Machine learning models for vehicle accident potential injury detection

US12211620B2 · kind B2 · utility

Assignee

• State Farm Mutual Automobile Insurance Company · US

Inventors

• Justin Devore · Bloomington, US
• Sateesh K Nallamothu · Normal, US
• Dayne Dalpoas · Delavan, US
• Ruth Anne Grosskopf · Bloomington, US
• Jason Beckman · Bloomington, US
• Kathleen Cordiner · Sun Lakes, US
• Jennifer Megargell · Bloomington, US

Key dates

• Filing date: Mar 3, 2022
• Grant date: Jan 28, 2025
• Expiry date: Nov 12, 2042

Classification

• Technology area (CPC G): Physics
• CPC primary: G16H50/70
• WIPO field: Medical technology
• WIPO sector: Instruments

Abstract

Techniques described herein relate to training and executing machine learning models configured to determine injury probabilities based on vehicle accident data. In some cases, a decision tree model may be constructed including various branching criteria based on particular vehicle accident data and injury ground truth data, including leaf nodes storing corresponding injury probabilities. A model execution component may execute the trained models to determine the probability of potential injuries associated with vehicle accidents. Based on the potential injury probabilities, a vehicle accident analysis system may identify target computer systems and/or target processes to be initiated on the target systems, based on the injury probabilities. In some example, the model execution architecture may be implemented using an event-driven system and/or cloud-based data storage and services to receive, store, and process data events associated with individual vehicle accidents.