Measuring probability of influence using multi-dimensional statistics on deep learning embeddings

1 . A system for measuring probability of influence in communications, the system comprising:

a processor; and

a memory comprising computer-readable instructions, the memory and the computer-readable instructions configured to cause the processor to:

calculate a first probability that a first content associated with a first communication generated by a first user is derived from historic knowledge of the first user using semantic embeddings of prior communications associated with the historic knowledge of the first user, the semantic embeddings generated by a natural language processing model;

calculate a second probability that the first content associated with the first communication generated by the first user is derived in part from a second content of a second communication generated by a second user, the second communication generated prior to the first communication, the second communication occurring within a user-configurable time period prior to the first communication, wherein the first user interacted with the second communication prior to generation of the first communication;

determine whether the second probability is greater than the first probability;

generate an influence attribution recommendation, including an influence attribution of the second user, responsive to a determination that the second probability is greater than the first probability, wherein the influence attribution recommendation is presented to the first user via a user interface;

present the influence attribution recommendation to the first user via a user interface, the influence attribution recommendation comprising the second communication; and

suggest the first user send a message to the second user acknowledging a contribution of the second content to the first content.

2 . The system of claim 1 , wherein the instructions are further operative to:

generate a plurality of semantic embeddings representing a plurality of prior communications generated by the first user, the plurality of prior communications comprising transcripts of meetings attended by the first user, emails generated by the first user, documents authored by the first user and messages written by the first user, wherein the plurality of semantic embeddings are stored in a remote data storage.

3 . The system of claim 1 , wherein the instructions are further operative to:

apply a Kolmogorov-Smirnov statistics test to estimate the probability that the first communication is from a same distribution as the historic knowledge of the first user.

4 . The system of claim 1 , wherein the instructions are further operative to:

apply Kolmogorov-Smirnov statistics test to estimate the probability that the first communication is from a same distribution as the second communication of the second user.

5 . The system of claim 1 , wherein the instructions are further operative to:

represent communications associated with a user as a timeline of semantic embeddings.

6 . The system of claim 1 , wherein the instructions are further operative to:

generate a graph representing propagation of influence associated with a plurality of users.

7 . The system of claim 1 , wherein the instructions are further operative to:

generate an influence attribution report identifying influencers on the first user and influences the first user has had on other users based on influence attribution associated with a plurality of communications generated by a plurality of users.

8 . A method for measuring probability of influence in communications, the method comprising:

obtaining first semantic embeddings representing a new communication generated by a first user at a first time, the first semantic embeddings generated by a natural language processing model;

calculating a first probability that a first portion of content associated with the new communication is derived from historic knowledge of the first user based on analysis of the first semantic embeddings of the new communication and second semantic embeddings representing prior communications of the first user associated with the historic knowledge of the first user, the second semantic embeddings generated by a natural language processing model;

receiving third semantic embeddings representing a second communication generated by a second user at a second time, the second time occurring prior to the first time, wherein the second communication was accessible to the first user prior to generation of the new communication by the first user, the third semantic embeddings generated by the natural language processing model;

calculating a second probability that the content of the new communication is influenced by the second communication, the second communication occurring within a user-configurable time period prior to the new communication;

determining that the second probability is greater than the first probability;

generating an influence attribution recommendation, including an influence attribution of the second user, responsive to determining that the second probability exceeds the first probability;

presenting the influence attribution recommendation to the first user via a user interface, the influence attribution recommendation comprising the second communication; and

suggesting the first user send a message to the second user acknowledging a contribution of the second communication to the new communication.

9 . The method of claim 8 , further comprising:

generating a plurality of semantic embeddings representing a plurality of prior communications generated by the first user, the plurality of prior communications comprising transcripts of meetings attended by the first user, emails generated by the first user, documents authored by the first user and messages written by the first user, wherein the plurality of semantic embeddings are stored in a remote data storage.

10 . The method of claim 8 , further comprising:

using a Kolmogorov-Smirnov statistics test to estimate the probability that the new communication is from a same distribution as the historic knowledge of the first user.

11 . The method of claim 8 , further comprising:

using a Kolmogorov-Smirnov statistics test to estimate the probability that the new communication is from a same distribution as the second communication of the second user.

12 . The method of claim 8 , further comprising:

representing communications associated with a user as a timeline of semantic embeddings.

13 . The method of claim 8 , further comprising:

generating a graph representing propagation of influence associated with a plurality of users.

14 . The method of claim 8 , further comprising:

identifying influencers on the first user and influences the first user has had on other users based on influence attribution associated with a plurality of communications generated by a plurality of users.

15 . One or more computer storage devices having computer-executable instructions stored thereon for measuring probability of influence in communications, which, on execution by a computer, cause the computer to perform operations comprising:

generating first semantic embeddings representing a new communication generated by a first user at a first time, the first semantic embeddings generated by a natural language processing model;

calculating a first probability that a first portion of content associated with the new communication is derived from historic knowledge of the first user using multidimensional statistics on the first semantic embeddings and second semantic embeddings representing prior communications of the first user associated with the historic knowledge of the first user, the second semantic embeddings generated by a natural language processing model;

generating second semantic embeddings representing a second communication generated by a second user at a second time, the second time occurring prior to the first time, wherein the second communication was accessible to the first user prior to generation of the new communication by the first user, the second communication occurring within a user-configurable time period prior to the new communication;

calculating a second probability that the content of the new communication is influenced by the second communication based on the multidimensional statistics on the second semantic embeddings, the second communication occurring within a user-configurable time period prior to the new communication;

generating an influence attribution recommendation, including an influence attribution of the second user, responsive to determining that the second probability exceeds the first probability;

presenting the influence attribution recommendation to the first user via a user interface, the influence attribution recommendation comprising the second communication; and

suggesting the first user send a message to the second user acknowledging a contribution of the second communication to the first portion of content.

16 . The one or more computer storage devices of claim 15 , wherein the operations further comprise:

generating a plurality of semantic embeddings representing a plurality of prior communications generated by the first user, the plurality of prior communications comprising transcripts of meetings attended by the first user, emails generated by the first user, documents authored by the first user and messages written by the first user, wherein the plurality of semantic embeddings are stored in a remote data storage.

17 . The one or more computer storage devices of claim 15 , wherein the operations further comprise:

applying a Kolmogorov-Smirnov statistics test to estimate the probability that the new communication is from a same distribution as the historic knowledge of the first user.

18 . The one or more computer storage devices of claim 15 , wherein the operations further comprise:

applying Kolmogorov-Smirnov statistics test to estimate the probability that the new communication is from a same distribution as the second communication.

19 . The one or more computer storage devices of claim 15 , wherein the operations further comprise:

generating a first timeline of semantic embeddings representing communications associated with the first user; and

generating a second timeline of semantic embeddings representing communications associated with the second user, wherein the first and second timelines of semantic embeddings are utilized to measure probability of influence on communications of the first user and the second user.

20 . The one or more computer storage devices of claim 15 , wherein the operations further comprise:

generate a graph representing propagation of an idea associated with a plurality of users based on influence attribution.