DESC:BACKGROUND
Technical Field
[0001] The embodiments herein generally relate to social networking, and more specifically to a system and method for matching two or more people based on commonalities.
Description of the related art
[0002] Understanding commonalities between two people is significant for many reasons, contributing to developing and maintaining meaningful connections. Identifying commonalities helps in building rapport and establishing a successful connection. It provides a foundation for conversations and increases the probability of success of the reason for reach-out of one person to the other. Further, recognizing commonalities between two individuals fosters a deeper understanding and appreciation for diverse perspectives contributing to smoother communication and collaboration.
[0003] Existing systems are designed to connect individuals offer a cold reach out resulting in connections that are not relevant to the preferences or needs of a user. Some existing systems recommend people for connection to the user based on shared experiences, mutual respect, open communication, and common connections. However, these recommendations do not consider, the reason for reach-out resulting in a lack of engagement between individuals. The existing systems provide a degree of connection between two people which merely provides the number of hops to reach that person. This does not depend on common interests, backgrounds, or characteristics and hence does not truly represent the strength of the relationship. The existing systems do not focus on improving the odds of connection success. When a user wants to connect with an individual for a specific purpose, it is important to know and use any commonalities between them. These could be social or professional commonalities. It is also important to check the strength of the commonality. The odds of connection success depend on the commonality and the strength of the commonality.
[0004] Therefore, there arises a need to address the aforementioned technical drawbacks in existing technologies.
SUMMARY OF THE INVENTION
[0005] According to the first aspect of the invention, a system for determining one or more attributes that are common between two or more people based on data associated with the people to determine a strength of relationship between the two or more people is provided. The system includes a server that is configured to provide a user interface on a first user device associated with a first user to receive input from the first user. The input from the first user includes personally identifiable information of a second user. The server is configured to generate a query for obtaining data associated with the second user from a database by converting the input from the first user. The data associated with the second user includes personally identifiable information, demographic data, education data, employment data, groups, personality details, and/or interaction data. The server is configured to update a people graph with the data associated with the second user. The people graph represents relationships between the first user and the second user. The people graph includes a plurality of nodes. Each node represents a person or a data field. The person node stores the personally identifiable information of the first user or the second user. The data field node represents an entity and stores details about the entity associated with the first user or the second user. The entity includes educational institutes, workplaces, and personality traits. The server is configured to identify one or more attributes that are common between the first user and the second user from the people graph based on the input and the data associated with the second user. The people graph establishes connections between the person nodes based on identified common attributes. The server is configured to estimate a strength of relationship between the first user and the second user by implementing a sieve filtering mechanism that determines the depth and quality of the relationship by analyzing the one or more common attributes including communication patterns, shared interests, collaboration frequency, temporal factors, feedback and ratings, mutual connections, and user engagement metrics. The sieve filtering mechanism indicates the strength of the relationship by sifting through various data points and interactions. The relationships retained at the top of the sieve filter indicate extremely strong relationships, and relationships moving down indicate decreasing strength.
[0006] In some embodiments, the server is configured to learn from interactions of populations of users over time to identify patterns. The identified patterns are stored in the database.
[0007] In some embodiments, the identified patterns including continuous positive interaction patterns between the first user and the second user are flagged into a strength of relationship override store.
[0008] In some embodiments, the identified patterns existing in the override store are provided with an extremely strong relationship tag.
[0009] In some embodiments, the server is configured to obtain data from various data sources through data source APIs if the data of the first user and the second user is not available in the database.
[0010] In some embodiments, the various data sources include LinkedIn, social media platforms, webpages, public databases, professional networks, email interactions, chat logs, online forums, and other digital communication channels.
[0011] In some embodiments, the server is configured to update the people graph in a pre-defined frequency if the data of the first user and the second user is already available in the database.
[0012] In some embodiments, the people graph includes a link with associated properties between the person node and data field node. The associated properties include details of the data field associated with the first user or the second user.
[0013] In some embodiments, the server is configured to query the database to identify relationships between person nodes based on shared data field nodes and the associated properties if no commonality exists between the person nodes.
[0014] In some embodiments, the details about the educational institutes includes name of the educational institution, location (city, state, country), degree obtained, field of study, dates of attendance, activities and societies. The details about the work places include name of the company, job title, department, location (city, state, country), dates of employment, key responsibilities and achievements. The details about the personality traits includes personality, interests, hobbies, values and beliefs.
[0015] In some embodiments, the server is configured to (i) present the interface on a second user device associated with the second user to receive the input from the second user, the input from the second user includes personally identifiable information of the first user, (ii) generate a query for obtaining data associated with the first user by converting the input from the second user, the data includes the personally identifiable information, the demographic data, education data, the employment data, the groups, the personality details, and the interaction data and (iv) update the people graph with the data associated with the second user.
[0016] According to the second aspect of the invention, a method for determining one or more attributes that are common between two or more people based on data associated with the people to determine a strength of relationship between the two or more people is provided. The method includes providing a user interface on a first user device associated with a first user to receive input from the first user. The input from the first user includes personally identifiable information of a second user. The method includes generating a query for obtaining data associated with the second user from a database by converting the input from the first user. The data associated with the second user includes personally identifiable information, demographic data, education data, employment data, groups, personality details, and/or interaction data. The method includes updating a people graph with the data associated with the second user. The people graph represents relationships between the first user and the second user. The people graph includes a plurality of nodes, each node represents a person or a data field. The person node stores the personally identifiable information of the first user or the second user and the data field node represents an entity and stores details about the entity associated with the first user or the second user. The entity includes educational institutes, workplaces, and personality traits. The method includes identifying one or more attributes that are common between the first user and the second user from the people graph based on the input and the data associated with the second user. The people graph establishes connections between the person nodes based on identified common attributes. The method includes estimating a strength of relationship between the first user and the second user by implementing a sieve filtering mechanism that determines the depth and quality of the relationship by analyzing the one or more common attributes comprising communication patterns, shared interests, collaboration frequency, temporal factors, feedback and ratings, mutual connections, and user engagement metrics, wherein the sieve filtering mechanism indicates the strength of the relationship by sifting through various data points and interactions. The relationships retained at the top of the sieve filter indicate extremely strong relationships, and relationships moving down indicate decreasing strength.
