets;
determining, at the processing unit (104), based on the first set of attributes, category of the requesting entity (112);
determining, at the processing unit (104), geographical location of the requesting entity (112), from the second set of data packets;
identifying, at the processing unit (104), one or more first (112A), second (112B) and third (112C) entities located within a pre-determined range from the determined geographical location of the requesting entity (112);
receiving, at the processing unit (104), a third set of data packets corresponding to a second set of attributes related to the geographical location of the requesting entity (112), from a database operatively coupled to the one or more processors of the processing unit (104);
determining, at the processing unit (104), a pattern of a third set of attributes pertaining to interactions between the computing device (102) and the identified one or more first (112A), second (112B) and third (112C) entities and correspondingly generating a fourth set of data packets, the interactions being related to one or more predetermined first time intervals;
analyzing, at the processing unit (104), variations in the third set of attributes based on the determined fourth set of data packets;
predicting, at the processing unit (104), a fourth set of attributes pertaining to the query and correspondingly generating a fifth set of data packets, the predicted fourth set of attributes being related to one or more predetermined second time intervals;
determining, at the processing unit (104), relative degree of influence of the third, fourth and fifth set of data packets pertaining to the query and generating a sixth set of data packets corresponding to a response to the query, the sixth set of data packets being a weighted combination of the third, fourth and fifth set of data packets;
transmitting, by the processing unit (104), the sixth set of data packets to the registered user device (110), associated with the requesting entity (112), using the communication network (106);
updating, at the processing unit (104), information stored in the database with the third, fourth, fifth and sixth set of data packets.
, Description:TECHNICAL FIELD
[0001] The present disclosure relates to the field of query based information facility. In particular, the present disclosure provides a system and method for providing predictive information, determined through interaction between the system and multiple entities, in response to a query.

BACKGROUND
[0002] Background description includes information that may be useful in understanding the present disclosure. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed disclosure, or that any publication specifically or implicitly referenced is prior art.
[0003] Presently, vendors, manufacturers and farmers plan their ventures in harvest and related items, including raw materials, tools and finished products based on experience and intuitive expectations. However, such efforts may fail due to multiple reasons like changes in environmental conditions, changes in regulations of relevant governing institutions, changes in requirement and delivery of items, leading to irrefutable damages to the corresponding ventures.
[0004] Hence, there is need in the art to develop a system and a method that can automatically provide customized guidance related to planning future ventures to multiple entities like vendors, manufacturers and farmers with predictive information. The prediction is based on multiple factors including locality based environmental and social factors, analysis of a set of patterns pertaining to usage and interest in different items among multiple entities of the locality over different time frames and determination of relative influence of the factors on a specific query placed by a requesting entity to the system. Existing literatures disclose systems that generate alerts corresponding to items of low availability, but do not disclose any system or method that considers interactions with multiple entities, analysis of current and historical patterns of interactions, forecasts attributes related to the queries in a future time frame, determines relative degrees of influence of the multiple factors and generates customized responses quantifying the success and damages to the planned ventures.
OBJECTS OF THE PRESENT DISCLOSURE
[0005] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.
[0006] It is an object of the present disclosure to provide a system and method for providing predictive information through interaction between the system and multiple entities, the system comprising a computing device, configured to receive inputs from a requesting entity.
[0007] It is an object of the present disclosure to provide a system for providing predictive information through interaction, that facilitates the interactions to pertain to one or more predetermined first time interval before reception of a query from the requesting entity.
[0008] It is an object of the present disclosure to provide a system for providing predictive information through interaction that enables a processing unit coupled to the computing device to determine a pattern of the interactions between the system and the multiple entities.
[0009] It is an object of the present disclosure to provide a system for providing predictive information through interaction that enables selection of the multiple entities from within a predetermined distance about the determined geographical location of the requesting entity.
[0010] It is an object of the present disclosure to provide a system for providing predictive information through interaction that enables the computing device to determine a second set of attributes pertaining to the identified geographical location, response to the query being dependent on the second set of attributes.
[0011] It is an object of the present disclosure to provide a system for providing predictive information through interaction that enables a database operatively coupled to the processing unit to store the second set of attributes and a third set of attributes of the interactions and update related information periodically.
[0012] It is an object of the present disclosure to provide a system for providing predictive information through interaction that enables the processing unit to predict a fourth set of attributes related to the query, based on the determined patterns of the third set of attributes and the second set of attributes.
[0013] It is an object of the present disclosure to provide a system for providing predictive information through interaction that facilitates the prediction of the fourth set of attributes pertaining to one or more predetermined second time interval after reception of the query.
