AI Cancer Prognosis System Using EHR Data
Summary
The USPTO granted patent US12603184B2 on April 14, 2026, to The Regents of the University of California for a system and method for AI-driven cancer prognosis using electronic health records. The patent covers natural language processing of EHR data and term frequency inverse document frequency analysis to identify predictive terms for cancer survival outcomes. The assignee Olivier Morin and co-inventors developed a continuously learning infrastructure for applying AI to oncology clinical needs and in silico clinical trials.
What changed
The USPTO has granted patent US12603184B2 titled 'Systems and Methods for Continuous Cancer Treatment and Prognostics' to The Regents of the University of California. The patent covers AI systems that process electronic health records using natural language processing and apply term frequency inverse document frequency techniques to identify terms predictive for cancer survival. The patent also covers capabilities for in silico clinical trials based on health record data.
Healthcare institutions, oncology departments, AI developers in health informatics, and researchers using similar AI-driven cancer prognosis methods should assess whether licensing from the patent holder is required. The University of California Regents now holds exclusive rights to this AI methodology for cancer treatment and prognosis, which may affect competitive offerings or research activities in this domain.
What to do next
- Monitor for potential licensing opportunities
- Review if similar AI-driven health informatics systems may require licensing
Archived snapshot
Apr 15, 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.
Systems and methods for continuous cancer treatment and prognostics
Grant US12603184B2 Kind: B2 Apr 14, 2026
Assignee
The Regents of the University of California
Inventors
Olivier Morin
Abstract
Oncology faces a digital chasm in its quest for personalized treatments. Despite the adoption of electronic health records (EHR), most hospitals are ill-equipped for data science research. Embodiments herein describe a continuously learning infrastructure through which multimodal health data are systematically organized and data quality is assessed with the goal of applying artificial intelligence to address unmet clinical needs. Embodiments describe systems and methods for improved cancer prognostics, including by obtaining electronic medical records and performing natural language processing thereon. Additional embodiments apply term frequency inverse document frequency to identify terms that are predictive for cancer survival. Additional embodiments are capable of performing in silico clinical trials based on information comprised in a collection of health records.
CPC Classifications
G16H 50/30 G16H 10/60 G16H 50/70 G16H 50/20 G06F 40/216
Filing Date
2022-05-02
Application No.
18557909
Claims
17
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