IPR.01
Prior-Art Search
Semantic prior-art search over patent and non-patent literature, each hit with its matching passage mapped to claim elements, validated on closed matters. The novelty opinion stays the attorney’s.
Open pageIndustry
AI for patent work,
with the attorney signing.
Search and draft faster. An attorney signs. Prior-art search, drafting support and portfolio monitoring for patent and trademark firms, with unpublished inventions kept confidential and a qualified attorney responsible for everything filed.
Last reviewed 30 October 2026 · Engineering guidance, not legal advice.
In context
AI patent tools help IP firms search prior art by meaning rather than keywords, draft the routine parts of a specification, and watch portfolios and trademark registers for conflicts. Claim strategy, legal judgement and the signature on a filing stay with a qualified attorney.
Where it breaks today
01
Keyword search misses the art
The closest prior art rarely uses the applicant’s words. Boolean strings and classification codes in Espacenet or PATENTSCOPE catch what was phrased the same way and miss the paper that describes the same mechanism in different terms, which is exactly the one an examiner finds.
02
Confidentiality comes first
An invention disclosure is among the most sensitive documents a firm holds. Pasting it into a consumer chat tool with unclear retention and training terms is a professional risk no firm should take, so many firms do not use AI at all.
03
Drafting tools overreach
Generators that produce a whole application from a disclosure produce text that reads well and claims badly. Claim scope is strategy, and an attorney who has to unpick a generated draft saves no time.
OUR APPROACH
Faster research, unchanged responsibility
The systems we build for IP firms speed up the research and the routine writing and leave the legal work where it belongs. Search returns ranked documents with the passage that matched, so the attorney judges relevance from the source. Drafting support writes the parts that follow from decisions already made, background, figure descriptions, consistent terminology, and leaves claims to the attorney. Monitoring surfaces candidates for opposition or renewal and never files anything.
Patent offices and courts have been consistent that an inventor must be a natural person, and that the practitioner who signs a filing is responsible for its content whatever tools were used. We design to that reality. Confidential material is processed on infrastructure with no training on your data and no retention beyond the task, or on models you run yourself, and every output is traceable to its sources.
HOW IT DEPLOYS
PHASE 01
Test on closed matters
We run search against past matters where the cited art is known, and measure whether it would have been found, before any live disclosure is involved.
PHASE 02
Deploy inside your boundary
Confidential processing on terms you approve or on infrastructure you control, with matter-level access and a log of every query.
PHASE 03
Fit the docket
Connect to the docketing and document systems your attorneys already use, so results land in the matter rather than in another tool.
What we build
01
Prior-art search
AI patent search across patent and non-patent literature by meaning, with the matching passage, claim mapping and the queries used, so a search report can be checked and repeated.
SEE PRIOR-ART SEARCH →02
Patent drafting support
Specification sections, figure descriptions, terminology consistency and support checks between claims and description, for the attorney to edit. Claims stay the attorney’s.
SEE DRAFTING SUPPORT →03
Portfolio and trademark monitoring
Watching new filings and registers for marks and patents that may conflict with a client’s portfolio, with each candidate explained for an attorney to assess.
SEE PORTFOLIO MONITORING →04
Confidential retrieval
Retrieval over the firm’s own precedents, prosecution histories and office-action responses, so past work is findable without leaving the firm’s systems.
SEE RAG SYSTEMS →Controls we implement
NO TRAINING ON DISCLOSURES
MATTER-LEVEL ACCESS
SELF-HOSTED MODELS AVAILABLE
EVERY RESULT CITED TO SOURCE
QUERY LOG PER MATTER
NOTHING FILED AUTOMATICALLY
These are engineering controls we build into the architecture, not certifications. QQuantum.ai holds no certifications, and your professional conduct obligations remain yours — see our trust centre.
Ip use cases
-
-
IPR.02
Patent Drafting Support
Description, embodiments and figure text drafted around claims the attorney has settled, with claim-support and terminology checks. Claims and strategy are never generated for you.
Open page -
IPR.03
Portfolio & Trademark Monitoring
Trademark and patent watching that compares new filings by look, sound, meaning and goods, explaining each likely conflict so an attorney decides on opposition while the window is open.
Open page
Questions
Questions about IP & Patent Firms.
Will AI replace patent attorneys?
Not in any foreseeable form. Claim strategy, responding to an examiner, judging infringement risk and advising a client are legal judgements, and the practitioner who signs a filing is responsible for it regardless of the tools used. What AI changes is the research and routine writing around that work, which can take a large share of an attorney’s hours.
Can AI be named as an inventor on a patent?
No. The UK Supreme Court, the European Patent Office and the US courts have each held, in the DABUS cases, that an inventor must be a natural person. AI-assisted inventions can be patented when a person made a qualifying contribution, and how inventorship is assessed in AI-assisted work is a question for the attorney on each matter.
Is it safe to put an invention disclosure into an AI tool?
Not into a consumer tool with unclear retention or training terms. A confidential disclosure should only be processed under terms that forbid training and retention, with access limited to the matter, or on models the firm runs itself. We build both ways; which meets your professional obligations and your client’s instructions is your decision.
What are the best AI tools for patent search?
Free semantic search, such as Google Patents and open projects like PQAI, is a reasonable starting point, and commercial platforms add coverage and analytics. A custom system makes sense when a firm wants search over its own precedents as well as public databases, needs confidential handling it controls, or wants results delivered into its own docketing and reporting.
Can AI draft a patent application?
It can draft parts well: background, figure descriptions, alternative embodiments the attorney specifies, and consistency checks between claims and description. Generating a complete application from a disclosure tends to produce fluent text with poorly judged claim scope. The systems we build draft around the attorney’s claims, not instead of them.
