Click Therapeutics, Inc.

Chuanhan Qiu

William Morse

Sudheer Guttikonda

Austin Speier

Yinxiang Gao

Ritesh Manchanda

Apoorv Bajaj

Aspects of the present disclosure are directed to systems, methods, and computer readable media for regulating digital therapeutic content for provision. A computing system may identify a digital therapeutic content to be provided via a network. The computing system may apply the digital therapeutic content to a machine learning (ML) model having a set of weights. The computing system may determine, from applying the digital therapeutic content to the ML model, an indication as of one of compliance or non-compliance. The computing system may store, using one or more data structures, an association between the digital therapeutic content and the indication used to control provision of the digital therapeutic content via the network, by (i) restricting the digital therapeutic content from provision responsive to determining the indication of non-compliance and (ii) permitting the digital therapeutic content to be provided responsive to determining the indication of compliance.

Systems and methods for regulating provision of messages with content from disparate sources based on risk and feedback data

Provided herein are systems and methods for simulating testing environments for digital therapeutic applications to address conditions over spans of time. A computing system may select a trigger from a plurality of triggers of an event scheduler for addressing a condition. The computing system may set a clock to a timestamp of the trigger, wherein the clock is accessible to the service and to the user device. The computing system may cause the service to transmit a message for addressing the condition to the user device, responsive to the timestamp of the clock corresponding to the timestamp of the trigger. The computing system may identify that the message is communicated between the service and the user device in accordance with the trigger. The computing system may determine that the trigger of the event scheduler is validated, responsive to identifying that the message is communicated.

Automatically varying system clocks to simulate test environments for application triggers generated using machine learning

Aspects of the present disclosure are directed to systems, methods, and computer readable media for configuring generation of digital therapeutic content for provision. The system can receive a text input including one or more parameters identifying an audience and at least one domain. The system can identify content items generated by corresponding generative transformer models each using a prompt created based on the text input. The system can select, from a set of risk models for the domains, at least one risk model corresponding to the at least one domain. The system can apply the at least one risk model to each content item of the set of content items to determine a risk score. The system can select a content item based on the risk score of the content item. The system can present the content item including the respective digital therapeutic content.

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New York, NY, United States of America

Chuanhan Qiu