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Annexure I of the Patent Office's AI guidelines is a set of eleven worked uses of AI in examination, each followed by the lesson the Office draws. It is written for officers, but it shows applicants and searchers how AI tools behave on patent tasks and why the Office insists on checking each output. This article summarises each example in our own words. The tools are not named in the Guidelines, and none is named here. The guidelines are about the Office's own use of AI, not about patenting AI inventions.
The Guidelines, as published by the Patent Office, are the Office's guidance and do not have the force of law; the Patents Act, 1970 and the Patents Rules, 2003 as now in force prevail. The Office revises its guidelines, so check the current version on ipindia.gov.in.
The Annexure shows that AI output on patent tasks is sensitive to the model, the prompt and the conditions, can be fluent and wrong, and must be verified against primary sources. Its examples cover classification, search terms, claim breakdown, translation, drafting help, citations, novelty mapping, clarity, sufficiency and case law. A note states the examples are not guidance on prompt techniques or good practice for AI tools.
How the Annexure frames the examples
The note at the start says the examples do not teach prompt engineering or advise on training AI models; officers may adopt any suitable prompts within the safeguards of the main Guidelines. Each example pairs a use with a result and a finding about human oversight. The pairing shows the Office's reasoning on where AI helps and where it misleads, which is also useful for a searcher preparing a prior-art report for a client; see our patent search and prior art service.
The eleven examples in short
| Example | What was tried | What the Office concluded |
|---|---|---|
| 1.1 Classification | The same claim text was given to several AI tools with different prompts and conditions, and the suggested IPC and CPC codes were compared with manually assigned codes for two applications; a second comparison used three models on three applications | Output varied by model, prompt and conditions, sometimes close to the manual codes and sometimes broad or irrelevant, even after a long set of conditions was given; human classification remains essential |
| 1.2 Search terms | Two models generated core terms, near synonyms, related expressions, broader exploratory terms and terms to avoid for a water-treatment claim | Both found the central concepts but differed in breadth and drift; the officer must decide which terms are close, adjacent, exploratory or noisy |
| 1.3 Claim-feature extraction | Described in words: AI can split a long claim into features | Risks: wrong splitting, ignored relational words, missed negative limits and ranges; treat as a rough draft, a restricted use |
| 1.4 Translation | Described in words: rough understanding of a foreign document for screening | Words such as coupled, bonded and attached are not interchangeable; check passages relied on and record that a machine translation was used |
| 1.5 Draft office communication | Described in words: help with grammar, headings and structure | Language support only; check every statement; remove casual AI introductory lines from a final draft |
| 1.6 Citations | Described in words: AI used to find case law and references | Fabricated or misdescribed authorities harm the credibility of the action; verify in the source |
| 1.7 Public tool and unpublished material | Described in words | Prohibited: unpublished claims, descriptions and hearing notes must not go into a public tool |
| 1.8 Novelty and inventive step | A claim for a laparoscopic instrument was compared with one prior-art document under two differently worded prompts | One answer found no novelty, the other found novelty; both were fluent, so scrutiny against the document is mandatory. A feature table then showed AI mapping with the officer's note on each row, several marked as needing verification |
| 1.9 Clarity | A composition claim with percentages and a ratio was put to AI for clarity issues under section 10(4), then a draft objection in the Office's style | AI spotted real ambiguity and drafted objections of rising scope, but the final legal call stays with the officer |
| 1.10 Sufficiency | AI was asked to assess sufficiency of a specification for a medical signal-processing monitor, and of a refused wind-energy application | The first answer was long, mixed and hedged and "can sow confusion"; the second matched the Office's earlier refusal; human oversight remains needed |
| 1.11 Case law | AI was asked for verbatim quotes from a decision of the former appellate board, the case named in the example being Pharmacyclics v. Controller General of Patents, as cited in the Guidelines | The quotes were constructed, not extracted, and the tool admitted this only after repeated prompts; never trust a verbatim quote without the primary source |
What the Office takes from the examples
Four patterns run through the examples.
- Sensitivity. The same claim can give different classifications, terms or conclusions depending on the model and prompt. The Annexure notes that AI responses may not be consistent or reproducible.
- Fluency is not accuracy. Examples 1.8, 1.10 and 1.11 involve confident, well-formed answers that were incomplete, hedged or wrong.
- Preliminary use only. Mapping, issue-spotting and drafting help are acceptable only as a first pass, with each point checked against the actual document, drawings and specification.
- Verification is mandatory. The Office states that due diligence is mandatory and that all outputs must be critically reviewed by a qualified human professional using primary sources.
For the safeguards the main text attaches to each use, see the article on permitted uses and prohibited uses, and for the risks and the oversight rule see scope, terms and risks.
Lessons for applicants and searchers
- Search. If you use AI to generate search terms, treat them as a starting vocabulary and then test them against the claim language and the classification. Record which terms you excluded and why.
- Classification. Check suggested codes against the official scheme and the claims; broad codes lead to noisy results.
- Mapping. If you prepare a claim chart, verify every row against the cited document, as the Office expects its officers to.
- Clarity and sufficiency. An AI list of "gaps" in your own specification can point to real weaknesses, such as a range without a stated basis. Fix them in the specification before filing; do not rely on the AI view of the law.
- Citations. Do not cite any case, or any passage from a case, that you have not read in the source.
A worked example
Lotus Filtration Pvt Ltd asks an AI tool for search terms for its claim on a ceramic membrane. The tool returns forty terms, among them reverse osmosis and desalination, which belong to different technologies. The searcher keeps the ceramic, pore-size and filtration terms, sets aside the drifting ones, tests the rest against the claim's wording and the class suggested by the tool, and corrects two codes by checking the classification scheme. The final report states which sources were searched and which terms were left out, and every cited document is read in full.
Common lapses
- Trusting a classification suggestion without comparing it with the scheme.
- Using an AI feature list for a novelty comparison without re-reading the claim.
- Quoting a decision from an AI summary.
- Treating one AI answer on novelty as the answer.
- Pasting an unpublished specification into a public tool.
Need help with a prior art search?
A search report is only as reliable as its method and its citations. If you want a documented search, with every reference verified, speak to our patent search and prior art team.
Key takeaways
- Annexure I gives eleven worked uses showing where AI helps and where it misleads.
- Output varies by model, prompt and conditions, and may not be reproducible.
- Fluent answers can be wrong; verify every point in primary sources.
- Use AI only for preliminary work, never for the final judgment.
- The tools are not named, and the examples are not prompt advice.
Read next
- AI in patent examination: scope, terms, risks and human oversight
- AI in patent examination: permitted uses and prohibited uses
- Chapter 9: reference to the examiner and novelty
- Chapter 9: sufficiency of disclosure and unity of invention
Disclaimer: Based on the manuals and guidelines published by the Office of the Controller General of Patents, Designs and Trade Marks that are named in the article, as consulted on 4 October 2026. They are guidance and do not have the force of law; the Patents Act, 1970 and the Patents Rules, 2003 as amended (including the 2024 amendment rules) prevail, and the current versions on ipindia.gov.in should be checked. This article is general information, not legal advice; check the official text before acting.