System and method for management of a talent network

Abstract

A system and method for management of a talent network are disclosed. The system includes an expert ranking measurement subsystem configured to compute a quantitative score of an expert for a user query based on a talent metric and a trust metric, a qualitative score measurement subsystem configured to compute a qualitative score of the expert based on a content score and an activity score, wherein the content score is computed based on a content provided by the expert and the activity score is computed based on an activity occurring on a profile of the expert, an overall expert rank calculation subsystem configured to calculate an overall rank of the expert, wherein the overall expert rank is calculated based on the quantitative score, the qualitative score, predefined weightages assigned to the connection relationship, the plurality of professional achievements, the content score, the activity score, and user preferences.

We claim: 
     
         1 . A system for management of a talent network comprising:
 an expert ranking measurement subsystem operable by one or more processors, wherein the expert ranking measurement subsystem comprises:
 a quantitative score measurement subsystem configured to compute a quantitative score of an expert for a user query based on a talent metric and a trust metric, wherein
 the trust metric is computed based on a connection relationship between the expert and a user in the talent network, and 
 the talent metric is computed based on a plurality of professional achievements of the expert in the talent network; 
 
 a qualitative score measurement subsystem configured to compute a qualitative score of the expert based on a content score and an activity score of the expert in the talent network, wherein the content score is computed based on content provided by the expert in the talent network and the activity score is computed based on an activity occurring on a profile of the expert in the talent network; and 
 an overall expert rank calculation subsystem configured to calculate an overall rank of the expert, wherein the overall expert rank is calculated based on the quantitative score, the qualitative score, predefined weightages assigned to the connection relationship, the plurality of professional achievements, the content score, the activity score, and user preferences. 
   
     
     
         2 . The system of  claim 1 , wherein the plurality of professional achievements of the expert comprise a number of recognitions the expert has received, a number of successful tasks, a number of goals the expert has achieved, or a combination thereof. 
     
     
         3 . The system of  claim 1 , wherein the connection relationship comprises a degree of connection of the expert with the user. 
     
     
         4 . The system of  claim 1 , wherein the quantitative score is calculated as a product of the talent metric and the trust metric. 
     
     
         5 . The system of  claim 1 , wherein the content comprises at least two of a profile of the expert in the talent network, a plurality of images uploaded by the expert, a plurality of videos uploaded by the expert, one or more confirmed contracts obtained by the expert, one or more certificates uploaded by the expert, or a combination thereof. 
     
     
         6 . The system of  claim 1 , wherein the activity comprises at least two of a number of likes received by the expert, a number of views received by the expert, a number of shares received by the expert, or a combination thereof. 
     
     
         7 . The system of  claim 1 , wherein the predefined weightages are assigned in real time for each of the user query based on trained machine learning models. 
     
     
         8 . The system of  claim 1 , wherein the overall expert rank is generated based on a cumulative sum of the quantitative score and the qualitative score. 
     
     
         9 . The system of  claim 1 , wherein the user preferences comprises one or more details associated with the task to be performed by the expert and one or more rules for delivery of the one or more tasks defined by the user associated with the task. 
     
     
         10 . The system of  claim 1 , further comprising a skill extraction subsystem configured to extract one or more skill sets from the one or more tasks completed by the one or more experts using one or more natural language processing techniques and one or more artificial intelligence techniques. 
     
     
         11 . The system of  claim 1 , wherein the talent network comprises a blockchain-based contract management subsystem for execution and completion of a contract between the user and the expert. 
     
     
         12 . A method for management of a talent network, the method comprising:
 computing, by a quantitative score measurement subsystem, a quantitative score of an expert for a user query based on a talent metric and a trust metric, wherein the trust metric is computed based on a connection relationship between the expert and a user in the talent network, and the talent metric is computed based on a plurality of professional achievements of the expert in the talent network;   computing, by a qualitative score measurement subsystem, a qualitative score of the expert based on a content score and an activity score of the expert in the talent network, wherein the content score is computed based on a content provided by the expert in the talent network, and the activity score is computed based on an activity occurring on a profile of the expert in the talent network; and   calculating, an overall expert rank calculation subsystem, an overall rank of the expert, wherein the overall expert rank is calculated based on the quantitative score, the qualitative score, predefined weightages assigned to the connection relationship, the plurality of professional achievements, the content score, the activity score, and user preferences.   
     
     
         13 . The method of  claim 12 , wherein the plurality of professional achievements of the expert comprising a number of recognitions the expert has received, a number of successful tasks, a number of goals the expert has achieved, or a combination thereof. 
     
     
         14 . The method of  claim 13 , wherein the recognitions are received by the expert from the users within a first degree of connection of the expert. 
     
     
         15 . The method of  claim 12 , wherein the connection relationship comprising a degree of connection of the expert with the user. 
     
     
         16 . The method of  claim 12 , wherein the quantitative score is calculated as a product of the talent metric and the trust metric. 
     
     
         17 . The method of  claim 12 , wherein the content comprising at least two of a profile of the expert in the talent network, a plurality of images uploaded by the expert, a plurality of videos uploaded by the expert, one or more confirmed contracts obtained by the expert, one or more certificates uploaded by the expert, or a combination thereof. 
     
     
         18 . The method of  claim 12 , wherein the activity comprising at least two of a number of likes received by the expert, a number of views received by the expert, a number of shares received by the expert, or a combination thereof. 
     
     
         19 . The method of  claim 12 , wherein the predefined weightages are assigned in real time for each case based on trained machine learning models. 
     
     
         20 . The method of  claim 12 , wherein the overall expert rank is generated based on a cumulative sum of the quantitative score and the qualitative score. 
     
     
         21 . The method of  claim 12 , wherein the talent network comprises a blockchain-based contract management subsystem for execution and completion of a contract between the user and the expert. 
     
     
         22 . A method for management of a talent network comprising:
 creating a plurality of micro networks corresponding to each of a plurality of users in the talent network, wherein each micro network is owned by a corresponding micro network owner, and wherein each micro network comprises one or more users having a first degree of connection with the micro network owner in the talent network;   onboarding the one or more users within micro network of the micro network owner, as an expert by nominating the one or more users as an expert by providing a first time recognition to the one or more users for one or more skills;   creating a trusted expert relationship between the expert and the one or more users within the micro network of the micro network owner providing the first time recognition; and   displaying the expert to one or more users based on the trusted expert relationship created between the expert and the one or more users upon receiving a search query for an expert in one or more skills from the one or more users.   
     
     
         23 . The method of  claim 22 , wherein onboarding the one or more users within the micro network as the expert comprises onboarding an existing user of the talent network as the expert or a person unlisted in the talent network as the expert. 
     
     
         24 . The method of  claim 22 , further comprising receiving one or more recognitions associated with the expert for the one or more skills after receiving the first time recognition from one or more users in the micro network. 
     
     
         25 . The method of  claim 24 , further comprising creating the trusted expert relationship between the expert and the one or more users within the one or more micro networks of the corresponding one or more users providing recognitions to the expert after the first time recognition. 
     
     
         26 . The method of  claim 22 , further comprising adding one or more users in the micro network of the micro network owner using a gesture executed via a device.