[0014] It is an object of the present disclosure to provide a system for providing predictive information through interaction that enables the processing unit to determine the response from a weighted combination of the multiple attributes pertaining to the location, the patterns and the prediction.
[0015] It is an object of the present disclosure to provide a system for providing predictive information through interaction that enables the computing device to transmit the customized response to the requesting entity over a secured communication network.
[0016] It is an object of the present disclosure to provide a system for providing predictive information through interaction that facilitates generation of alert signals pertaining to previous interactions, upon updating information stored in a database coupled to the computing device.
[0017] It is an object of the present disclosure to provide a system for providing predictive information through interaction that facilitates a requesting entity to access information shared by multiple entities with the system based on a set of features associated with each entity communicatively coupled to the system.
[0018] It is an object of the present disclosure to provide a method for providing predictive information through interaction that enables the processing unit to collect a personalized information of the requesting entity and determine the response based on relative influence of any or a combination of factors including the personalized information, location of the requesting entity, current and historical patterns of interactions between the computing device and multiple entities around the location, social factors, upcoming events, climatic changes and forecast of outcomes pertaining to the query.

SUMMARY
[0019] The present disclosure relates to the field of query based information facility. In particular, the present disclosure provides a system and method for providing predictive information, determined through interaction between the system and multiple entities.
[0020] In an aspect, the system may comprise a computing device that may be configured to receive inputs in the form of a first set of data packets from a user device associated with the requesting entity, the user device being communicatively coupled to the system through a secured network.
[0021] In an aspect, the system may be configured to authenticate the requesting entity and identify a category that the requesting entity may belong to, the category pertaining to a first, second and third entity among the multiple entities.
[0022] In an aspect, the second set of data packets may pertain to a first set of attributes of the query, the first set of attributes including geographical location of the requesting entity.
[0023] In an aspect, upon determination of the geographical location, the processing unit coupled to the computing system may be configured to identify one or more first, second and third entities located within a predetermined distance from the detected geographical location.
[0024] In an aspect, information shared by each of the registered first, second and third entities with the system may be facilitated to be accessed by the requesting entity based on a set of features.
[0025] In an aspect, the system may be configured to receive a third set of data packets from the database , the third set of data packets pertaining to a second set of attributes related to the geographical location.
[0026] In an aspect, the processing unit may be enabled to determine a pattern pertaining to a third set of attributes corresponding to interactions between the system and the identified one or more first, second and third entities.
[0027] In an aspect, a fourth set of data packets corresponding to the interactions may be generated at the processing unit, the interactions pertaining to one or more predetermined first time intervals before reception of the query.
[0028] In an aspect, the predicted information may be determined from a previously generated third set of attributes pertaining to said time frame, the third set of attributes being stored in the database .
[0029] In an aspect, the system may be enabled to analyze variations in the fourth set of data packets and determine a fourth set of attributes related to the query, based on the analysis of the patterns of the third set of attributes and the second set of attributes related to the geographical location.
[0030] In an aspect, the predicted information may correspond to a fifth set of data packets, the fifth set of data packets pertaining to one or more predetermined second time intervals after reception of the query.
[0031] In an aspect, the processing unit of the computing device may be enabled to determine relative degree of influence of the third, fourth and fifth set of data packets pertaining to the query.
[0032] In an aspect, the system may be configured to determine a sixth set of data packets corresponding to the response to the query by calculating a weighted combination of the third, fourth and fifth set of data packets and transmit the sixth set of data packets to the requesting entity.
[0033] In an aspect, the computing device may be enabled to update information pertaining to the third, fourth, fifth and sixth set of data packets stored in the database.
[0034] In an aspect, the updated information may be used by the computing device to reconfigure previous responses to one or more queries and transmit the updated responses to the corresponding requesting entities.
[0035] In an aspect the computing device may be configured to receive authentication details from the requesting entity, the authentication details including any or a combination of first name, last name, contact number, email id, and an authentication key code.
[0036] In an aspect, the one or more entities may be facilitated to enroll with the system in one or more categories corresponding to the authentication details.
[0037] In an aspect, the system may be enabled to broadcast information to the registered user devices associated with the first, second and third entities, the information pertaining to any or a combination of frequently received queries, sudden changes in second set of attributes, changes in patterns corresponding to the third set of attributes, and changes in predicted fourth set of attributes.
