The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jun. 27, 2023

Filed:

Dec. 01, 2020
Applicant:

8x8, Inc., Campbell, CA (US);

Inventors:

Solomon Fung, San Mateo, CA (US);

Soumyadeb Mitra, San Jose, CA (US);

Abhishek Kashyap, San Jose, CA (US);

Arunim Samat, San Francisco, CA (US);

Venkat Nagaswamy, San Francisco, CA (US);

Justin Driemeyer, Driemeyer, CA (US);

Assignee:

8x8, Inc., Campbell, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06F 40/30 (2020.01); G06Q 30/0241 (2023.01); G06Q 10/067 (2023.01); G06F 16/951 (2019.01); G06Q 10/105 (2023.01); G06F 18/2135 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06N 3/084 (2023.01); G06N 3/088 (2023.01); G06N 5/04 (2023.01);
U.S. Cl.
CPC ...
G06F 40/30 (2020.01); G06F 16/951 (2019.01); G06F 18/21355 (2023.01); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06Q 10/067 (2013.01); G06Q 10/105 (2013.01); G06Q 30/0276 (2013.01); G06N 3/084 (2013.01); G06N 3/088 (2013.01); G06N 5/04 (2013.01);
Abstract

In one example, a computer-based system determines a relationship between a first job and a second job at one or more companies, by using a title data store, a training module, and a prediction module, wherein the title data store accepts job-related information characterizing at least one job-related position that includes at least one of title, corporate entity, job description, and job-related interest data. The training module accepts input data from the title data store, calculates or generates a set of coefficients and a set of job-related vectors from the input data, and stores the coefficients into a database. The prediction module may accept: a first set of data including at least one of a first title, a first corporate designation data, a second set of data including at least one of a second title and a second corporate designation data, and the coefficients from the training module; and then a similarity between the first set of data and the second set of data may be calculated.


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