[0017] In some embodiments, the method includes learning from interactions of large populations of users over time to identify patterns. The identified patterns are stored in the database.
[0018] In some embodiments, the identified patterns including continuous positive interaction patterns between the first user and the second user are flagged into a strength of relationship override store.
[0019] In some embodiments, the identified patterns existing in the override store are provided with an extremely strong relationship tag.
[0020] In some embodiments, the method includes obtaining data from various data sources through data source APIs if the data of the first user and the second user is not available in the database.
[0021] In some embodiments, the various data sources include LinkedIn, social media platforms, webpages, public databases, professional networks, email interactions, chat logs, online forums, and other digital communication channels.
[0022] In some embodiments, the method includes updating the people graph in a pre-defined frequency if the data of the first user and the second user is already available in the database.
[0023] In some embodiments, the people graph includes a link with associated properties between the person node and data field node. The associated properties comprise details of the data field associated with the first user or the second user.
[0024] In some embodiments, the method includes querying the database to identify relationships between person nodes based on shared data field nodes and the associated properties if no commonality exists between the person nodes.
[0025] In some embodiments, the details about the educational institutes include name of the educational institution, location (city, state, country), degree obtained, field of study, dates of attendance, activities and societies. The details about the work places include name of the company, job title, department, location (city, state, country), dates of employment, key responsibilities and achievements. The details about the personality traits include personality, interests, hobbies, values and beliefs.
[0026] In some embodiments, the method includes (i) presenting the interface on the second user device to receive the input from the second user, the input from the second user includes personally identifiable information of the first user, (ii) converting the input from the second user into the query for obtaining data associated with the first user from the database, the data includes personally identifiable information, demographic data, education data, employment data, groups, personality details, and interaction data, and (iv) updating the people graph with the data associated with the second user.
BRIEF DESCRIPTION OF THE DRAWINGS
[0027] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
[0028] FIG. 1 illustrates a system for determining one or more attributes that are common between two or more people based on data associated with the people to determine a strength of relationship between the two or more people according to some embodiments herein;
[0029] FIG. 2 is a block diagram of the server of FIG. 1 according to some embodiments herein;
[0030] FIG. 3A-B are flow diagrams that illustrate a method for determining one or more attributes that are common between two or more people based on data associated with the people to determine a strength of relationship between the two or more people according to some embodiments herein; and
[0031] FIG. 4 is a schematic diagram of a computer architecture in accordance with the embodiments herein.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0032] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0033] As mentioned, there remains a need to address the aforementioned technical drawbacks in existing technologies in providing a system for determining one or more attributes that are common between two or more people based on data associated with the people to determine a strength of relationship between the two or more people. Referring now to the drawings, and more particularly to FIGS. 1 through 4, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments.
[0034] FIG. 1 illustrates a system for determining one or more attributes that are common between two or more people based on data associated with the people to determine a strength of relationship between the two or more people according to some embodiments herein. The system 100 includes a server 102, a first user 104, a second user 106, a first user device 108, a second user device 110, and a network 112. The server 102 may be a cloud server. The server 102 is configured to present an interface on the first user device 108 to receive input from the first user 104. The input includes personally identifiable information of the second user 106. The input may include a LinkedIn URL of the second user 106. The server 102 is configured to determine the one or more attributes between the first user 104 and the second user 106 from a people graph based on the input if the first user 104 and a second user 106 are already part of the people graph. The people graph is a representation of relationships among the first user 104 and the second user 106. The people graph is a graphical depiction wherein each user is a node, and connections between the first user 104 and the second user 106 are represented as edges.
[0035] The server 102 is configured to store the input from the first user 104 and the obtained one or more attributes in a database 114. The server 102 is configured to obtain the input from the first user device 108 and generate a query based on the input for obtaining data associated with the second user from the database 114. The data includes, but not restricted to, personally identifiable information data including name, email, and phone number, demographic data including location, education data including school name, school details, tenure, courses, and affiliations, employment data including employer name, firmographics, tenure, role, designation, function and industry, groups including volunteer groups, support groups, interest groups, professional groups and memberships, personality details including empathy map, behavioral traits and likes/dislikes, interaction data including social networks, digital interactions, email, chats, messages. The social networks include Instagram, Facebook, LinkedIn, Twitter, Snapchat, and Reddit. The interaction data includes physical interactions like meetings, events, and clubs.
[0036] The server 102 is configured to update the people graph with the data associated with the second user 106 obtained from the database 114. The people graph is constantly updated based on the input received from the first user 104 or the second user 106. If data of the first user 104 and the second user 106 is not available in the database 114, the input from the first user 104 is passed as a command to a data source API to obtain the data of the second user 106 from various data sources. If data of the first user 104 and the second user 106 is already available in the database 114, then the people graph is updated with the data of the first user 104 and the second user 106 in a pre-defined frequency. The server 102 is configured to estimate the strength of the relationship between the first user 104 and the second user 106 based on a “Relationship strength logic”. The server 102 may be configured to present an interface on the second user device 110 to receive the input from the second user 106 for obtaining the one or more attributes between the first user 104 and the second user 106. The input includes personally identifiable information, a LinkedIn URL of the first user 104. The input from the second user 106 is passed as a command to the data source API to obtain the data of the first user 104 from various data sources.
