Systems and methods for structuring information in a collaboration environment

1. A computer-implemented machine learning method for improving a collaboration environment, the method comprising:

receiving text data for one or more users of the collaboration environment;

generating a statement by partitioning the text data;

determining an act using the statement using a trained machine learning model;

receiving user feedback from the one or more users;

training the machine learning model using the user feedback to generate a retrained machine learning model;

generating an improved statement using the retrained machine learning model; and

determining an improved act using the retrained machine learning model.

2. The computer-implemented method of claim 1 , wherein determining the act comprises extracting feature data and semantic vector data from the statement, and processing the feature data and the semantic vector data using the trained machine learning model.

3. The computer-implemented method of claim 2 , wherein extracting feature data and semantic vector data from the statement further comprises:

extracting the feature data and the semantic vector data from the statement using the trained machine learning model.

4. The computer-implemented method of claim 1 , further comprising

generating a thread using at least the statement and the act; and

generating an actor list using at least the statement.

5. The computer-implemented method of claim 4 , further comprising:

extracting participant data, named entity data, and conceptual type data from the statement; and

wherein generating the thread comprises generating using the participant data, the named entity data, and the conceptual type data.

6. The computer-implemented method of claim 4 , further comprising linking the thread using an actor from the actor list to generate a linked thread.

7. The computer-implemented method of claim 4 , further comprising generating an actionable item using the actor list and the thread.

8. The computer-implemented method of claim 1 , wherein the statement is associated with a same security settings as the text data.

9. A machine learning system for improving a collaboration environment, the system comprising:

a processor;

a memory operatively connected to the processor and storing instructions that, when executed by the processor, cause:

receiving text data for one or more users of the collaboration environment;

generating a statement by partitioning the text data;

determining an act using the statement using a trained machine learning model;

receiving user feedback from the one or more users;

training the machine learning model using the user feedback to generate a retrained machine learning model;

generating an improved statement using the retrained machine learning model; and

determining an improved act using the retrained machine learning model.

10. The machine learning system of claim 9 , wherein determining the act comprises extracting feature data and semantic vector data from the statement, and processing the feature data and the semantic vector data using the trained machine learning model.

11. The machine learning system of claim 10 , wherein extracting feature data and semantic vector data from the statement further comprises:

extracting the feature data and the semantic vector data from the statement using the trained machine learning model.

12. The machine learning system of claim 9 , further comprising

generating a thread using at least the statement and the act; and

generating an actor list using at least the statement.

13. The machine learning system of claim 12 , further comprising:

extracting participant data, named entity data, and conceptual type data from the statement; and

wherein generating the thread comprises generating using the participant data, the named entity data, and the conceptual type data.

14. The machine learning system of claim 12 , further comprising linking the thread using an actor from the actor list to generate a linked thread.

15. The machine learning system of claim 12 , further comprising generating an actionable item using the actor list and the thread.

16. A non-transitory, computer-readable medium storing instructions that, when executed by a processor, cause:

receiving text data for one or more users of the collaboration environment;

generating a statement by partitioning the text data;

determining an act using the statement using a trained machine learning model;

receiving user feedback from the one or more users;

training the machine learning model using the user feedback to generate a retrained machine learning model;

generating an improved statement using the retrained machine learning model; and

determining an improved act using the retrained machine learning model.

17. The non-transitory, computer-readable medium of claim 16 , wherein determining the act comprises extracting feature data and semantic vector data from the statement, and processing the feature data and the semantic vector data using the trained machine learning model.

18. The non-transitory, computer-readable medium of claim 16 , storing further instructions that, when executed by the processor, cause:

generating a thread using at least the statement and the act; and

generating an actor list using at least the statement.

19. The non-transitory, computer-readable medium of claim 18 , storing further instructions that, when executed by the processor, causes linking the thread using an actor from the actor list to generate a linked thread.

20. The non-transitory, computer-readable medium of claim 18 , storing further instructions that, when executed by the processor, causes generating an actionable item using the actor list and the thread.