Translation is one of the smaller line items in a global patent filing. It is also one of the most consequential.
A patent filed in ten jurisdictions may have been translated into ten different languages, each with its own legal conventions and its own quality risks, and any one of those translations can come into question if a competitor decides to challenge the filing later.
The good news: AI translation has made the process faster and cheaper.
The bad news: It has also introduced new ways for translations to fail and cause great risk.
One of the central operational questions for IP teams now is how to use AI in patent translation without increasing risk. In this article, we’ll walk through how to think about that.
Patent Filing Volume Is Forcing the AI Question
Both the volume and the geographic location of patent activity have shifted. The World Intellectual Property Organization (WIPO) recorded 3.7 million patent applications worldwide in 2024, the fastest year-on-year growth since 2018. More than 70% of those applications were filed at offices in Asia, up from 60% a decade ago.
Cross-border patent activity almost always involves translation at some stage, whether it is to file an application in a foreign jurisdiction, prosecute an existing filing, search prior art, or evaluate freedom to operate. For IP teams, more international filings mean more translation work, more language combinations, more to manage, and higher expenses.
Pure human translation cannot keep pace with that volume on its own. In recent years, AI translation has moved from an experimental option to a working solution for most IP teams, even if only for prior art research or freedom-to-operate searches. The question now is where it fits and where it does not, particularly for the filings that carry the highest stakes.
Jennifer Winfield, Director of Localization Program Management at BIG Language Solutions, has watched this conversation shift in real time across her client base.
“Almost every conversation we are having with IP teams at the moment touches on AI in some way. Clients are not really asking us whether they should be using it anymore. They are asking how to use it appropriately, particularly for content where the stakes are high, and where to draw the line between content that is suitable for an AI-assisted workflow and content that is not.”
The 3 Problems IP Teams Should Watch For When Using AI to Translate Patents
Your patent claims define the legal scope of an invention. Every word in a claim is operative, which means a single shift in a conjunction, qualifier, or technical term can narrow your protection or open the door to invalidity challenges.
Three issues come up most often when you use AI to handle patent text without proper guardrails:
- Inconsistent terminology: General-purpose AI translation often renders the same term differently across sections of the same document, or even within a single paragraph. In ordinary content, this reads as a stylistic quirk. In a patent, two different renderings can suggest two different concepts. The result can be a claim that has been narrowed or broadened in ways the applicant never intended.
- Imprecise claim transition phrases: The phrases “comprising,” “consisting of,” and “consisting essentially of” carry distinct legal meanings. “Comprising” is open-ended; “consisting of” is closed. AI systems do not inherently understand these distinctions and can substitute one for another, with significant consequences for what the claim actually covers.
- Mistranslations of new terminology: Inventions in fast-moving fields like AI, biotech, and advanced materials often contain terms with no established translated equivalent. AI translation systems can struggle to render these terms reliably, particularly when no human reviewer with subject-matter expertise is involved.
The reliability of AI translation also varies significantly by language pair. Research has consistently shown that machine translation quality is uneven across languages, with Chinese-English patent translation historically preserving less disclosure content than major European-language pairs.
So, for IP teams filing into China, one of the highest-volume patent jurisdictions in the world, that performance variability is a crucial consideration.
Jennifer’s team reviews AI-translated patent content regularly. She says, “The errors that cause us the most concern are not always the obvious ones. AI output can read very fluently and still contain a problem that only someone with the right domain expertise will catch. A different word for the same concept used in two different sections, or a transition phrase that subtly changes the scope of a claim, are exactly the kinds of issues that can lead to office actions later in prosecution.”
The Risk Most IP Teams Underestimate: Confidentiality
Patent filing is one of the most data-sensitive processes in legal practice. Pre-filing technical details, draft claims, inventor information, and strategy notes all need strict controls over who can access them, where they are stored, and how they are handled downstream.
But free and consumer-grade AI translation tools do not provide those controls. Worse, many of these platforms retain user inputs and use them to train future model versions.
The Institute of Professional Representatives before the European Patent Office (EPI), the professional body for European patent attorneys, addressed this directly in 2024. Its guidelines on the use of generative AI state that if there is doubt about whether an AI model will maintain adequate confidentiality of input data, the model should not be used. That standard rules out most consumer-grade AI translation tools (like ChatGPT or Google Translate). However, there are a number of professional-grade tools with the appropriate safeguards.
Jennifer’s team handles data governance for the IP teams we work with. But for teams making these decisions on their own, she offers the following advice: “If a team is going to use a free or consumer AI tool on patent content, they need to understand what happens to their input, whether it is retained, and whether it is used for training. If you can’t get a definitive answer or the tool doesn’t keep your data safe, it’s not worth the risk. Professional-grade tools like ours eliminate that risk.”
Where MTPE Fits Into a Patent Workflow
Machine translation post-editing, or MTPE, is a workflow in which an AI engine generates an initial translation, and a qualified human reviewer then edits the output for accuracy, terminology, and quality. The combination delivers the speed of AI without sacrificing the judgment of a qualified linguist.
MTPE adoption has grown quickly across the language services industry, rising from 26% in 2022 to roughly 46% in 2024.
In a patent context, MTPE works well when three conditions are in place.
- The MT engine is patent-specialized and secure: Not all AI translation is created equal. A consumer-grade tool trained on the open internet is wholly different from a proprietary engine trained on validated patent corpora and operated in a closed environment. For unpublished patent content, only the second category should be on the table.
- Post-editors are domain experts: Bilingual fluency alone is not enough. A post-editor working on a pharmaceutical patent needs to understand the chemistry. One working on a software patent needs to understand the technology. They also need to understand patent claim conventions in the target jurisdiction, which differ meaningfully across the USPTO, EPO, and other major offices.
- A patent attorney reviews claims before filing: This is the step that catches what neither AI nor a post-editor reliably will: jurisdiction-specific legal precision in claim language.
Lower-stakes translation use cases like prior art research, freedom-to-operate searches, and internal knowledge gathering may not require the full workflow. But for any translation intended for official filing, in-country patent attorney review is the appropriate standard, not a value-add.
Jennifer’s team works through this kind of decision with clients regularly.
“There are filings where we will recommend full human translation over MTPE, even though MTPE would be faster and less expensive. A pioneering invention in a contested technical field, a high-value claim that is likely to face opposition, a translation into a language pair where AI quality is known to be weaker — those are the situations where the speed and cost advantages of MTPE do not outweigh the additional risk. Clients tend to appreciate that distinction once we walk them through it.”
How BIG Language Solutions Approaches AI in Patent Translation
Every IP team’s situation is different. Content type, language pairs, jurisdiction mix, internal review capacity, and risk tolerance all shape what the right workflow looks like. BIG Language Solutions works with clients to think through those variables and to build a patent translation approach that fits.
The workflow itself combines patent-specialized machine translation, expert post-editing by linguists with advanced technical degrees in their fields, and a four-step QA process that includes in-country patent attorney review for every official filing. We also offer a fully human translation service for clients who require it, and an AI-assisted option for clients who want speed without giving up oversight.
Our proprietary IPVault™ provides the structural layer underneath: secure document handling, automated deadline tracking, instant quoting, and full audit trails so IP teams can see where every translation sits at every stage. The platform supports the workflow without dictating it, which means clients can choose the approach that matches each filing.
If you are figuring out where AI fits in your own filing process, talk to our patent translation experts. We will walk through your content, your jurisdictions, and your risk tolerance, and help you build a workflow that protects your filings.