[0038] In an aspect, the system may be facilitated to generate a set of alert signals pertaining to previous interactions between the requesting entity and the computing device, the alert signals being transmitted to the requesting entity through any or a combination of electronic mails, messages and automated calls.
[0039] It is an aspect of the present disclosure is to provide a method for providing predictive information through interaction, that enables the processing unit of the computing device to receive a query including a set of personalized information from the requesting entity, the received query being in the form of first set of data packets
[0040] In an aspect, the method may enable in extracting a second set of data packets pertaining to a first set of attributes of the query, the second set of data packets including category and geographical location of the requesting entity.
[0041] In an aspect, upon determination of the geographical location, the method may be configured to identify one or more first, second and third entities located within a predetermined distance from the detected geographical location.
[0042] In an aspect, the method may enable in receiving a third set of data packets from the database , the third set of data packets pertaining to a second set of attributes related to the geographical location.
[0043] In an aspect, the method may enable in determining a pattern pertaining to a third set of attributes corresponding to interactions between the system and the identified one or more first, second and third entities.
[0044] In an aspect, a fourth set of data packets corresponding to the interactions may be generated by the method, the interactions pertaining to one or more predetermined first time intervals before reception of the query.
[0045] In an aspect, the method may enable in analyzing variations in the fourth set of data packets and determining a fourth set of attributes related to the query, based on the analysis of the patterns of the third set of attributes and the second set of attributes related to the geographical location.
[0046] In an aspect, the method may enable in determining relative degree of influence of the third, fourth and fifth set of data packets pertaining to the query, the fifth set of data packets corresponding to the predicted information and pertaining to one or more predetermined second time intervals after reception of the query.
[0047] In an aspect, the method may be enabled in determining a sixth set of data packets corresponding to the response to the query by calculating a weighted combination of the third, fourth and fifth set of data packets and transmitting the sixth set of data packets to the requesting entity.
[0048] In an aspect, the method may enable in updating information pertaining to the third, fourth, fifth and sixth set of data packets stored in the database.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
[0049] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.
[0050] The diagrams described herein are for illustration only, which thus are not limitations of the present disclosure, and wherein:
[0051] FIG. 1 illustrates network architecture (100) of the proposed system for providing predictive information through interaction, to elaborate upon its working in accordance with an embodiment of the present disclosure.
[0052] FIG. 2 illustrates exemplary functional components (200) of a processing unit (104) of the computing device (102) of the proposed system for providing predictive information through interaction, in accordance with an embodiment of the present disclosure.
[0053] FIG. 3 illustrates an exemplary method (300) of proposed system for providing predictive information through interaction, in accordance with an embodiment of the present disclosure.
[0054] FIG. 4 illustrates an exemplary computer system (400) in which or with which embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure.

DETAILED DESCRIPTION
[0055] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[0056] If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0057] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[0058] While embodiments of the present invention have been illustrated and described in the accompanying drawings, the embodiments are offered only in as much detail as to clearly communicate the disclosure and are not intended to limit the numerous equivalents, changes, variations, substitutions and modifications falling within the spirit and scope of the present disclosure as defined by the appended claims.
[0059] The present disclosure relates to the field of query based information facility. In particular, the present disclosure provides a system and method for providing predictive information, determined through interaction between the system and multiple entities, in response to a query.
[0060] FIG. 1 illustrates network architecture (100) of the proposed system for providing predictive information through interaction, to elaborate upon its working in accordance with an embodiment of the present disclosure.
[0061] In an illustrative embodiment, a system for providing predictive information through interaction (100) (interchangeably known as the system (100)) may include a computing device (102) that may be configured to receive inputs from a requesting entity belonging to one or more entities (112), communicatively coupled to the computing device (102) through a secured network (106). In an embodiment, enrolment of the one or more entities (112) (individually and interchangeably referred to as user or entity and collectively referred to as entities, herein ) with the system (100) may include a first time registration using a set of personalized details like but not limited to name, age, gender, occupation, place of residence, contact number and email ID. The one or more entities (112) may be associated with one or more user devices (110-1, 110-2,…, 110-N) that may be registered with the system through auto-generated one time passwords.
[0062] In an embodiment, the user (112) may belong to any of the categories of a first entity (112A), a second entity (112B) and a third entity (112C). In an embodiment, the system may be coupled to one or more entities of same category. By way of example, a requesting entity (112) may be any one of the one or more first entities (112A-1,112A-2,…,112A-N), one or more second entities (112B-1,112B-2,…,112B-N) and one or more third entities (112C-1,112C-2,…,112C-N).
