Claims:Claims:
We claim:
1. An intelligent system (100) for facilitating decision making, the system (100) comprising:
a. a memory unit (101) to store a collection module (103), curation module (104), contextual module (105), classification module (106), connecting module (107), correlation module(108), compilation module (109) and a creation module (110);
b. a graphic processing unit (102) to execute a set of instruction stored in the collection module (103), curation module (104), contextual module (105), classification module (106), connecting module (107), correlation module(108), compilation module (109) and the creation module (110) to provide actionable insights related to an entity pertaining to one or more domain of interest to the user, wherein:
i. the collection module (103) extracts a plurality of structured and unstructured data from a digital platform;
ii. the curation module (104) identifies relevant data from the extracted data by executing a relevant marking analysis;
iii. the contextual module (105) learns from the identified relevant data and establishes a contextual data pattern through a neural network layer;
iv. the classification module (106) classifies the contextual data pattern based on the context of the subject determined from the extracted data in the domain pertinent to the interest of the user;
v. the connecting module (107) connects the classified data by incorporating a meta-data analytics by:
a. identifying one or more entity gathered from a plurality of domain pertinent to interest of the user;
b. accessing the user-base of an entity from the extracted data in the domain pertinent to interest of the user to establish the significance of the entity;
c. identifying social media sentiments from social media platforms pertaining to the entity corresponding to the domain pertinent to the interest of the user;
d. identifying number of active users pertaining to the entity in professional media business; and
e. gathering application market data pertaining to the application usage pertaining to the entity corresponding to the domain pertinent to the interest of the user;
vi. the correlation module (108) correlates the extracted data with the historical patterns and trends of the conforming data, wherein the patterns and trends of the conforming data is analyzed to stipulate the potential impact of the extracted data in the domain pertinent to interest of the user;
vii. the compilation module (109) connected to the correlation module (108) to compile one or more parameters of the correlated data to establish the predicted outcome of the entity corresponding to the domain pertinent to the interest of the user;
viii. the creation module (110) creates an executive summary of the research insights pertaining to the entity corresponding to the domain pertinent to the interest of the user in order to derive actionable insights; and
ix. a display unit (111) displays the created executive summary of the research insights pertaining to the entity corresponding to the domain pertinent to the interest of the user in order to derive actionable insights on a graphical user-interface of the user device.

2. The system (100) as claimed in claim 1, wherein the structured data relates to market data, company published report data, fundamental data and financial data of the entity for providing quantitative analysis pertaining to the entity corresponding to the domain pertaining to the interest of the user.

3. The system (100) as claimed in claim 1, wherein the unstructured data relates to a news data, social sentiments gathered from a plurality of social media platforms and analyst rating gathered from global platform for providing qualitative analysis pertaining to the entity corresponding to the domain pertaining to the interest of the user.

4. The system (100) as claimed in claim 1, wherein the extracted data from a digital platform is recognized and sorted for relevance marking.

5. The system (100) as claimed in claim 1, wherein the collection module (103) performs a deduping process to track the recurrence of the eliminated information from the extracted data to prognosticate the outreach of the information.

6. The system (100) as claimed in claim 1, wherein the curation module (104) performs a relevance marking on the extracted data to categorize the extracted data in the order of higher and lower relevance on the basis of the significance of the extracted data.

7. The system (100) as claimed in claim 1, wherein the contextual data pattern is established by providing an exhaustive insight of the relevant data gathered from the curation module (104).

8. The system (100) as claimed in claim 1, wherein the classification module (106) uses semantic tagging and clustering analysis technique to classify the contextual data pattern based on the context of the subject determined from the extracted data in the domain pertinent to the interest of the user.

9. The system (100) as claimed in claim 1, wherein the connecting module (107) connects the classified data based on the outreach of the extracted data to the audience on a plurality of platforms and databases.
, Description:PREAMBLE TO THE DESCRIPTION
[001] The following specification particularly describes the invention and the manner in which it is to be performed:
DESCRIPTION OF THE INVENTION
Technical field of the invention
[002] The present invention relates to an intelligent system for facilitating decision making and more particularly relates to an intelligent system for providing insights related to an entity corresponding to the domain pertinent to the interest of the user in order to derive actionable insights.
