Systems and methods for structuring information in a collaboration environment
AssigneeRingCentral, Inc.
InventorsVadim Zhuk, Helen Prask, Ivan Anisimov, William Zhuk
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Abstract
A computer-implemented machine learning method for improving a collaboration environment is provided. The method comprises receiving text data for one or more users of the collaboration environment. The method further comprises generating a statement by partitioning the text data. The method further comprises determining an act using the statement and generating a thread using at least the statement and the act. The method further comprises generating an actor list using at least the thread, and generating an actionable item using the actor list and the thread.
Claims20
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;
- generating an actor list using at least the statement, wherein the actor list comprises a plurality of actors identified by at least one of a user identification or a team identification;
- determining an act using the statement, 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 a trained machine learning model to determine the act,
- wherein the act comprises a string of text extracted from the text data indicating an action for at least one actor from the plurality of actors;
- and
- determining first relational information between at least a first actor and a second actor in the actor list, the first relational information comprising identifications of the first actor and the second actor, information identifying the first actor as requesting the action, and the act indicating the action for the second actor, wherein the determining comprises processing the statement using a trained machine learning model.
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;
- generating an actor list using at least the statement, wherein the actor list comprises a plurality of actors identified by at least one of a user identification or a team identification;
- determining an act using the statement, wherein determining the act using the statement comprises:
- extracting feature data and semantic vector data from the statement, and
- processing the feature data and the semantic vector data using a trained machine learning model to determine the act,
- wherein the act comprises a string of text extracted from the text data indicating an action for at least one actor from the plurality of actors;
- and
- determining first relational information between at least a first actor and a second actor in the actor list, the first relational information comprising identifications of the first actor and the second actor, information identifying the first actor as requesting the action, and the act indicating the action for the second actor, wherein the determining comprises processing the statement using a trained machine learning model.
A non-transitory, computer-readable medium storing a set of 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;
- generating an actor list using at least the statement, wherein the actor list comprises a plurality of actors identified by at least one of a user identification or a team identification;
- determining an act using the statement, wherein determining the act using the statement comprises:
- extracting feature data and semantic vector data from the statement, and
- processing the feature data and the semantic vector data using a trained machine learning model to determine the act,
- wherein the act comprises a string of text extracted from the text data indicating an action for at least one actor from the plurality of actors;
- and
- determining first relational information between at least a first actor and a second actor in the actor list, the first relational information comprising identifications of the first actor and the second actor, information identifying the first actor as requesting the action, and the act indicating the action for the second actor, wherein the determining comprises processing the statement using a trained machine learning model.
Dependencies are inferred from the claim text.