[0063] In an embodiment, interactions between the entities (112) and the computing device (102) may include sharing of information pertaining to interests and requirements of the entities and guidance provided by the system (100) related to ventures associated with the entities. In an exemplary embodiment, interactions may include searches, queries, requirement wishlists, items procured, items vended and the likes with one or more entities (112) through the system (100). By way of example, information shared by the entity may include a set of features including but not limited to items intended to be vended, exchange rates, number of items available, brand of items, specifications of items, dates of manufacture and expiry, user ratings and contact information. The one or more first (112A), second (112B) and third (112C) entities may be facilitated to be accessed by the requesting entity (112) based on the set of features.
[0064] In an exemplary embodiment, the category of the entities (112) may be determined from the nature of interaction between the entities (112) and the system (100) in any or a combination of a current and a previous log-in session. In another embodiment, the category may be related to the occupation of the entities. In yet another embodiment, the category of the requesting entity (112) may be retrieved form the query placed by the requesting entity (112). By way of example, a user belonging to the first entity (112A) may be a farmer, a user belonging to the second entity (112B) may be a vendor and a user belonging to the third entity (112C) may be a manufacturer.
[0065] In an embodiment, the user (112) may be enabled to enroll in any of the categories of the first, second and third entities with the same authentication details in different log-in sessions with the system (100). By way of example, a user who may have previously accessed the system (100) in the category of a vendor may be enabled to place a query in the category of a manufacturer in another session of accessing the system (100).
[0066] In an embodiment, the requesting entity (112) may be enabled to place a query with the system from the associated registered user device (110). The computing device (102) may be enabled to receive the query in the form of a first set of data packets. The first set of data packets may include a first set of attributes pertaining to personalized information of the requesting entity (112) including but not limited to type of his preferred crop, requested information related to farming of the preferred crop, amount of land he owns, location of the land, infrastructure available to him, preferred amount of investment in the venture, preferred amount of time to harvest benefits from the venture, preferred items he wants to procure, preferred items he wants to disburse, and preferred items he wants to manufacture.
[0067] In an embodiment, the system (100) may be coupled to a processing unit (104) that may be configured to analyze the first set of attributes of the query. The processing unit may be configured to identify one or more first (112A), second (112B) and third (112C) entities communicatively coupled to the system and are located within a predetermined distance from the requesting entity (112). Depending on multiple factors including but not limited to interactions between the system (100) and the identified one or more first (112A), second (112B) and third (112C) entities and a second set of attributes pertaining to the location of the requesting entity (112), the processing unit (104) may be enabled to generate a customized response of the query.
[0068] In an embodiment, the system (100) may be enabled to broadcast information to the registered user devices (110) associated with the first (112A), second (112B), and third (112C) entities. In an embodiment, exemplary and non-limiting broadcasted information may pertain to any or a combination of items related to frequently received queries, expected changes in environmental conditions and governance regulations, changes in nature and focus of interactions and changes in forecast related to success and failures in ventures pertaining to the queries. In another embodiment, the system (100) may be facilitated to generate a set of alert signals pertaining to previous interactions between the requesting entity (112) and the computing device (102). The alert signals may be transmitted to the requesting entity (112) through any or a combination of communication media including but not limited to electronic mails, messages and automated calls.
[0069] In an embodiment, information, including the query and the response shared between the entities (112) and the computing device (102) may be transmitted through the secured network (106). The corresponding data packets may be further transmitted to a server (108) for storage and periodic updating of the stored information.
[0070] In an embodiment, the computing device (102) and the one or more user devices (110) may include any or a combination of cell phones, mobiles, laptops, computers, a smart camera, a smart phone, a portable computer, a personal digital assistant, a handheld device, computer, and the likes.
[0071] In an embodiment, the system (100) may be implemented using any or a combination of hardware components and software components such as a cloud, a server (108), a computing system, a computing device, a network device and the like. Further, the first computing device (102) and the one or more user devices (110) may interact through one or more networking units, such as Wi-Fi, Bluetooth, Li-Fi, or an application, that may reside in the computing device (102) and the one or more user devices (110). In an implementation, the system (100) may be accessed by the server (108), the computing device 102) and one or more user devices (110) that may be configured with any operating system, including but not limited to, Windows, Linux, AndroidTM, iOSTM, and the like.
[0072] In an embodiment, the network (106) may be a wireless network, a wired network or a combination thereof that can be implemented as one of the different types of networks, such as Intranet, Local Area Network (LAN), Wide Area Network (WAN), Internet, and the like. Further, the network (106) may either be a dedicated network or a shared network. The shared network may represent an association of the different types of networks that may use variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like.
