By Julius Melnitzer | December 22, 2025
“More things are becoming potentially discoverable in trademark searches, and faster, because of how interconnected we are and how much we live our lives, and conduct our commerce, over the internet” — Tamara Céline Winegust
Artificial intelligence (AI) is turning trademark (TM) watching from a tedious necessity to a strategic advantage for rights holders.
Typically, lawyers are on the tail-end of trademark (TM) watching, be it clearance, market, or enforcement searches.
“Our job is to take the information, form judgments, and advise the clients,” says Tamara Céline Winegust, a Principal in the Toronto office of Smart & Biggar, part of the IPH network. “How the hits are identified is outside our control, and the bulk of the results are filtered by the clients before they get to us.”
Indeed, most law firms outsource TM watching to specialised third-party providers who have access to tools that enable monitoring of thousands of marks across jurisdictions, languages, and classes.
“We rely on the service providers because that’s the most cost-effective and efficient way to do the searches,” Winegust says.
But that doesn’t mean TM watching hasn’t evolved.
“More things are becoming potentially discoverable in trademark searches, and faster, because of how interconnected we are and how much we live our lives, and conduct our commerce, over the internet,” Winegust notes.
Much as technological advancement is responsible for this connectivity, so is its offspring, AI, responsible for the transformation of TM watching.
“Service providers are advertising AI capability that make searches faster and more accurate,” Winegust says. “They’re also advertising ranked results and appearance searches.”
Traditional search methods relied on manual scans, design code matching, and keyword filtering, all of which were slow, prone to error and required intensive resources.
AI tools, by contrast, may use image recognition based on machine learning to detect figurative similarities and interpret logos, stylised text, and visual patterns.
AI tools may also help catch more “sound-alikes”, misspelling and alternate spellings, and related goods or services. As well, they can make filtering easier, ranking filings in such categories as likelihood of confusion, applicability of legal precedent, and market relevance.
To be sure, trustworthy service providers need to train, improve, and update their AI engines on datasets that include TM registers, case law, and historical filings that become the basis for recognizing similarities and conflicts, and generating alerts. They also rely on agreements and relationships with TM offices around the world to ensure information is accurate and current.
The upshot is that when such proprietary datasets combine with machine learning and are able to scan the world’s TM databases continuously and in seconds, they can facilitate real-time, more relevant alerts that are far preferable to time-lagged manual checks.
What this means is that threats are detected earlier, enabling faster conflict resolution and reducing the risk of litigation. On the proactive side, the insights derived from real-time data can help brand owners make decisions about market entry and expansion.
AI tools can also offer predictive analysis, estimating the likelihood of registration approval and the probability of opposition by analyzing historic decision patterns and identifying similar rejected applications. They can examine products and recommend which international classes are best for filing.
Indeed, observers have estimated that AI searches can reduce research time by 90 per cent, reduce TM clearance costs by up to 75 per cent, and cut application rejection rates by more than 50 per cent.
In one example involving Houston-based fintech startup PaySphere, AI-powered clearance identified three high-risk conflicts in 15 minutes (compared to eight hours for the average manual search), detected a phonetic conflict with a Brazilian trademark, and recommended an alternative trademark with clearance probability of 82 per cent.
The extent to which AI has infiltrated the TM search world is evident from the extent of the United States Patent and Trademark Office’s adoption of the technology. The agency uses AI-based specimen checks to detect fraudulent submissions, smart filters to root out duplicate or suspicious applications, and AI classification tools to assign proper filing codes.
All this having been said, AI-driven TM searches share typical AI limitations:
- Lack of legal judgment, which the pattern recognition that AI excels at does not replace;
- Lack of contextual judgment: unlike live experts, AI struggles to interpret consumer perception, industry differences, and actual likelihood of confusion;
- Reliance on data quality;
- Cultural and linguistic sensitivity: AI can miss cultural nuances or negative connotations;
- Susceptibility to false positives: AI can generate huge volumes of irrelevant alerts; and
- Over-reliance: IP offices and courts still expect human validation of AI outputs.
Ultimately, AI in TM searches, as in its applications generally, is an enhancement of, not a replacement for, human judgement, interpretation, and intervention. Put another way, AI supports, but does not replace, human decision-making.
Still, this much is clear: while TM searches have always been an integral and essential tool for diligent rights holders, their enhanced AI capabilities have turned them into a potentially strategic asset.
As Kathi Vidal, formerly the Under Secretary of Commerce for Intellectual Property and Director of the USPTO from April 2022 to December 2024, told Bloomberg Law in January 2025: “AI adoption in trademark practice isn’t coming—it’s already here. Firms leveraging these tools gain significant competitive advantage in brand protection.”
Tamara Céline Winegust is a Principal at Smart & Biggar who specialises in trademark and brand protection. More information.
Julius Melnitzer is a Toronto-based legal affairs writer, ghostwriter, writing coach and media trainer. Readers can reach him at julius@legalwriter.net or on his website.
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