US 11,922,334 B2
Using machine learning to determine job families using job titles
Jianhang Kuang, Newark, CA (US); and Andrew J. Min, Millbrea, CA (US)
Assigned to Sequoia Benefits and Insurance Services, LLC, San Mateo, CA (US)
Filed by Sequoia Benefits and Insurance Services, LLC, San Mateo, CA (US)
Filed on Jan. 20, 2023, as Appl. No. 18/099,789.
Application 18/099,789 is a division of application No. 16/856,382, filed on Apr. 23, 2020, granted, now 11,562,266.
Prior Publication US 2023/0162061 A1, May 25, 2023
Int. Cl. G06N 5/04 (2023.01); G06N 20/00 (2019.01)
CPC G06N 5/04 (2013.01) [G06N 20/00 (2019.01)] 20 Claims
OG exemplary drawing
 
1. A method for using a trained machine learning model with respect to information pertaining to a job title of a plurality of job titles to determine a job family of a plurality of job families that corresponds to the job title, the method comprising:
providing, by a processing device, to the trained machine learning model first input comprising information identifying the job title associated with an organization of a plurality of organizations; and
obtaining, from 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, (ii) a level of confidence that the job family corresponds to the job title.