[0073] FIG. 2 illustrates exemplary functional components (200) of a processing unit (104) of the computing device (102) of the proposed system for providing predictive information through interaction, in accordance with an embodiment of the present disclosure.
[0074] As illustrated in an embodiment, the processing unit (104) may include one or more processor(s) (202). The one or more processor(s) (202) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processor(s) (202) may be configured to fetch and execute computer-readable instructions stored in a memory (204) of the processing unit (104). The memory (204) may store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory (204) may include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[0075] In an embodiment, the processing unit (104) may also include an interface(s) (206). The interface(s) (206) may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) (206) may facilitate communication of the processing unit (104) with various devices including but not limited to networking hardware, printer, display units, portable mass storage devices and the likes coupled to the processing unit (104). The interface(s) (206) may also provide a communication pathway for one or more components of processing unit (104). Examples of such components include, but are not limited to, processing engine(s) (208), memory (204) and database (230).
[0076] In an embodiment, the processing engine(s) (208) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) (208). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) (208) may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) (208) may include a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) (208). In such examples, the processing unit (104) can include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to processing unit (104) and the processing resource. In an embodiment, the processing engine may be implemented as an Artificial Intelligent engine. In other examples, the processing engine(s) (208) may be implemented by electronic circuitry. A database (230) may include information that is either stored or generated as a result of functionalities implemented by any of the components of the processing engine(s) (208).
[0077] In an embodiment, the processing engine (208) may include an extraction unit (210) that may be enabled to extract a second set of data packets pertaining to the first set of attributes of the query. The second set of data packets may be configured as a computer readable binary stream corresponding to the first set of attributes.
[0078] In an embodiment, the one or more processors (202) may be enabled to determine the category of the requesting entity (112) by analyzing the extracted second set of data packets in the authentication and analyzing unit (212) of the processing engine (208). By way of example, if the requesting entity places query regarding farming of a crop like sugarcane, he may belong to the first entity (112A). If the requesting entity places query regarding an item manufactured from sugarcane, like sugar, he may belong to the second (112B) or third entity (112C). The authentication and analyzing unit (212) may also authenticate a registered user by verifying details including but not limited to any or a combination of first name, last name, contact number, email id, and an authentication key code. By way of example, authentication may also be done by facilitating transmission of a one-time password to the corresponding registered user device (110) from the computing device (102). The authentication details may be extracted from the first set of data packets and compared with authentication details stored in the database (230).
[0079] In an embodiment, the processing engine (208) may include a location determination unit (214) that may be configured to determine the geographical location of the requesting entity (112) by analyzing the extracted second set of data packets. The geographical location may be determined from the first set of attributes pertaining to the query or may be received from the requesting entity (112) as part of the interaction or from the authentication details of the requesting entity (112). In an embodiment, upon determination of the geographical location, the one or more processors (202) may be configured to identify one or more first (112A), second (112B) and third (112C) entities located within a predetermined distance from the detected geographical location. In an embodiment, the identified entities may be communicatively connected to the system (100) at the time of reception of the query or disconnected from the system, but previously registered to the system (100). By way of example, if the requesting entity is a first entity seeking information regarding farming of sugarcane, the system (100) may be configured to identify one or more sugarcane farmers (112A), sugar manufacturers (112B) and sugar vendors (112C) located in and around the same locality as the requesting first entity.
[0080] In an embodiment, the processing engine (208) may include a dependent factor determination unit (214) that may be configured to receive a third set of data packets from the database (230). The third set of data packets may pertain to a second set of attributes related to the geographical location of the requesting entity (112). By way of non-limiting examples, the second set of attributes may include amount of rain, timing of monsoon hitting said geographical location, possibilities and frequency of storms, topology, soil type, elevation, temperature and the likes. The third set of data packets may be in the form of computer readable binary stream indicating any or a combination of deductions including the suitability of said geographical location for the preferred crop corresponding to the query, suitability of timing for starting farming and collecting harvest, requirement of additional tools and/or resources like extra water, pesticide, nutrient, mulch for the soil, requirement of tillage and the likes. In another embodiment, the one or more processors (202) may also be configured to determine other dependent factors pertaining to said geographical location like but not limited to regulations, subsidies, availability of licenses, resources like irrigation, electricity, subsidized procurement of seeds, pesticides, facilities of direct procurement of harvest from the farmers laid out by concerned authorities of said locality.
