Toi Labs, Inc.

Canopy Acquisition, LLC

Vikram Kashyap

Benjamin K Yaffe

Kevin D Simmons

Thomas Reidemeister

Rodolfo Camacho

Dan Cheung

Gabrielle Kim

Parmoon Sarmadi

Ting-Hsuan Lin

Jeremy J Blackburn

Anthony T Banas

The disclosed embodiments relate to system, method and apparatus for forming ML sessions so that an AI system can identify, track and diagnose a user through the user's bathroom sessions. As used herein, a session is a period of time during which a user is active in the toilet. The session may include certain inactivity if such inactivity lasts only for a predefined duration. In an exemplary embodiment, a system is configured to capture images of excreta and, using ML algorithms, classifies the toileting session using Digital Biomarkers (DBMs). The ability to create an excreta log to accurately deliver detailed information to doctors and healthcare providers can revolutionize healthcare by notifying when further urine or fecal screening is necessary. The disclosed embodiments of ML and image identification through AI requires no user behavior change and provides a link between medical records and specific excreta patterns.

System, method and apparatus for forming machine learning sessions

Provided is a biomonitoring device that measures a parameter of a material expelled during use of a toilet by a user. Also provided is a biomonitoring mirror device that identifies a user, detects a febrile illness in a user, dispenses medications/supplements, connects to electrical device accessories in the bathroom, and provides an interactive user interface. Additionally provided is a system for measuring a parameter of a material expelled during use of a toilet by a user. Further provided is a method of determining a physiological parameter of a user.

Biomonitoring devices, methods, and systems for use in a bathroom setting

Methods and apparatus, including computer program products, for a predictive score for lending. A method includes receiving an employee's health insurance plan design information and general health and wellness data, weighting selected ones of the employee's health insurance plan design information and general health and wellness data, and generating a credit risk indicator from the weighted selected ones of the employee's health insurance plan design information and general health and wellness data.

Predictive score for lending

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San Francisco, CA, United States of America

Vikram Kashyap