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.

Patent No.:

US 10412225 B1

Date of Patent:

Sep. 10, 2019

Filed:

Oct. 08, 2018

Systems and methods of natural language processing to rank users of real time communications connections

Applicant:

Ingenio, Llc, San Francisco, CA (US);

Inventor:

Neeraj Jandwani, San Ramon, CA (US);

Assignee:

INGENIO, LLC, San Francisco, CA (US);

Attorney:

Greenberg Traurig, LLP

Primary Examiner:

Thjuan K Addy

Int. Cl.

CPC ...

H04M 3/00 (2006.01); H04M 5/00 (2006.01); H04M 3/523 (2006.01); G06F 17/27 (2006.01); H04L 29/06 (2006.01); G06F 17/30 (2006.01); G06N 7/00 (2006.01); G06N 99/00 (2019.01); H04M 3/56 (2006.01); G06N 20/00 (2019.01); G06F 16/9535 (2019.01); G06F 17/28 (2006.01); G09B 7/00 (2006.01); G10L 15/26 (2006.01); H04M 3/42 (2006.01); G06F 7/02 (2006.01); G06Q 20/14 (2012.01); G06Q 30/02 (2012.01); H04M 7/00 (2006.01);

U.S. Cl.

CPC ...

H04M 3/56 (2013.01); G06F 16/9535 (2019.01); G06F 17/28 (2013.01); G06N 7/005 (2013.01); G06N 20/00 (2019.01); G09B 7/00 (2013.01); G10L 15/26 (2013.01); H04L 65/1069 (2013.01); H04L 65/80 (2013.01); H04M 3/42221 (2013.01); H04M 3/5232 (2013.01); H04M 3/5233 (2013.01); G06F 7/026 (2013.01); G06Q 20/14 (2013.01); G06Q 30/0275 (2013.01); H04M 7/003 (2013.01); H04M 2201/40 (2013.01); H04M 2203/2061 (2013.01); H04M 2203/408 (2013.01); H04M 2203/551 (2013.01); H04M 2203/558 (2013.01);

Abstract

A computing apparatus configured to perform natural language processing, e.g., by comparing the words in a communication transcript of a user to a list of keywords, to generate an input vector representing a pattern of text in the communication transcript. A predictive model is generated from correlating input vectors to user ranking scores, e.g., for retention. The input vector determined from a communication transcript is applied to the computation model to compute a predicted retention score of the user. The retention score can be used, for example, to select a personalized recommendation for a communication connection to an adviser and/or a targeted offer.

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