[0037] The people graph has two types of nodes including person and data field. The person node, for example, has personal identification information details. The data field node includes, for example, educational institutes, workplaces, and personality traits details. Links between the person and the data field nodes include properties. people nodes are linked in the people graph based on the “Relationship strength logic”. If one or more attributes that are common are identified, the link includes those commonalities. If the one or more attributes that are common are NULL, then the people graph includes NULL in the link. The server 102 is configured to establish the strength of a relationship between the first user 104 and the second user 106 using a sieve filtering mechanism. The sieve filtering mechanism operates akin to a metaphorical sieve, sifting through various data points and interactions between the first user 104 and the second user 106. The sieve filtering mechanism includes multifaceted analysis, including communication patterns, shared interests, collaboration frequency, temporal factors, feedback and ratings, mutual connections, and user engagement metrics. The sieve filtering mechanism determines the depth and quality of the relationship. For example, it considers how often the first user 104 and the second user 106 communicate, the nature of their shared interests, the effectiveness of their collaboration, the duration of their relationship, feedback received, mutual connections, and the level of engagement with the provided services.
[0038] If a relationship is retained at the top of the sieve filter, it indicates an extremely strong strength of a relationship. If a relationship is moving down from the top of the sieve filter, it indicates that the strength of the relationship decreases. For example, positive interaction over the last three months, positive interaction over the last 3 to 12 months, and education overlap including school, course, and year of study are indicated as extremely strong relations in the sieve filter. Education overlap including school and year of study, education overlap including school, course, and share years in school, education overlap including school and share years in school, work overlap including small company (0-200 people), and overlapping years are indicated as very strong relations in the sieve filter. Work overlap including medium-sized companies (200-1000 people), overlapping years and geo, educational overlap including alumnus within 5 years, educational overlap including alumnus within 5 to 10 years are indicated as strong relations in the sieve filter. Education overlap including alumnus anytime, work overlap including small company (0-200 people, alumnus), work overlap including medium-sized company (200-1000 people) and overlapping years, work overlap including medium-sized company (200-1000 people) and alumnus, work overlap including large company (greater than 1000 people, overlapping years and same geo and random direct connection on social platforms are indicated as weak relations in the sieve filter. Random interactions on the social platform are indicated as very weak relations in the sieve filter. The server 102 learns from the interactions of large populations of the first user 104 and the second user 106 over time. The server 102 understands that being alumnus of certain schools or certain workplaces collectively known as institutions has a higher chance of getting warm connect responses than other similar institutions and stores these patterns in the database 114. If a continuous positive pattern is noticed among many such users, the pattern is flagged into a strength of relationship override store. Any pattern that exists in this override store is given an extremely strong relationship tag.
[0039] FIG. 2 is a block diagram of the server 102 of FIG. 1 according to some embodiments herein. The server 102 includes the database 114, an input receiving module 202, a second user data obtaining module 204, a people graph updation module 206, a people graph analysing module 208, an attribute identification module 210, and a data source API management module 212. The input receiving module 202 is configured to receive input from the first user 104 by presenting an interface on the first user device 108. The second user data obtaining module 204 is configured to obtain data associated with the second user from the database 114 by generating a query based on the input from the first user 104. The people graph updation module 206 is configured to update the people graph with the data associated with the second user obtained from the database 114. The people graph analysing module 208 is configured to analyse the people graph based on the input from the first user 104. The attribute identification module 210 is configured to obtain one or more attributes that are common between the first user 104 and the second user 106 based on the analysis of the people graph. The data source API management module 212 is configured to pass the input from the first user 104 as a command to a data source API to obtain the data of the first user 104 and the second user 106 or both from various data sources and store in the database 114.
[0040] FIG. 3A-B are flow diagrams that illustrate a method for determining one or more attributes that are common between two or more people based on data associated with the people to determine a strength of relationship between the two or more people according to some embodiments herein. At step 302, the method includes providing an interface on a first user device to receive input from a first user by a server. The input from the first user includes personally identifiable information of a second user. At step 304, the method includes generating a query for obtaining data associated with the second user from a database by converting the input from the first user. The data associated with the second user comprises personally identifiable information, demographic data, education data, employment data, groups, personality details, and/or interaction data. At step 306, the method includes updating a people graph with the data associated with the second user by the server. The people graph represents relationships between the first user and the second user. The people graph includes a plurality of nodes. Each node represents a person or a data field. The person node stores the personally identifiable information of the first user or the second user. The data field represents an entity and store specific details about the entity associated with the first user or the second user. The entity includes educational institutes, workplaces, and personality traits. At step 308, the method includes identifying one or more attributes that are common between the first user and the second user from the people graph based on the input and the data associated with the second user. The people graph establishes connections between the person nodes based on identified common attributes. The people graph establishes connections between person nodes based on identified common attributes. At step 310, the method includes estimating a strength of relationship between the first user and the second user by implementing a sieve filtering mechanism that determines the depth and quality of the relationship by analyzing the one or more common attributes comprising communication patterns, shared interests, collaboration frequency, temporal factors, feedback and ratings, mutual connections, and user engagement metrics. The sieve filtering mechanism indicates the strength of the relationship by sifting through various data points and interactions. The relationships retained at the top of the sieve filter indicate extremely strong relationships, and relationships moving down indicate decreasing strength.
[0041] A representative hardware environment for practicing the embodiments herein is depicted in FIG. 4, with reference to FIGS. 1 through 3. This schematic drawing illustrates a hardware configuration of a server 102 /computer system in accordance with the embodiments herein. The server 102 /computer includes at least one processing device 10 and a cryptographic processor 11. The special-purpose CPU 10 and the cryptographic processor (CP) 11 may be interconnected via system bus 14 to various devices such as a random access memory (RAM) 15, read-only memory (ROM) 16, and an input/output (I/O) adapter 17. The I/O adapter 17 can connect to peripheral devices, such as disk units 12 and tape drives 13, or other program storage devices that are readable by the system. The server 114 / computer can read the inventive instructions on the program storage devices and follow these instructions to execute the methodology of the embodiments herein. The server 102/computer system further includes a user interface adapter 20 that connects a keyboard 18, mouse 19, speaker 25, microphone 23, and/or other user interface devices such as a touch screen device (not shown) to the bus 14 to gather user input. Additionally, a communication adapter 21 connects the bus 14 to a data processing network 26, and a display adapter 22 connects the bus 14 to a display device 24, which provides a graphical user interface (GUI) 30 of the output data in accordance with the embodiments herein, or which may be embodied as an output device such as a monitor, printer, or transmitter, for example. Further, a transceiver 27, a signal comparator 28, and a signal converter 29 may be connected with the bus 14 for processing, transmission, receipt, comparison, and conversion of electric or electronic signals.
[0042] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the scope of the appended claims.

