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Patra Awarded U.S. Patent for AI Value Extraction

USPTO grant validates the governed, human-in-the-loop AI approach Patra has built since 2022, the same principles that underpin its agentic automation platform.

El Dorado Hills, CA, July 07, 2026 (GLOBE NEWSWIRE) -- Patra, which operates one of the insurance industry’s largest production-grade agentic AI platforms, today announced that the United States Patent and Trademark Office has granted U.S. Patent No. 12,639,972, "Systems and Methods for Value Extraction and Guided Review," describing the AI and human-in-the-loop methods at the core of Patra's Policy Checking AI. The patent issued May 26, 2026, names Patra Chief Technology Officer Tony Li and Senior Director of AI Engineering Juan Cristian Martinez Vega as inventors, with Patra Corporation as sole assignee.

Human-in-the-loop, by design and ahead of the curve 

The patent describes a design philosophy the broader industry has only recently adopted as consensus: AI is powerful but requires governance, with expert human review built into the workflow rather than added as an afterthought. With a priority date of February 2022, the patented systems and methods predate the generative and agentic AI surge that has since reshaped the market. “We were building governed, expert-supervised AI with real review at the center of the process before that became the industry’s accepted position,” said Pratap Sarker, Chief Executive Officer at Patra. “That same conviction guides the agentic platform we’re building now: capable, inspectable, and governed by design, with a person able to step in at any time.” 

What the patent describes 

The patent describes a method for automatically extracting values from insurance documents and guiding a reviewer directly to the information that matters. Among other things, the system scans a document, maps the position of every word, locates the items a user is looking for, identifies the likely value associated with each, and then directs the user to confirm the result. Accuracy improves over time through a feedback loop centered on expert review.  

In practice, this is the engine behind faster, more accurate policy checking: the AI does the heavy lifting of finding and extracting information across lengthy policy documents, while a Patra expert reviewer remains in control of the final determination. 

“This patent illustrates the foundation of the AI and human-in-the-loop mechanisms that are still at the core of what Policy Checking AI is today,” said Cristian Vega, Senior Director of AI Engineering at Patra. “More importantly, it supports the needs of our clients and the carriers and regulators who increasingly expect explainable, documented AI use with meaningful human oversight.”

Why it matters for the insurance distribution channel 

As outlined in Patra’s 2026 AI and Insurtech Trends report, there is practical value for agencies and brokers: policy checking is faster and more accurate, backed by proprietary, patented technology, and verified by experienced professionals. The more decisive advantage right now is risk mitigation, especially as the legal framework determining liability for AI failures is unsettled. Patra’s patented approach to governed, expert-supervised AI allows it to continue offering the protection of its market-leading errors and omissions (E&O) liability insurance to back up its work. With accountability for technology and expert judgement together, Patra can confidently commit to client outcomes. 

About Patra 

Patra is an outcome-focused operating partner working exclusively for the insurance industry. Combining an agentic platform, process intelligence, and expert teams, Patra delivers measurable results to clients. With 20 years of deep domain expertise, exceptional client retention rates, and significant sustained investment in AI, Patra partners with agencies, brokers, MGAs, MGUs, and carriers to transform how insurance operations are delivered. Learn more at patra.com


Joanna Rogers SVP, Marketing
Patra Corporation
(630) 205-3153
joanna.rogers@patracorp.com

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