Background of the invention
[003] The success or failure in attaining the objective of the individual or entities depends on the approach and the decision making ability which the firm or individual possess. The uncertainty of the forthcoming consequence affects the rational thinking of one or more individuals. An incorrect decision or failure to achieve the desired outcome may catastrophe the psychological and economic status of an individual or an entity. A negative state of mind of an individual or an entity affects a decision making ability which may lead to a disastrous outcome whereas the complacent state of mind may have the same negative effects in the outcome which is caused due to a shallow decision.
[004] Over the years, a few statistical methods were employed to inspect and prognosticate the outcome to aid in different domains such as financial sector by making use of fundamental analysis of the preceding data. The conventional method requires considerable human intervention to prepare, analyze and interpret the data in order to predict the output which is prone to error. Further, the method employed to undertake an analysis based prediction is limited since it considers a scant amount of data which is manually fed and the output provided is defined by the confined data thereby producing vague results. The existing solutions fail to adapt to a rapidly changing landscape since the method follows a rule based system to analyze and predict from a pre-defined set of data. Further, the conventional methods employed to assist in decision making involve a limited insight from inadequate data providing inaccurate, delayed and rigid output.
[005] The US Patent Application No. US7130836B2 relates to a computer-aided decision-making system and method that is applicable to a variety of decision-making contexts and applications such as, but not limited to, automobile or home purchase decisions. The computer-aided decision-making system provides immediate, useful, and relevant information to a person in a decision-making context, overcoming common human cognitive problems that occur in decision-making, and enabling consumer purchases in an on-line sales environment. In particular, aspects of the invention that aid a person in decision-making include, but are not limited to: managing all the sub-decisions, educating the decision-maker, highlighting the most important sub-decisions, offering the most viable proposals for evaluation, distinguishing significant differences between proposals, supplying various evaluation tools, preventing blind spots, assisting the decision-maker's memory, gauging the progress of the decision process, and learning about the decision maker from the decision process.
[006] The US Patent Application No. US5732397A discloses an automated system for use in decision-making, comprises input, output processing and storage capabilities. The storage and processing portions of the system include a number of components which receive information and data specific to a selected topic of decision-making. The components perform data collection, screening and decision-making functions. The system compares received data to previously stored data and identifies received data which does and does not correspond to the previously stored data. If the data does correspond, the system implements a first decision operation. If the received data does not correspond, the system determines the nature and degree of such non-correspondence, and implements a decision operation which is reflective of the nature and degree of the non-correspondence. If the nature and degree of non-correspondence cannot be determined, additional data pertaining to the selected topic is requested and compared to the previously stored data in an iterative manner. Discrepancies existing between corresponding elements of data are identified. The system also identifies a plurality of alternative options for the first and second decision operations, each of which may alternatively be selected to form at least a part of the decision operation. Data relating to the decision operations implemented are stored and compared to subsequently received data relating to outcomes. The system may be modified based on this comparison.
[007] However, most of the existing system that provides insights for decision-making is based on a pre-defined set of rules which fails to provide insights based on accurate, swift and social sentiments gathered from a plurality of networks of the entity in the domain relevant to the interest of the user.
[008] Hence, there is a need for an intelligent system for facilitating enhanced decision making by providing insights based on accurate, swift and social sentiments of the entity in the domain relevant to the interest of the user.
Summary of the invention
[009] The present invention relates to a system for facilitating decision making. The system comprises a memory unit to store a collection module, curation module, contextual module, classification module, connecting module, correlation module, compilation module and a creation module. The memory unit is connected to a graphic processing unit which executes a set of instruction stored in the collection module, curation module, contextual module, classification module, connecting module, correlation module, compilation module and the creation module to provide actionable insights related to an entity corresponding to the domain pertinent to the interest of the user. The collection module extracts multiple structured and unstructured data from a digital platform by employing data farming operation, wherein the data extracted from the digital platform undergoes data harvesting process which filters the extracted data by performing a deduping process thereby following an intelligent compression technique to eliminate duplicate information and increase the storage utilization.
[0010] The curation module identifies relevant data by executing the relevant marking analysis which establishes the data uncovered from a collection module as higher and lower relevance on the basis of its significance in the corresponding domain. Further, the contextual module learns from the identified relevant data and establishes a contextual data pattern through a neural network architecture. The contextual module employs natural language processing and natural level understanding which may be a natural language application programming interface to interpret the relevant data which is in the form of text or speech by providing an exhaustive insight of the relevant data gathered from the curation module.
