US 12,333,243 B2
Machine learning of response selection to structured data input including momentum classification
Joshua Howard Levy, Austin, TX (US); Alexa Breann Eun Taylor, Georgetown, TX (US); and Reed Coke, Austin, TX (US)
Assigned to Ojo Labs, Inc., Austin, TX (US)
Filed by OJO Labs, Inc., Austin, TX (US)
Filed on Dec. 27, 2020, as Appl. No. 17/134,481.
Application 17/134,481 is a continuation in part of application No. 15/992,851, filed on May 30, 2018, granted, now 10,970,290.
Application 15/992,851 is a continuation of application No. 15/897,885, filed on Feb. 15, 2018, granted, now 10,019,491, issued on Jul. 10, 2018.
Application 15/897,885 is a continuation in part of application No. 15/826,151, filed on Nov. 29, 2017, granted, now 10,013,654, issued on Jul. 3, 2018.
Claims priority of provisional application 62/956,166, filed on Dec. 31, 2019.
Prior Publication US 2021/0192131 A1, Jun. 24, 2021
Int. Cl. G06N 20/00 (2019.01); G06F 16/2457 (2019.01); G06F 17/11 (2006.01); G06F 40/186 (2020.01); G06F 40/20 (2020.01); G06F 40/30 (2020.01); G06F 40/35 (2020.01); G06F 40/56 (2020.01)
CPC G06F 40/20 (2020.01) [G06F 16/24578 (2019.01); G06F 17/11 (2013.01); G06F 40/56 (2020.01); G06N 20/00 (2019.01); G06F 40/186 (2020.01); G06F 40/30 (2020.01); G06F 40/35 (2020.01)] 30 Claims
OG exemplary drawing
 
1. A method of machine learning in the selection of a ranked response to a structured data input having a natural language processing output schema received from a requesting device, the method comprising:
in an electronic, machine learning processing system:
receiving the structured data input, wherein the structured data input includes filtering parameters for conversion into response template filtering criteria;
querying a library of response templates to identify candidate response templates that meet the response template filtering criteria to filter the response templates;
receiving a selection of the candidate response templates that meet the response template filtering criteria and respond to the structured input data, wherein the candidate response templates include static data;
receiving messages from user devices after transmission of one or more response templates to the user devices;
providing the messages to a momentum classifier implemented as a machine learning model;
assigning momentum classifications to the messages using the machine learning model;
correlating the templates communicated to the user devices with corresponding momentum classifications;
operating a ranking engine to rank the selection of response templates in accordance with ranking criteria, wherein the ranking criteria includes momentum classifications for the candidate response templates; and
selecting a highest ranked response template to provide a response to a device;
deriving the response to the structured data input from the selected, highest ranked, candidate response template;
providing to the response to a recipient device; and
providing feedback to the ranking engine to refine the ranking criteria.