US 12,315,611 B2
Systems and methods for regulating provision of messages with content from disparate sources based on risk and feedback data
Sudheer Guttikonda, Edison, NJ (US); William Morse, New York, NY (US); Chuanhan Qiu, New York, NY (US); and Austin Speier, Brooklyn, NY (US)
Assigned to Click Therapeutics, Inc., New York, NY (US)
Filed by Click Therapeutics, Inc., New York, NY (US)
Filed on Nov. 6, 2024, as Appl. No. 18/939,143.
Application 18/939,143 is a continuation of application No. 18/750,013, filed on Jun. 21, 2024.
Application 18/750,013 is a continuation of application No. 18/377,931, filed on Oct. 9, 2023, granted, now 12,040,063.
Prior Publication US 2025/0118404 A1, Apr. 10, 2025
This patent is subject to a terminal disclaimer.
Int. Cl. G16H 20/00 (2018.01); A61B 5/00 (2006.01); G06F 40/40 (2020.01); G16H 20/10 (2018.01)
CPC G16H 20/00 (2018.01) [A61B 5/4833 (2013.01); A61B 5/7475 (2013.01); G06F 40/40 (2020.01)] 26 Claims
OG exemplary drawing
 
1. A method, comprising:
identifying, by one or more processors, first digital content to be provided via a network;
applying, by the one or more processors, the first digital content to a machine learning (ML) model having a set of weights to generate a first output, wherein the ML model is trained by:
identifying a training dataset including a plurality of examples, each example of the plurality of examples identifying respective second digital content and a first indication identifying one of compliance or non-compliance for provision,
applying the second digital content from an example of the plurality of examples of the training dataset into the ML model to generate a second output,
determining, from the second output, a second indication of the second digital content as one of compliant or non-compliant used to control provision,
comparing the first indication from the example of the training dataset with the second indication determined by the ML model, and
updating, responsive to comparing the first indication with the second indication, at least one of the set of weights of the ML model using the comparison to further train the ML model;
determining, by the one or more processors, from the first output, an indication of the first digital content as non-compliant;
storing, by the one or more processors, using one or more data structures, an association between the first digital content and the indication to restrict the first digital content from provision responsive to determining the indication of the first digital content as non-compliant; and
generating, by the one or more processors, based on applying the ML model, a portion identifying at least a subsection of the first digital content to be modified, responsive to determining the indication of the first digital content as non-compliant.