,CLAIMS:I/We claim:
1. A system for determining one or more attributes that are common between two or more people based on data associated with the people to determine a strength of relationship between the two or more people, comprising:
a server (102) comprising, a memory that comprises a set of instructions, a processor that is configured to retrieve and execute the set of instructions from the memory and is configured to;
provide a user interface on a first user (104) device associated with a first user (104) to receive input from the first user (104), wherein the input from the first user (104) comprises personally identifiable information of a second user (106);
generate a query for obtaining data associated with the second user (106) from a database (114) by converting the input from the first user (104), wherein the data associated with the second user (106) comprises personally identifiable information, demographic data, education data, employment data, groups, personality details, and/or interaction data;
update a people graph with the data associated with the second user (106), wherein the people graph represents relationships between the first user (104) and the second user (106), wherein the people graph comprises a plurality of nodes, wherein each node represents a person or a data field, wherein the person node stores the personally identifiable information of the first user (102) or the second user (106), wherein the data field node represents an entity and stores details about the entity associated with the first user (102) or the second user (106), wherein the entity comprises educational institutes, workplaces, and personality traits;
identify one or more attributes that are common between the first user (102) and the second user (106) from the people graph based on the input and the data associated with the second user (106), wherein the people graph establishes connections between the person nodes based on identified common attributes; and
estimate a strength of relationship between the first user (102) and the second user (106) by implementing a sieve filtering mechanism that determines the depth and quality of the relationship by analyzing the one or more common attributes comprising communication patterns, shared interests, collaboration frequency, temporal factors, feedback and ratings, mutual connections, and user engagement metrics, wherein the sieve filtering mechanism indicates the strength of the relationship by sifting through various data points and interactions, wherein the relationships retained at the top of the sieve filter indicate extremely strong relationships, and relationships moving down indicate decreasing strength.

