MACHINE LEARNING COLLABORATION SYSTEM AND METHOD

• US 20180109574A1
• Filed: 12/15/2017
• Published: 04/19/2018
• Est. Priority Date: 03/05/2015
• Status: Abandoned Application

• Current Assignee: Gamalon, Inc.
• Original Assignee: Gamalon, Inc.
• Inventors: Vigoda, Benjamin W., Neely, Jacob E., Barr, Matthew C., Ring, Daniel F., Freer, Cameron E.
• Application Number:
  US15/843,904
• Publication Number:
  US 20180109574A1
• Time in Patent Office: Days
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/101 Collaborative creation, e.g. joint development of products or services
• CPC Class Codes: H04L 65/4015 where at least one of the additional parallel sessions is real time or time sensitive, e.g. white board sharing, collaboration or spawning of a subconference

Abstract

A method, computer program product, and computer system for acquiring data representing a plurality of collaboration items, each collaboration item being associated with one of a communication and a collaboration among a subset of one or more users. Using a machine learning procedure, one of at least one latent variable and at least one action variable in a model of the data representing the plurality of collaboration items may be determined. At least one of a representation of the collaboration items may be presented to one or more users based upon, at least in part, the at least one latent variable, and potential collaboration actions may be presented to the one or more users based upon, at least in part, the at least one action variable.

Claims

1. A computer-implemented method comprising:

acquiring, by a computing device, data representing a plurality of collaboration items, each collaboration item being associated with one of a communication and a collaboration among a subset of one or more users;

determining, using a machine learning procedure, one of at least one latent variable and at least one action variable in a model of the data representing the plurality of collaboration items; and

at least one of,

presenting a representation of the collaboration items to one or more users based upon, at least in part, the at least one latent variable, and

presenting potential collaboration actions to the one or more users based upon, at least in part, the at least one action variable.

2. The computer-implemented method of claim 1 wherein the model of the data is a model of at least one of human collaboration and relationships.

3. The computer-implemented method of claim 1 wherein one or more of the at least one action variable the at least one latent variable in the model of the data includes information identifying at least one of what task users of the one or more users are working on, what users of the one or more users are working on the task together, when the task is being worked on, why the task is being worked on, and how the one or more users participate in the collaboration.

4. The computer-implemented method of claim 3 wherein why the task is being worked on includes a relationship of the project being worked on relative to one or more other projects within the collaboration.

5. The computer-implemented method of claim 1 wherein the machine learning procedure infers one or more of the at least one action variable the at least one latent variable about at least one of what task users of the one or more users are working on, what users of the one or more users are working on the task together, when the task is being worked on, why the task is being worked on, and how the one or more users participate in the collaboration.

6. The computer-implemented method of claim 5 wherein the machine learning process includes a second probabilistic model generated by modifying a first probabilistic model, the modification based upon, at least in part, inferences of one or more of the at least one action variable the at least one latent variable.

7. The computer-implemented method of claim 1 wherein one or more of the at least one latent variable and the at least one action variable determined using the machine learning procedure is based upon, at least in part, user feedback received from the one or more users.

8. A computer program product residing on a computer readable storage medium having a plurality of instructions stored thereon which, when executed across one or more processors, causes at least a portion of the one or more processors to perform operations comprising:

acquiring data representing a plurality of collaboration items, each collaboration item being associated with one of a communication and a collaboration among a subset of one or more users;

determining, using a machine learning procedure, one of at least one latent variable and at least one action variable in a model of the data representing the plurality of collaboration items; and

at least one of,

presenting a representation of the collaboration items to one or more users based upon, at least in part, the at least one latent variable, and

presenting potential collaboration actions to the one or more users based upon, at least in part, the at least one action variable.

9. The computer program product of claim 8 wherein the model of the data is a model of at least one of human collaboration and relationships.

10. The computer program product of claim 8 wherein one or more of the at least one action variable the at least one latent variable in the model of the data includes information identifying at least one of what task users of the one or more users are working on, what users of the one or more users are working on the task together, when the task is being worked on, why the task is being worked on, and how the one or more users participate in the collaboration.

11. The computer program product of claim 10 wherein why the task is being worked on includes a relationship of the project being worked on relative to one or more other projects within the collaboration.

12. The computer program product of claim 8 wherein the machine learning procedure infers one or more of the at least one action variable the at least one latent variable about at least one of what task users of the one or more users are working on, what users of the one or more users are working on the task together, when the task is being worked on, why the task is being worked on, and how the one or more users participate in the collaboration.

13. The computer program product of claim 12 wherein the machine learning process includes a second probabilistic model generated by modifying a first probabilistic model, the modification based upon, at least in part, inferences of one or more of the at least one action variable the at least one latent variable.

14. The computer program product of claim 8 wherein one or more of the at least one latent variable and the at least one action variable determined using the machine learning procedure is based upon, at least in part, user feedback received from the one or more users.

15. A computing system including one or more processors and one or more memories configured to perform operations comprising:

acquiring data representing a plurality of collaboration items, each collaboration item being associated with one of a communication and a collaboration among a subset of one or more users;

determining, using a machine learning procedure, one of at least one latent variable and at least one action variable in a model of the data representing the plurality of collaboration items; and

at least one of,

presenting a representation of the collaboration items to one or more users based upon, at least in part, the at least one latent variable, and

presenting potential collaboration actions to the one or more users based upon, at least in part, the at least one action variable.

16. The computing system of claim 15 wherein the model of the data is a model of at least one of human collaboration and relationships.

17. The computing system of claim 15 wherein one or more of the at least one action variable the at least one latent variable in the model of the data includes information identifying at least one of what task users of the one or more users are working on, what users of the one or more users are working on the task together, when the task is being worked on, why the task is being worked on, and how the one or more users participate in the collaboration.

18. The computing system of claim 17 wherein why the task is being worked on includes a relationship of the project being worked on relative to one or more other projects within the collaboration.

19. The computing system of claim 15 wherein the machine learning procedure infers one or more of the at least one action variable the at least one latent variable about at least one of what task users of the one or more users are working on, what users of the one or more users are working on the task together, when the task is being worked on, why the task is being worked on, and how the one or more users participate in the collaboration.

20. The computing system of claim 19 wherein the machine learning process includes a second probabilistic model generated by modifying a first probabilistic model, the modification based upon, at least in part, inferences of one or more of the at least one action variable the at least one latent variable.

21. The computing system of claim 15 wherein one or more of the at least one latent variable and the at least one action variable determined using the machine learning procedure is based upon, at least in part, user feedback received from the one or more users.