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IBM · AI / ML · Filed May 7, 2025 · USPTO published Nov 12, 2026 · This week's release

IBM Patents a Panel of AI Judges to Grade Chatbot Answers

IBM's patent builds a courtroom for chatbot answers: a panel of AI judges scores each reply, and a higher court of super judges breaks any tie.

A panel of automated AI judges reviews a chatbot response against reference facts and votes on its accuracy. Drawing from patent filing US 2026/0359578 A1.
A panel of automated AI judges reviews a chatbot response against reference facts and votes on its accuracy.All 7 drawings ↓
Publication number
US 2026/0359578 A1(US20260359578A1)
Applicant
International Business Machines Corporation
Filed · Published
May 7, 2025 · Nov 12, 2026
Inventors
Bo WEN
Status when we published
Waiting for an examiner
Document
20 claims
US classification
704/9
Examiner
SERRAGUARD, SEAN ERIN (Art Unit 2657)

What IBM's panel of AI judges actually does

Every time you ask a chatbot a question, you're trusting that it didn't make something up. For a dinner recipe, that's a small risk. For medical advice or computer code, a confident wrong answer can do real damage.

IBM's patent describes a grading system for those answers. A panel of AI judges, each set up to look at something different such as accuracy, safety or clear writing, scores the response. If the judges mostly agree, that's the verdict.

If they split, the answer goes up to a second tier of super judges that settle the dispute. Their rulings then train the first panel, so the judges get better at their job over time.

From the filing · CLAIM 1
… measuring, by the generative system, a consensus among the evaluations of the ensemble of judge agents using a differentiator; resolving, by the generative system, one or more low consensus cases among the evaluations using a second layer of super judge agents; …

Translation: The system checks if the AI judges agree and calls in a panel of superior judges when they disagree.

How the judges' disagreement triggers a second tier

The system starts after a chatbot has produced an answer. That answer goes to a first layer of judge agents (separate AI programs, each told to grade the answer from one angle). The filing's examples include clinical accuracy, safety, fit with medical guidelines and clear writing. The judges can run on different large language models (the AI engines behind chatbots), from different vendors or with different settings, so they don't all think alike. The filing suggests an odd number of judges, such as 3, 5 or 7, to avoid ties.

A component called the differentiator then checks how much the judges agree. One method averages the scores and calculates the variance (a measure of how spread out they are), then compares it to a set threshold. Another compares how similar the judges' written reasoning is in meaning.

High agreement ends the process. Low agreement calls in the super judges, which compare the judges' evaluations in pairs and can consult a domain knowledge base, such as medical guidelines. A weighted vote, where each judge counts according to its record on test data, produces the final evaluation.

Finally, the system tunes the judges using reinforcement learning (a trial-and-reward training method) based on the super judges' rulings. The filing says this can run continuously.

Why a chatbot referee matters for health and code

Chatbots that confidently invent facts become a bigger problem the more people rely on them for things like medical questions or code. The filing's pitch is an automated referee that can grade answers at a scale human experts can't match, since expert review is slow and expensive. If it works as described, a checking layer like this could catch bad answers before they reach you, or show where a chatbot keeps going wrong.

The patent doesn't name a product. It frames the system as an evaluation tool for companies running chatbots, and the claims reach any system that generates content. For you, the effect would show up indirectly, as chatbots in sensitive fields that get tested more often and more carefully.

IBM's 78th filing we've tracked since May in our AI safety guardrails work adds to earlier applications on keeping AI on facts and grading how you ask AI, pushing further into AI oversight.

Source: US 2026/0359578 A1 at the USPTO (PDF). Full patent text and figures are in the official publication.

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The drawings · 7 sheets

7 drawing sheets from US 2026/0359578 A1 · click any drawing to enlarge

Cloud servers and network links hosting the chatbot system, drawing sheet 1 from US 2026/0359578 A1
Sheet 1 Cloud servers and network links hosting the chatbot system
Sending your question to the server to draft an answer, drawing sheet 2 from US 2026/0359578 A1
Sheet 2 Sending your question to the server to draft an answer
AI judges voting on whether a chatbot answer is accurate, drawing sheet 3 from US 2026/0359578 A1
Sheet 3 AI judges voting on whether a chatbot answer is accurate
Senior AI judges resolving split votes using specialized knowledge, drawing sheet 4 from US 2026/0359578 A1
Sheet 4 Senior AI judges resolving split votes using specialized knowledge
Returning the vetted answer and updating the AI judges, drawing sheet 5 from US 2026/0359578 A1
Sheet 5 Returning the vetted answer and updating the AI judges
Basic computer hardware used to evaluate conversational answers, drawing sheet 6 from US 2026/0359578 A1
Sheet 6 Basic computer hardware used to evaluate conversational answers
Checking answers using tiered panels of AI reviewers, drawing sheet 7 from US 2026/0359578 A1
Sheet 7 Checking answers using tiered panels of AI reviewers
Patent filing page

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