US 12,278,006 B1
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 Jun. 21, 2024, as Appl. No. 18/750,013.
Application 18/750,013 is a continuation of application No. 18/377,931, filed on Oct. 9, 2023, granted, now 12,040,063.
Int. Cl. G16H 20/00 (2018.01); A61B 5/00 (2006.01); G06F 40/40 (2020.01)
CPC G16H 20/00 (2018.01) [A61B 5/4833 (2013.01); A61B 5/7475 (2013.01); G06F 40/40 (2020.01)] 18 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving, by one or more processors, an identification of input digital therapeutic content via a user interface;
applying, by the one or more processors, the input digital therapeutic content to a machine learning (ML) model having a set of weights, wherein the ML model is trained by:
identifying a training dataset identifying training digital therapeutic content and a label identifying one of compliance or non-compliance for provision,
applying the training digital therapeutic content of the training dataset into the ML model to generate an output, and
updating, based on comparing the label with the output, at least one of the set of weights of the ML model to train the ML model;
selecting, by the one or more processors, based on applying the input digital therapeutic content to the ML model, a portion in the input digital therapeutic content identified as non-compliant;
causing, by the one or more processors, information associated with the portion in the input digital therapeutic content to be presented via the user interface;
generating, by the one or more processors, feedback data using interactions with the user interface; and
further training, by the one or more processors, the ML model by updating at least one of the set of weights of the ML model using the feedback data generated using the interactions.