Methods and Systems of Predicting Total Loss Events
Summary
USPTO published patent application US20260091748A1 assigned to Cambridge Mobile Telematics Inc., covering machine learning methods for predicting total loss events. The system uses mobile device sensors to detect crash events and generates confidence scores for total loss predictions using multiple ML models. Application was filed December 9, 2025 and published April 2, 2026.
What changed
Cambridge Mobile Telematics Inc. filed patent application US20260091748A1 disclosing methods for predicting total loss events using machine learning. The system detects crash events through mobile device sensors, records sensor data, and generates feature vectors combining sensor data with additional data types. A first ML model is selected from multiple models based on available data types to generate a confidence score for total loss events.
Patent applications represent early-stage filings and do not impose immediate compliance obligations. R&D teams should monitor relevant patent filings in crash detection and insurance technology to identify emerging technologies and potential prior art. Legal and IP strategy teams may review for freedom-to-operate analysis or potential licensing considerations.
Archived snapshot
Apr 2, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
METHODS AND SYSTEMS OF PREDICTING TOTAL LOSS EVENTS
Application US20260091748A1 Kind: A1 Apr 02, 2026
Assignee
Cambridge Mobile Telematics Inc.
Inventors
Yuting Qi, Cornelius Young, Rizki Syarif, Burak Erem
Abstract
A mobile device detects a crash event using one or more sensors of a mobile device. The mobile device records a first set of data from the one or more sensors of the mobile device. The mobile device generates a first feature vector including the first set of data and available values for one or more additional data types. The mobile device executes a first machine-learning model selected from a plurality of machine-learning models based on the one or more additional data types for which there are available values to generate a first confidence of a total loss event.
CPC Classifications
B60R 21/013 G06N 5/04 G06N 20/00 G06Q 30/0278 G06Q 40/08 G07C 5/008 H04W 4/40 G06Q 10/20
Filing Date
2025-12-09
Application No.
19414201
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