[0081] In an embodiment, the processing engine (208) may include a pattern generation unit (218) that may be enabled to receive from the database (230) a third set of attributes corresponding to interactions between the system (100) and the identified one or more first (112A) , second (112B) and third (112C) entities. The interactions may pertain to one or more predetermined first time interval before the reception of the query. In an exemplary embodiment, the first predetermined time interval may correspond to a few weeks prior to the reception of the query, indicating a current trend of interactions. In another embodiment, the first predetermined time interval may correspond to a span of few months over several years prior to the reception of the query, indicating a historical trend of interactions. By way of example, the third set of attributes may include but may not be limited to items searched, items wish listed, items booked, items vended and items frequently queried for procurement and disbursal. In an embodiment, depending on the historical and current indication of trend of interest and usage of the entities (112) over multiple time frames a pattern may be generated from the deductions that may influence response to the query. An interaction analysis unit (220) of the processing engine (208) may be enabled to analyze variations of the determined pattern and generate a fourth set of data packets that may be configured in the form of computer readable binary stream.
[0082] In an embodiment, the processing engine (208) may include a prediction unit (222) that may be configured to predict or forecast a fourth set of attributes pertaining to the query and correspondingly generate a fifth set of data packets. In an exemplary embodiment, the fourth set of attributes may include but may not be limited to any or a combination of information pertaining appropriate time for starting a venture, investment required for said venture, accessories necessary for the venture, quantification of success rate, specific environmental and geographical constraints or advantages that may be avoided or utilized, items that may have increased or decreased requirement and items that currently or in near future may be manufactured in larger or smaller scale.
[0083] In an embodiment, the predictions pertaining to the fourth set of attributes may be generated corresponding to one or more predetermined second time interval after reception of the query. By way of example, the second time interval may indicate a time frame of six months ahead of the placement of the query. In an embodiment, generation of the fifth set of data packets may be done by the one or more processors (202) based on the fourth set of data packets pertaining to analysis of the determined pattern and on the third set of data packets pertaining to the dependent factor and location based information. By way of example, the pattern of current interactions may indicate that multiple entities are showing interests in items based on seasonal flowers. The pattern of interactions at the same time of the previous two years may show similar surge in requirement of seasonal flowers. Geographically, the location and corresponding climatic conditions may also be found to be suitable for production of seasonal flowers in large scale. Dependent factors may indicate an imminent festival in the locality that may enhance the requirement and usage of seasonal flowers. Therefore, an exemplary prediction of the system (100) may endorse farming of seasonal flowers to the requesting entity (112).
[0084] In an embodiment, the processing engine (208) may include an influence determination unit (224) that may be enabled to determine relative degree of influence of the third, fourth and fifth set of data packets pertaining to the query. By way of example, the fourth set of data packets may indicate a reduced pattern of interest in multiple entities in a particular crop two years back due to lack of rain. But the third set of data packets may indicate introduction of a subsidized license for using one or more water bodies in the locality for facilitating irrigation in the last few months by concerned authorities. The fifth set of data packets may predict a growth in items manufactured from said crop over the next few months. Therefore the one or more processors (202) may assign relative weights to the multiple data packets for determination of a customized response to a query pertaining to the particular crop.
[0085] In an embodiment, the processing engine (208) may include a response generation unit (226) that may be configured to determine a sixth set of data packets corresponding to the response to the query. The response may be enabled to include a weighted combination of the third, fourth and fifth set of data packets. The one or more processors (202) may be enabled to transmit the sixth set of data packets to the requesting entity (112) over the network (106).
[0086] In an embodiment, the processing engine (208) may include other units (228) that may be configured to implement functionalities that supplement actions performed by the one or more processors (202). In an exemplary embodiment, such actions may include updating information pertaining to the third, fourth, fifth and sixth set of data packets stored in the database (230). In an embodiment, the updated information may be used by the computing device (102) to reconfigure previous responses to one or more queries and transmit the updated responses to the corresponding requesting entities.
[0087] FIG. 3 illustrates an exemplary method (300) of proposed system for providing predictive information through interaction, in accordance with an embodiment of the present disclosure.
[0088] The method (300) may include a step (302), of receiving, through a computing device (102), inputs from a requesting entity (112), and correspondingly generating a first set of data packets. The first set of data packets may include a first set of attributes pertaining to personalized information of the requesting entity (112) including but not limited to his requirements, authentication details, geographical location and availability of resources.
