AI SEO Statistics: Patent Broker (2026-07 edition)
40 questions · 120/120 expected AI responses · 3 models · measured 2026-07-06
Apply these findings to your patent broker SEO strategy.
This benchmark explains how AI assistants advise buyers. The related service page turns those findings into the technical, content, authority, and conversion priorities for this market.
The question bank
The questions we tested: a frozen buyer-intent benchmark for patent broker.
The question set was curated from a predefined buyer-intent taxonomy and held constant for this edition. Each model received the same wording. These are the prompts behind every percentage on this page.
Show all 40 questions
Model by model
22.8% question-level model disagreement.
This rate is the average pairwise disagreement between binary behavior codes across questions and behaviors. It is not the gap between the highest and lowest aggregated model percentages.
Behavior prevalence across 40 patent broker benchmark questions, 2026-07 edition. Last column: equal-model mean.
| Behavior | ChatGPT | Claude | Gemini | Equal-model mean |
|---|---|---|---|---|
| Recommends hiring a professional | 87.5% | 67.5% | 42.5% | 65.8% |
| Suggests DIY first | 15% | 12.5% | 12.5% | 13.3% |
| Names specific providers | 20% | 25% | 20% | 21.7% |
| Gives price or cost info | 17.5% | 22.5% | 35% | 25% |
| Tells to check reviews | 7.5% | 10% | 0% | 5.8% |
| Tells to verify credentials | 10% | 17.5% | 0% | 9.2% |
| Mentions case studies / portfolio | 15% | 27.5% | 2.5% | 15% |
| Mentions local proximity | 2.5% | 2.5% | 0% | 1.7% |
| Gives selection criteria | 27.5% | 45% | 22.5% | 31.7% |
| Warns about red flags | 15% | 20% | 7.5% | 14.2% |
| Asks a clarifying question | 50% | 77.5% | 0% | 42.5% |
| Recommends multiple quotes | 10% | 7.5% | 0% | 5.8% |
Question-level agreement
How often all measured models received the same binary code.
Agreement is calculated question by question for each behavior. A high value can coexist with a low behavior prevalence; it means the models usually agreed on whether the behavior appeared.
All-model binary agreement by behavior across 40 benchmark questions.
| Behavior | All-model agreement |
|---|---|
| Recommends hiring a professional | 45% |
| Suggests DIY first | 72.5% |
| Names specific providers | 67.5% |
| Gives price or cost info | 60% |
| Tells to check reviews | 85% |
| Tells to verify credentials | 75% |
| Mentions case studies / portfolio | 67.5% |
| Mentions local proximity | 95% |
| Gives selection criteria | 50% |
| Warns about red flags | 77.5% |
| Asks a clarifying question | 10% |
| Recommends multiple quotes | 85% |
By model
How each assistant handled Patent Broker questions.
Reading the 120 answers model by model shows how differently the three assistants treat the same patent broker questions. On the most consequential behavior, whether to send the buyer to a professional at all, the rate ranged from 87.5% (ChatGPT) down to 42.5% (Gemini), a 45-point gap on an identical question set.
Across the 40 patent broker answers it produced, ChatGPT recommended hiring a professional in 87.5% of them and suggested a DIY approach first 15% of the time. It named a specific provider in 20% of answers (about 0.6 distinct providers per answer) and included price or cost information 17.5% of the time. ChatGPT asked a clarifying question before answering in 50% of cases, warned about red flags or scams in 15%, and told the buyer to verify credentials in 10%, averaging 575 words per answer. On the remaining cues it told the buyer to check reviews in 7.5%, pointed to case studies or a portfolio in 15%, and framed the choice around local proximity in 2.5%; a selection-criteria checklist appeared in 27.5% of its answers and a recommendation to gather multiple quotes in 10%.
Across the 40 patent broker answers it produced, Claude recommended hiring a professional in 67.5% of them and suggested a DIY approach first 12.5% of the time. It named a specific provider in 25% of answers (about 1.2 distinct providers per answer) and included price or cost information 22.5% of the time. Claude asked a clarifying question before answering in 77.5% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 17.5%, averaging 312 words per answer. On the remaining cues it told the buyer to check reviews in 10%, pointed to case studies or a portfolio in 27.5%, and framed the choice around local proximity in 2.5%; a selection-criteria checklist appeared in 45% of its answers and a recommendation to gather multiple quotes in 7.5%.
Across the 40 patent broker answers it produced, Gemini recommended hiring a professional in 42.5% of them and suggested a DIY approach first 12.5% of the time. It named a specific provider in 20% of answers (about 0.6 distinct providers per answer) and included price or cost information 35% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 7.5%, and told the buyer to verify credentials in 0%, averaging 278 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 2.5%, and framed the choice around local proximity in 0%; a selection-criteria checklist appeared in 22.5% of its answers and a recommendation to gather multiple quotes in 0%.
Taken together, ChatGPT is the assistant most likely to route a buyer researching patent broker toward professional help (87.5%) and Gemini the least (42.5%). ChatGPT produced the longest answers, at 575 words on average. Specific providers were named most often by Claude (25%). Even there, roughly one answer in 4 carried a name.
Where they disagree
The behaviors where the choice of model changes the answer.
Question-level model disagreement is 22.8%. This is the average pairwise rate at which models received different binary codes across questions and behaviors. The observed rate spreads below are a separate measure showing where the choice of assistant matters most for a buyer researching patent broker:
- Asks a clarifying question: from 0% (Gemini) to 77.5% (Claude). The spread is 78 points.
