- Patent Title: Using machine learning to determine job families using job titles
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Application No.: US18099789Application Date: 2023-01-20
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Publication No.: US11922334B2Publication Date: 2024-03-05
- Inventor: Jianhang Kuang , Andrew J. Min
- Applicant: Sequoia Benefits and Insurance Services, LLC
- Applicant Address: US CA San Mateo
- Assignee: Sequoia Benefits and Insurance Services, LLC
- Current Assignee: Sequoia Benefits and Insurance Services, LLC
- Current Assignee Address: US CA San Mateo
- Agency: Lowenstein Sandler LLP
- The original application number of the division: US16856382 2020.04.23
- Main IPC: G06N5/04
- IPC: G06N5/04 ; G06N20/00

Abstract:
A method and system for using a trained machine learning model with respect to information pertaining to a job title of multiple job titles to determine a job family of multiple job families that corresponds to the job title is disclosed. First input comprising information identifying the job title associated with an organization of a plurality of organizations is provided to the trained machine learning model. One or more outputs identifying (i) an indication of the job family that identifies a category of personnel positions that are categorized based on one or more characteristics that are shared between the personnel positions of the category, and (ii) a level of confidence that the job family corresponds to the job title is obtained from the trained machine learning model.
Public/Granted literature
- US20230162061A1 USING MACHINE LEARNING TO DETERMINE JOB FAMILIES USING JOB TITLES Public/Granted day:2023-05-25
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