[0089] In an embodiment, the method (300) may include a step (304), of extracting, at a processing unit (104), operatively coupled to the computing device (102), a second set of data packets, pertaining to a first set of attributes of the query placed by the requesting entity (112), from the first set of data packets. The second set of data packets may include digital information pertaining to the first set of data packets in computer readable binary format.
[0090] In an embodiment, the method (300) may include a step (306), of determining, at the processing unit (104), based on the first set of attributes, category of the requesting entity (112). The requesting entity may belong to any of the categories of one or more first (112A), second (112B) and third (112C) entities communicatively coupled to the system (100).
[0091] In an embodiment, the method (300) may include a step (308), of determining, at the processing unit (104), geographical location of the requesting entity (112), from the second set of data packets. Information related to the geographical location may include the identification of the part of the country, elevation, soil type, climatic condition, and the likes.
[0092] In an embodiment, the method (300) may include a step (310), of identifying, at the processing unit (104), one or more first (112A), second (112B) and third (112C) entities located within a pre-determined range from the determined geographical location of the requesting entity (112). The one or more entities of each category may be identified based on the previous interactions of the one or more entities with the system (100) and the information shared by the one or more entities (112) that may be accessed based on a set of features like items, quantity, brand, manufacture and expiry dates, user ratings and the likes.
[0093] In an embodiment, the method (300) may include a step (312), of receiving, at the processing unit (104), a third set of data packets corresponding to a second set of attributes related to the geographical location of the requesting entity (112), from a database (230), operatively coupled to the one or more processors (202) of the processing unit (104). The second set of attributes may include climatic conditions of the locality, festivals, social factors, restrictions, resources provided and subsidies imposed by governing bodies in the locality and the likes.
[0094] In an embodiment, the method (300) may include a step (314), of determining, at the processing unit (104), a pattern of a third set of attributes pertaining to interactions between the computing device (102) and the identified one or more first (112A), second (112B) and third (112C) entities. The one or more processors (202) of the processing unit (104) may be enabled to generate a fourth set of data packets pertaining to the studied patterns. The patterns may be determined by studying interactions pertaining to one or more predetermined first time intervals before the reception of the query. The one or more first time intervals may pertain to a few weeks or a few years previous to the reception of the query. The patterns may be indicative of the trend of usage and interest of the multiple entities in multiple items over said time intervals.
[0095] In an embodiment, the method (300) may include a step (316), of analyzing, at the processing unit (104), variations in the third set of attributes based on the determined fourth set of data packets. Variations in the third set of attributes may include increase and reduction in multiple items, shift of domain of interest in multiple items, enhancement of requirement of multiple modified items over their traditional forms and the likes.
[0096] In an embodiment, the method (300) may include a step (318), of predicting, at the processing unit (104), a fourth set of attributes pertaining to the query. The one or more processors (202) of the processing unit (104) may be enabled to generate a fifth set of data packets corresponding to the predicted fourth set of attributes. The prediction may pertain to one or more predetermined second time intervals after reception of the query. The one or more predetermined second time intervals may pertain to a few months ahead of the time of reception of the query. The fourth set of attributes may include time of starting farming, time of procuring items, additional requirements for farming or storage, guidance regarding investments, growth or fall in requirement of items and the success rate in said time frame.
[0097] In an embodiment, the method (300) may include a step (320), of determining, at the processing unit (104), relative degree of influence of the third, fourth and fifth set of data packets pertaining to the query and generating a sixth set of data packets corresponding to a response to the query. The sixth set of data packets may be generated as a weighted combination of the third, fourth and fifth set of data packets.
[0098] In an embodiment, the method (300) may include a step (322), of transmitting, by the processing unit (104), the sixth set of data packets to the registered user device (110), associated with the requesting entity (112), using the communication network (106).
[0099] In an embodiment, the method (300) may include a step (324), of updating, at the processing unit (104), information stored in the database (230) with the third, fourth, fifth and sixth set of data packets. The updated information may be transmitted over the network (106) and stored in the server (108) for future use.
[00100] FIG. 4 illustrates an exemplary computer system (400) in which or with which embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure.
[00101] As shown in FIG. 4, computer system includes an external storage device (410), a bus (420), a main memory (430), a read only memory (440), a mass storage device (450), communication port (460), and a processor (470). A person skilled in the art will appreciate that computer system may include more than one processor and communication ports. Examples of processor (470) include, but are not limited to, an Intel® Itanium® or Itanium 2 processor(s), or AMD® Opteron® or Athlon MP® processor(s), Motorola® lines of processors, FortiSOC™ system on a chip processors or other future processors. Processor (470) may include various modules associated with embodiments of the present invention. Communication port (460) can be any of an RS-232 port for use with a modem based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. Communication port (460) may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which computer system connects.