2. The system as claimed in claim 1, wherein the server (102) is configured to learn from interactions of populations of users over time to identify patterns, wherein the identified patterns are stored in the database (114).

3. The system as claimed in claim 2, wherein the identified patterns comprising continuous positive interaction patterns between the first user (104) and the second user (106) are flagged into a strength of relationship override store.

4. The system as claimed in claim 3, wherein the identified patterns existing in the override store are provided with an extremely strong relationship tag.

5. The system of claim 1, wherein the server (102) is configured to obtain data from various data sources through data source APIs if the data of the first user (104) and the second user (106) is not available in the database (114).

6. The system as claimed in claim 5, wherein the various data sources include LinkedIn, social media platforms, webpages, public databases, professional networks, email interactions, chat logs, online forums, and other digital communication channels.

7. The system as claimed in claim 1, wherein the server (102) is configured to update the people graph in a pre-defined frequency if the data of the first user (104) and the second user (106) is already available in the database (114).

8. The system as claimed in claim 1, wherein the people graph comprises a link with associated properties between the person node and data field node, wherein the associated properties comprise details of the data field associated with the first user (104) or the second user (106).

9. The system as claimed in claim 8, wherein the server (102) is configured to query the database (114) to identify relationships between person nodes based on shared data field nodes and the associated properties if no commonality exists between the person nodes.

10. The system as claimed in claim 1, wherein the details about the educational institutes comprise name of the educational institution, location (city, state, country), degree obtained, field of study, dates of attendance, activities and societies, wherein the details about the work places comprise name of the company, job title, department, location (city, state, country), dates of employment, key responsibilities and achievements, wherein the details about the personality traits comprise personality, interests, hobbies, values and beliefs.