[0011] The classification module classifies the contextual data pattern based on the relevance of the contents determined from the extracted data thereby connecting the classified data through a metadata analytics based on the significance of the data which is measured by assessing the outreach of the extracted data to the audience on multiple platforms and databases. A correlation module implements a deep learning technique to correlate the extracted data with the historical patterns and trends of the conforming data, wherein the pattern and trends of the conforming data is analyzed to stipulate the outcome signifying the potential impact of the extracted data in the domain pertinent to the interest of the user.
[0012] The compilation module compiles multiple parameters such as ranking, outcome and weight determined from the antecedent process to establish the predicted outcome of the entity corresponding to the domain pertinent to the interest of the user. The creation module extracts data from the compilation module to create an executive summary of the research insights pertaining to the entity corresponding to the domain pertinent to the interest of the user in order to derive actionable insights. A display unit displays the created executive summary of the research insights pertaining to the entity corresponding to the domain pertinent to the interest of the user in order to derive actionable insights on a graphical user interface of the user device.
[0013] Further, the system is dynamic and has a self-learning capability to adapt to modern trends which eliminates a need to create a pre-defined set of rules to provide an output thereby eliminating any human error. The intelligent system integrates alternate data with quantitative analysis to provide a meticulous insight in the domain relevant to the interest of the user thereby providing an accurate, swift and adaptable output which assists the user in making a subtle decision.
[0014] Thus, the present invention provides utility in commercial affairs, portfolio managers, sovereign wealth funds, financial sector and in investment decisions by providing an in-depth analysis and prudent prediction to assist in conclusive decision making. The present invention establishes a deep analysis price behavior of each asset which determines the product price on the basis of product engagement with potential customer and guides the user to buy, hold or sell indicator per asset. The intelligent system characterizes the weight of the assets based on market behavior in order to minimize anticipated capital loss risk and meet the anticipated return objectives.
Brief description of drawings
[0015] FIG 1 illustrates an intelligent system for facilitating decision making in multiple domains.
[0016] FIG 2 illustrates a block diagram of the system for providing actionable insights to facilitate in decision making.
Detailed description of the invention
[0017] Reference will now be made in detail to the description of the present subject matter, one or more examples of which are shown in figures. Each example is provided to explain the subject matter and not a limitation. Various changes and modifications obvious to one skilled in the art to which the invention pertains are deemed to be within the spirit, scope and contemplation of the invention.
[0018] The present invention provides an intelligent system for facilitating decision making. The system comprises a memory unit to store multiple modules and a graphic processing unit to execute a set of instruction stored in multiple modules thereby providing actionable insights related to an entity corresponding to the domain pertinent to the interest of the user. The graphic processing unit comprises a collection module to extract multiple structured and unstructured data from a digital platform thereby identifying the relevance of the data by executing a relevant marking analysis on the extracted data. The relevant data is interpreted, classified, correlated and compiled to create an executive summary of the research insights relevant to the entity in the domain pertinent to the interest of the user which is displayed on a display unit to derive actionable insights.
[0019] Figure 1 illustrates a system (100) for facilitating decision making in multiple domain pertinent to the interest of the user. The system (100) comprises a memory unit (101) to store a collection module (103), curation module (104), contextual module (105), classification module (106), connecting module (107), correlation module (108), compilation module (109), and a creation module (110). The system (100) comprises input/output (i/0) ports which are connected to the memory unit (101) for receiving/transmitting data. The memory unit (101) is connected to a graphic processing unit (102) which may be a high performing processing unit to execute a set of instruction stored in the collection module (103), curation module (104), contextual module (105), classification module (106), connecting module (107), correlation module (108), compilation module (109) and the creation module (110) to provide actionable insights related to the entity corresponding to the domain pertinent to the interest of the user.
