Abstract: A system for optimizing project and/or task completion through the use of machine learning is disclosed. The system receives attributes for a project and participants to participate in the project. The attributes are provided to a natural language processing engine to determine content related to the attributes. Once the content is determined, the system receives a selection of a portion of the content and attributes to be searched via a search engine of the system. The search engine may then determine potential combinations of participants, such as individuals, devices, programs, and/or robots, which are suited to participate in the project. A selected combination of participants may perform the project. Feedback relating to the project may be utilized to adjust search algorithm variable weights and parameters utilized by the search engine to optimize the relevance of potential combinations of participants generated in response to a future search for a future project.
Type:
Application
Filed:
June 15, 2021
Publication date:
October 7, 2021
Applicant:
CONCEPTDROP INC.
Inventors:
Philip T. Alexander, Richard J. Hubbard, Frank Sung Chul Cho, Ruchir Doshi
Abstract: A system for optimizing project and/or task completion through the use of machine learning is disclosed. The system receives attributes for a project and participants to participate in the project. The attributes are provided to a natural language processing engine to determine content related to the attributes. Once the content is determined, the system receives a selection of a portion of the content and attributes to be searched via a search engine of the system. The search engine may then determine potential combinations of participants, such as individuals, devices, programs, and/or robots, which are suited to participate in the project. A selected combination of participants may perform the project. Feedback relating to the project may be utilized to adjust search algorithm variable weights and parameters utilized by the search engine to optimize the relevance of potential combinations of participants generated in response to a future search for a future project.
Type:
Grant
Filed:
October 19, 2018
Date of Patent:
June 15, 2021
Assignee:
ConceptDrop Inc.
Inventors:
Philip T. Alexander, Richard J. Hubbard, Frank Sung Chul Cho, Ruchir Doshi
Abstract: A system for optimizing project and/or task completion through the use of machine learning is disclosed. The system receives attributes for a project and participants to participate in the project. The attributes are provided to a natural language processing engine to determine content related to the attributes. Once the content is determined, the system receives a selection of a portion of the content and attributes to be searched via a search engine of the system. The search engine may then determine potential combinations of participants, such as individuals, devices, programs, and/or robots, which are suited to participate in the project. A selected combination of participants may perform the project. Feedback relating to the project may be utilized to adjust search algorithm variable weights and parameters utilized by the search engine to optimize the relevance of potential combinations of participants generated in response to a future search for a future project.
Type:
Application
Filed:
October 19, 2018
Publication date:
April 25, 2019
Applicant:
ConceptDrop Inc.
Inventors:
Philip T. Alexander, Richard J. Hubbard, Frank Sung Chul Cho, Ruchir Doshi