11. The system as claimed in claim 1, wherein the server (102) is configured to (i) present the interface on a second user device (110) associated with the second user (106) to receive the input from the second user (106), wherein the input from the second user (106) comprises personally identifiable information of the first user (104), (ii) generate a query for obtaining data associated with the first user (104) by converting the input from the second user (106), wherein the data comprises the personally identifiable information, the demographic data, education data, the employment data, the groups, the personality details, and the interaction data and (iv) update the people graph with the data associated with the second user (106).

12. A method for determining one or more attributes that are common between two or more people based on data associated with the people to determine a strength of relationship between the two or more people, wherein the method comprises,
providing a user interface on a first user device (108) associated with a first user (104) to receive input from the first user (104), wherein the input from the first user (104) comprises personally identifiable information of a second user (106);
generating a query for obtaining data associated with the second user (106) from a database (114) by converting the input from the first user (104), wherein the data associated with the second user (106) comprises personally identifiable information, demographic data, education data, employment data, groups, personality details, and/or interaction data;
updating a people graph with the data associated with the second user (106), wherein the people graph represents relationships between the first user (104) and the second user (106), wherein the people graph comprises a plurality of nodes, wherein each node represents a person or a data field, wherein the person node stores the personally identifiable information of the first user (104) or the second user (106), wherein the data field node represents an entity and stores details about the entity associated with the first user (104) or the second user (106), wherein the entity comprises educational institutes, workplaces, and personality traits;
identifying one or more attributes that are common between the first user (104) and the second user(106) from the people graph based on the input and the data associated with the second user (106), wherein the people graph establishes connections between the person nodes based on identified common attributes; and
estimating a strength of relationship between the first user (104) and the second user (106) by implementing a sieve filtering mechanism that determines the depth and quality of the relationship by analyzing the one or more common attributes comprising communication patterns, shared interests, collaboration frequency, temporal factors, feedback and ratings, mutual connections, and user engagement metrics, wherein the sieve filtering mechanism indicates the strength of the relationship by sifting through various data points and interactions, wherein the relationships retained at the top of the sieve filter indicate extremely strong relationships, and relationships moving down indicate decreasing strength.

13. The method as claimed in claim 12, wherein the method comprises learning from interactions of large populations of users over time to identify patterns, wherein the identified patterns are stored in the database (114).

14.The method as claimed in claim 13, wherein the identified patterns comprising continuous positive interaction patterns between the first user (104) and the second user (106) are flagged into a strength of relationship override store.

15. The method as claimed in 14, wherein the identified patterns existing in the override store are provided with an extremely strong relationship tag.

16. The method as claimed in claim 12, wherein the method comprises obtaining data from various data sources through data source APIs if the data of the first user (104) and the second user (106) is not available in the database (114).

17. The method as claimed in claim 12, wherein the various data sources comprise LinkedIn, social media platforms, webpages, public databases, professional networks, email interactions, chat logs, online forums, and other digital communication channels.

18. The method as claimed in claim 12, wherein the method comprises updating the people graph in a pre-defined frequency if the data of the first user (104) and the second user (106) is already available in the database (114).

19. The method as claimed in claim 12, wherein the people graph comprises a link with associated properties between the person node and data field node, wherein the associated properties comprise details of the data field associated with the first user (104) or the second user (106).

20. The method as claimed in claim 19, wherein the method comprises querying the database (114) to identify relationships between person nodes based on shared data field nodes and the associated properties if no commonality exists between the person nodes.

21. The method as claimed in claim 12, wherein the details about the educational institutes comprise name of the educational institution, location (city, state, country), degree obtained, field of study, dates of attendance, activities and societies, wherein the details about the work places comprise name of the company, job title, department, location (city, state, country), dates of employment, key responsibilities and achievements, wherein the details about the personality traits comprise personality, interests, hobbies, values and beliefs.

22. The method as claimed in claim 12, wherein the method comprises (i) presenting the interface on the second user device (110) to receive the input from the second user (106), wherein the input from the second user (106) comprises personally identifiable information of the first user (104), (ii) converting the input from the second user (106) into the query for obtaining data associated with the first user (104) from the database (114), wherein the data comprises personally identifiable information, demographic data, education data, employment data, groups, personality details, and interaction data, and (iv) updating the people graph with the data associated with the second user (106).
Aug 14th 2024

Name: Arjun Karthik Bala
Patent Agent Number: IN/PA - 1021