[0020] Figure 2 illustrates a block diagram of the system (100) for providing actionable insights to facilitate the user in decision making. The collection module (103) extracts multiple structured data and unstructured data from a digital platform. The structured data relates to a digital data which has a high degree of organization for providing a quantitative analysis whereas the unstructured data relates to a digital data which follows an unorganized structure for providing a qualitative analysis. In another embodiment, the collection module (103) extracts multiple structured data such as an app market data of an entity, company published report data, fundamental data and financial data of the entity for providing a qualitative analysis. The collection module (103) extracts multiple unstructured data such as news data, social sentiments gathered from a plurality of social media platforms and analyst rating gathered from global platform pertaining to commercial affairs for providing a quantitative analysis. The collection module (103) extracts structured and unstructured data from a digital platform through data farming operation, wherein the extracted data is recognized and sorted for relevance marking by the collection module (103). Further, the extracted data undergoes data harvesting process to filter the extracted data by performing a deduping process which follows an intelligent compression technique to eliminate duplicate information from the extracted data thereby increasing the storage utilization. The collection module (103) also tracks the recurrence of the eliminated information from the extracted data to prognosticate the outreach of the information. For example, the deduping process eliminates the duplicate copies of identical news reported by multiple news sources but tracks the reappearance of the news displayed from multiple sources to determine the weightage of the information thereby predicting the outreach of the news.
[0021] The curation module (104) identifies the relevant data from the extracted data by executing a relevant marking analysis thereby providing a relevance to one or more category of information extracted from the collection module (103). The relevance marking of the structured and unstructured data is performed to categorize the extracted data in the order of higher and lower relevance on the basis of the significance of the extracted data in the domain pertinent to the interest of the user. For example, an entity corresponding to the interest of the user publishes a new-brand of product which may be marked with lower relevance, since it has a minimal impact on the commercial affairs.
[0022] The contextual module (105) learns from the identified relevant data and establishes a contextual data pattern through a neural network layer. The natural language processing and natural level understanding layer is implemented in the contextual module (105) to provide an exhaustive insight of the relevant data by scrutinizing multiple contents and factors which may be in the form of text or speech thereby establishing a contextualized data pattern. For example, the contextual module (105) evaluates the multiple factors such as topics, contents, human bias and tonality of the individual corresponding to an entity to form a contextualized data pattern of the entity in the domain pertinent to the interest of the user.
[0023] The classification module (106) of the system (100) classifies the contextualized data pattern based on the context of the subject determined from the extracted data in the domain pertinent to the interest of the user. In an embodiment, the classification module (106) classifies the contextualized data in different categories such as business related, legal related, branding related and price related through a semantic topic tagging and clustering analysis. The semantic tagging and clustering analysis technique employed in the classification module (106) classifies the contextual data pattern based on the context of the subject determined from the extracted data in the domain pertinent to the interest of the user.
[0024] The connecting module (107) connects the classified data by incorporating a meta-data analytics to emphasize the data by initiating a rank based outline reliant on the significance of the content from the extracted data. The classified data is connected based on the outreach of the extracted data to the audience on multiple platforms and databases. Further, the classified data are connected by incorporating the meta-data analytics by identifying one or more entity gathered from a plurality of domain pertinent to interest of the user and accessing the user-base of an entity from the extracted data in the domain pertinent to interest of the user to establish the significance of the entity.
[0025] In an embodiment, the metadata analytics further comprises identifying social media sentiments from social media platforms pertaining to the entity corresponding to the domain pertinent to the interest of the user and identifying the number of active users pertaining to the entity in professional media business. The metadata analytics gathers application market data pertaining to the application usage pertaining to the entity corresponding to the domain pertinent to the interest of the user. Further, the employee sentiment and employee turnover are evaluated to determine the aptness and stability of the entity which is extracted from the data. For example, the connecting module (107) enumerates the number of current employee with the departed employee and identifies the average retention period of the employee corresponding to an entity. Further, the connecting module (107) gathers the feedback of the employee and evaluates the bias of one or more individuals pertaining to the entity in order to establish the relevance of the entity.