- Recommends hiring a professional: from 42.5% (Gemini) to 87.5% (ChatGPT). The spread is 45 points.
- Mentions case studies or portfolio: from 2.5% (Gemini) to 27.5% (Claude). The spread is 25 points.
- Gives selection criteria: from 22.5% (Gemini) to 45% (Claude). The spread is 23 points.
- Gives price or cost information: from 17.5% (ChatGPT) to 35% (Gemini). The spread is 18 points.
The widest single gap concerns asks a clarifying question at 78 points. This means a buyer researching patent broker can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the patent broker market.
Where they agree
The points of near-consensus in Patent Broker.
On other behaviors the three models move almost in lockstep. The points of near-consensus for patent broker, where all three landed within a few points of each other:
- Suggests a DIY approach first: 12.5%–15% across all three (a 3-point spread).
- Mentions local proximity: 0%–2.5% across all three (a 3-point spread).
- Names a specific provider: 20%–25% across all three (a 5-point spread).
- Tells the buyer to check reviews: 0%–10% across all three (a 10-point spread).
Measured question by question, the three assistants coded a response the same way most consistently on "mentions local proximity" (identical coding in 95% of questions) and least consistently on "asks a clarifying question" (10%).
Every behavior, measured
All twelve coded behaviors for Patent Broker, averaged across the three models.
The behaviors AI models reproduce most often for patent broker are recommends hiring a professional (65.8% on average), asks a clarifying question (42.5%) and gives selection criteria (31.7%); the rarest are mentions local proximity (1.7%), recommends multiple quotes (5.8%) and tells the buyer to check reviews (5.8%). Each figure below is the share of a model's 40 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:
- Recommends hiring a professional: 65.8% on average (ChatGPT 87.5%, Claude 67.5%, Gemini 42.5%). The spread is 45 points.
- Asks a clarifying question: 42.5% on average (ChatGPT 50%, Claude 77.5%, Gemini 0%). The spread is 78 points.
- Gives selection criteria: 31.7% on average (ChatGPT 27.5%, Claude 45%, Gemini 22.5%). The spread is 23 points.
- Gives price or cost information: 25% on average (ChatGPT 17.5%, Claude 22.5%, Gemini 35%). The spread is 18 points.
- Names a specific provider: 21.7% on average (ChatGPT 20%, Claude 25%, Gemini 20%). The spread is 5 points.
- Mentions case studies or portfolio: 15% on average (ChatGPT 15%, Claude 27.5%, Gemini 2.5%). The spread is 25 points.
- Warns about red flags or scams: 14.2% on average (ChatGPT 15%, Claude 20%, Gemini 7.5%). The spread is 13 points.
- Suggests a DIY approach first: 13.3% on average (ChatGPT 15%, Claude 12.5%, Gemini 12.5%). The spread is 3 points.
- Tells the buyer to verify credentials: 9.2% on average (ChatGPT 10%, Claude 17.5%, Gemini 0%). The spread is 18 points.
- Tells the buyer to check reviews: 5.8% on average (ChatGPT 7.5%, Claude 10%, Gemini 0%). The spread is 10 points.
- Recommends multiple quotes: 5.8% on average (ChatGPT 10%, Claude 7.5%, Gemini 0%). The spread is 10 points.
- Mentions local proximity: 1.7% on average (ChatGPT 2.5%, Claude 2.5%, Gemini 0%). The spread is 3 points.
Trust signals
How well the models protect the patent broker buyer.
Beyond whether to hire, the rubric codes how carefully each assistant protects the patent broker buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 5.8% of answers on average. Verifying credentials or certifications appeared in 9.2%. Warning about red flags or scams appeared in 14.2%.
On structuring the decision, a selection-criteria checklist showed up in 31.7% of answers on average and a recommendation to gather multiple quotes in 5.8%. The single least-reproduced protective signal for patent broker is "tells the buyer to check reviews" at 5.8% on average. This is the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.
Referral behavior
Do AI models name Patent Broker providers?
For service providers the decisive question is whether these systems name anyone at all. Across 120 patent broker answers, a specific provider was named in 21.7% of responses on average, or roughly 0.8 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for patent broker: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.
The question set
What these 40 Patent Broker questions cover.
The 40 questions behind every percentage on this page form a frozen patent broker (legal services; buyer hiring decisions for this specific service) buyer-intent benchmark. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact patent broker question set rather than a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.
How to read this
A note on the numbers.
A percentage here is the share of a model's 40 answers in which the behavior appeared at least once. It is not a confidence score. Because each model answered every question exactly once on 2026-07-06, the figures describe this specific patent broker question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.
Methodology
A controlled snapshot, documented end to end.
40 frozen benchmark questions, one expected response per model per question (ChatGPT API (gpt-5-mini), Claude API (claude-sonnet-5), Gemini API (gemini-3-flash-preview)), collected 2026-07-06 and coded against a fixed 12-behavior rubric. The pipeline validates the schema, recomputes aggregates and reports consistency issues. AI outputs vary with model version, location and time, so the figures describe this edition's exact sample and measurement window. Read the full methodology →
Citation
Cite this edition.
Authority Specialist. “AI SEO Statistics: Patent Broker (2026-07 edition).” AuthoritySpecialist.com. https://authorityspecialist.com/research/ai-seo-statistics/services/legal/patent-broker