[00102] In an embodiment, the memory (430) can be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. Read only memory (440) can be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or BIOS instructions for processor (470). Mass storage (450) may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces), e.g. those available from Seagate (e.g., the Seagate Barracuda 7102 family) or Hitachi (e.g., the Hitachi Deskstar 7K1000), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g. an array of disks (e.g., SATA arrays), available from various vendors including Dot Hill Systems Corp., LaCie, Nexsan Technologies, Inc. and Enhance Technology, Inc.
[00103] In an embodiment, the bus (420) communicatively couples processor(s) (470) with the other memory, storage and communication blocks. Bus (420) can be, e.g. a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), USB or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which connects processor (470) to software system.
[00104] In another embodiment, operator and administrative interfaces, e.g. a display, keyboard, and a cursor control device, may also be coupled to bus (420) to support direct operator interaction with computer system. Other operator and administrative interfaces can be provided through network connections connected through communication port (460). External storage device (410) can be any kind of external hard-drives, floppy drives, IOMEGA® Zip Drives, Compact Disc - Read Only Memory (CD-ROM), Compact Disc - Re-Writable (CD-RW), Digital Video Disk - Read Only Memory (DVD-ROM). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system limit the scope of the present disclosure.
[00105] As used herein, and unless the context dictates otherwise, the term "coupled to" is intended to include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements). Therefore, the terms "coupled to" and "coupled with" are used synonymously. Within the context of this document terms "coupled to" and "coupled with" are also used euphemistically to mean “communicatively coupled with” over a network, where two or more devices are able to exchange data with each other over the network, possibly via one or more intermediary device.
[00106] The terms, descriptions and figures used herein are set forth by way of illustration only. Many variations are possible within the spirit and scope of the subject matter, which is intended to be defined by the following claims and their equivalents in which all terms are meant in their broadest reasonable sense unless otherwise indicated.
[00107] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

ADVANTAGES OF THE INVENTION
[00108] The present disclosure provides for a system and method for providing predictive information through interaction between the system and multiple entities, the system comprising a computing device, configured to receive inputs from a requesting entity.
[00109] The present disclosure provides for a system for providing predictive information through interaction, that facilitates the interactions to pertain to one or more predetermined first time interval before reception of a query from the requesting entity.
[00110] The present disclosure provides for a system for providing predictive information through interaction that enables a processing unit coupled to the computing device to determine a pattern of the interactions between the system and the multiple entities.
[00111] The present disclosure provides for a system for providing predictive information through interaction that enables selection of the multiple entities from within a predetermined distance from the determined geographical location of the requesting entity.
[00112] The present disclosure provides for a system for providing predictive information through interaction that enables the computing device to determine a second set of attributes pertaining to the identified geographical location, response to the query being dependent on the second set of attributes.
[00113] The present disclosure provides for a system for providing predictive information through interaction that enables a database operatively coupled to the processing unit to store the second set of attributes and a third set of attributes of the interactions and update related information periodically.
[00114] The present disclosure provides for a system for providing predictive information through interaction that enables the processing unit to predict a fourth set of attributes related to the query, based on the determined patterns of the third set of attributes and the second set of attributes.
[00115] The present disclosure provides for a system for providing predictive information through interaction that facilitates the prediction of the fourth set of attributes to pertain to one or more predetermined second time interval after reception of the query.
[00116] The present disclosure provides for a system for providing predictive information through interaction that enables the processing unit to determine the response from a weighted combination of the multiple attributes pertaining to the location, the patterns and the prediction.
[00117] The present disclosure provides for a system for providing predictive information through interaction that enables the computing device to transmit the customized response to the requesting entity over a secured communication network.
[00118] The present disclosure provides for a system for providing predictive information through interaction that facilitates generation of alert signals pertaining to previous interactions, upon updating information stored in a database coupled to the computing device.
[00119] The present disclosure provides for a system for providing predictive information through interaction that facilitates a requesting entity to access information shared by multiple entities with the system based on a set of features associated with each entity communicatively coupled to the system.
[00120] The present disclosure provides for a method for providing predictive information through interaction that enables the processing unit to collect a personalized information of the requesting entity and determine the response based on relative influence of any or a combination of factors including the personalized information, location of the requesting entity, current and historical patterns of interactions between the computing device and multiple entities around the location, social factors, upcoming events, climatic changes and forecast of outcomes pertaining to the query.