[0026] In an embodiment, the correlation module (108) establishes a deep learning technique which is a subset of intelligent assistance employed to correlate the extracted data with the historic patterns and trends of the conforming data thereby developing the score by analyzing the histrionic pattern and trends of the conforming data to stipulate the outcome signifying the potential impact of the extracted data in the domain pertinent to the interest of the user. Further, the compilation module (109) connected to the correlation module (108) applies a proprietary predictive model to compile multiple parameters such as ranking, outcome and weight determined from the correlated data to establish the predicted outcome of the entity corresponding to domain pertinent to the interest of the user. The creation module (110) creates an executive summary of the research insights pertaining to the entity corresponding to the domain pertinent to the interest of the user in order to derive actionable insights. A display unit (111) displays the created executive summary of the research insights pertaining to the entity corresponding to the domain pertinent to the interest of the user in order to derive actionable insights on a graphical user interface of the user device. The graphical user interface may be the display of a computer system or a mobile device of the user for providing holistic insights thereby enabling the user to execute actionable decisions in multiple domain pertinent to the interest of the user.
[0027] In another embodiment, a source entity of the data which is identified by the collection module (103) is scanned to determine the impact of the entity on commercial affairs to assist the user in making a decision in the domain related to investments and financial affairs. The connecting module (107) determines the outreach of an entity from the extracted data by identifying the magnitude of response received from multiple databases for the entity extracted from the data. The peculiarity in the market response for a product or an entity is determined by identifying the spike in the response of multiple factors such as rise in the price and its potential value. The application store credit and the utility of the application are identified by determining the present and preceding users of the application corresponding to the entity to establish the weight and rank of the extracted data. The number of current employees with the exited employees is mapped and the response of the employees is noted which is extracted from one or more public platform to identify the effectiveness of the entity. Based on the weight and rank of the data determined through a metadata analytics, the connecting module (107) connects the classified data. The correlation module (108) correlates the extracted data with the historic patterns and trends of the conforming data to stipulate the outcome signifying the potential impact of the trends and patterns of the entity. The compilation module (108) compiles the ranking, weight and outcome of one or more entity identified by the correlation module (108) and applies the proprietary predictive models to determine the potential market movement of one or more entity from the correlated data. Further, the creation module (110) extracts data from the compilation module (109) to collate and present a detailed summary in a unique platform of the data extracted from the foregoing analysis pertaining to the stocks and insights of the entity to actuate the informed decisions on investments and financial affairs. The display unit (111) displays the recommendation to multiple users based on the detailed summary extracted from the creation module (110) to derive actionable insights and take informed decisions in the domain related to investments and financial affairs.
[0028] Thus, the present invention relates to an intelligent system (100) for facilitating enhanced decision making in multiple domain pertinent to the interest of the user. The intelligent system (100) employs tool for providing research insights pertaining to the entity corresponding to the domain pertinent to the interest of the user by integrating multiple data extracted from a digital platform with quantitative, statistical and technical analysis and to assist the user by providing relevant analysis in one or more fields involving building strategies and to predict the outcome on critical subjects matter. The present invention possesses self-learning capabilities which learns and analyses from the past outcome to provide an optimized real-time data of the ongoing trends.
[0029] Further, the present invention is dynamic and has a self-learning capability to adapt to modern trends which eliminates the need to create a pre-defined set of rules to provide an output thereby eliminating any human error caused from human bias. The intelligent system (100) integrates alternate data with quantitative analysis to provide a meticulous insight in the domain relevant to the interest of the user thereby providing an accurate, swift and adaptable output which assists the user in making a subtle decision.
[0030] Thus, the present invention provides utility in commercial affairs, portfolio managers, sovereign wealth funds, financial sector and to aid in investment decisions by providing an in-depth analysis and prudent prediction to assist in conclusive decision making. The present invention establishes a deep analysis price behavior of each asset which determines the product price on the basis of product engagement with potential customer and assists the user to buy, hold or sell indicator per asset. The intelligent system (100) dynamically characterizes weight of the assets based on market behavior in order to minimize anticipated capital loss risk and meet the anticipated return objectives.
[0031] The features and advantages of the invention are apparent from the detailed specification, and thus, it is intended by the appended claims to cover all such features and advantages of the invention which fall within the true spirit and scope of the invention. Further, since numerous modifications and variations will readily occur to those skilled in the art, it is not desired to limit the invention to the exact construction and operation illustrated and described, and accordingly, all suitable modifications and equivalents may be resorted to, falling within the scope of the invention.
Reference numbers:
Components Reference Numbers
Memory Unit 101
Graphic Processing Unit 102
Collection Module 103
Curation Module 104
Contextual Module 105
Classification Module 106
Connecting Module 107
Correlation Module 108
Compilation Module 109
Creation Module 110
Display Unit 111