New AI Promises to Find Patent Violators in Minutes, Not Months

CHEYENNE, Wyo. – July 23, 2026 – For decades, the world of patent enforcement has operated on a starkly uneven playing field. An individual inventor or a small company might hold a brilliant, world-changing patent, but lack the resources—the teams of lawyers, the millions in budget—to find out if a corporate giant is using their idea without permission. The process of proving infringement has been a war of attrition, a slow, expensive grind of statistical analysis and manual searches that often ends before it truly begins. You might know you have a good idea, but you’ll never know if it’s being used.

Today, a Wyoming-based company, ClaimHit LLC, argues it has built a new kind of slingshot for these Davids. The company has launched V3 of its discovery engine, a platform it claims can accomplish in minutes what has traditionally taken legal teams weeks or months: finding real products that appear to infringe on a specific patent. The tool was put to a dramatic test this week at IPBC Global in San Diego, the patent industry's premier conference, where skeptical licensing executives were invited to run their own patents through the system live. The results, according to the company, have led many of those same executives to sign up for trials.

From Weeks of Guesswork to Minutes of Evidence

The traditional first step in a patent licensing campaign is a dry, academic exercise. A team pores over a portfolio, running statistical models. They count forward citations, measure the breadth of patent claims, and weigh the size of the patent family. The result is a ranked list of “maybes”—patents that, on paper, look the most promising. But as any veteran of the patent wars will tell you, a high score means nothing without evidence of actual use in the marketplace. Finding that evidence is the real work, and it's where the process bogs down.

ClaimHit V3 is designed to skip the guesswork and go straight to the evidence. Instead of ranking patents, it ingests them—either one at a time or by the hundreds—and scours the digital world for products that match the patent's specific claims. The engine simultaneously searches the open web, product specification libraries, academic citation records, and competitor data, using what it calls a suite of “frontier models” working in concert.

For users analyzing an entire portfolio, the results populate a grid the company calls the “Hit Matrix.” Patents are listed across the top, and potential infringing companies down the side. As the search runs, cells fill in, creating an instant visual map of potential licensing opportunities. “One company lights up across ten of thirteen patents, and that is a conversation worth having,” the company’s launch materials explain. A patent that stays empty, by contrast, is quickly identified as a dead end, saving countless hours and dollars that would have been spent on fruitless analysis.

This shift from abstract scoring to concrete evidence represents a fundamental change in workflow. It’s a move away from asking “Which of my patents is statistically strongest?” to answering the only question that truly matters: “Is anyone actually using my invention, and can I prove it?”

Building Trust in AI for High-Stakes Law

In the current tech climate, any claim of an “AI” solution is met with a healthy dose of skepticism, especially in the legal field where a single fabricated fact can destroy a case and a career. The phenomenon of AI “hallucinations”—where a model confidently invents sources, products, or citations—is a well-documented risk. It’s a risk that ClaimHit’s founder, Bikram Singh, says his team built their entire system to eliminate.

“It is a fair question,” Singh said, acknowledging that the first thing most people asked him in San Diego was whether his tool was just a simple wrapper around a commercial chatbot like ChatGPT or Claude. “A wrapper hands your question to a chatbot and gives you back whatever it says. If you ask a general model to find products close to a claim, it will answer with confidence, and some of what it gives you will not be real.”

Singh explained that his platform works the opposite way. The AI models are just one part of a larger system governed by two rigid checks. First, the engine finds potential products through six different channels. Second, it runs two critical verifications: one confirms every potential product against a real, retrievable source, and the other meticulously checks if the product actually meets the specific requirements of the patent’s claims, not just the general subject matter. Anything that fails is discarded.

To prove it, the platform offers a level of transparency rarely seen in proprietary software. It provides users with a complete record of its search, showing not only the products it flagged as potential infringers but also every product it considered and rejected, along with the specific reason for each rejection. “In a negotiation or in court, an invented citation is not a small mistake. It costs you your credibility,” Singh stated. “We built the whole system so that cannot happen.” This focus on verifiable, auditable results aims to build a foundation of trust in a field where the truth is paramount.

Leveling the Playing Field

The most profound impact of this technology may lie in its potential to democratize patent enforcement. The competitive landscape for patent analytics is dominated by established players like Clarivate, LexisNexis IP, and PatSnap, whose powerful but often costly platforms are primarily geared toward large corporate legal departments and major law firms. By drastically reducing the time and, presumably, the cost of initial infringement analysis, ClaimHit V3 could empower a much broader spectrum of innovators.

The company explicitly targets not just large corporations, but also patent brokers, mid-size companies, and, most notably, individual inventors who “until now could rarely afford even to find out whether anyone was using their invention.” If the platform delivers on its promise, it could fundamentally alter the power dynamics of intellectual property. An inventor could potentially identify infringers and generate a detailed claim chart—the foundational document for any licensing negotiation or lawsuit—in an afternoon, armed with the kind of evidence that previously required a prohibitive investment.

The live demonstrations in San Diego provided an early test of this potential. According to Singh, the results were telling. Some executives saw the tool identify, in minutes, the same infringing companies their own legal teams had spent weeks finding. Others saw a company they had never thought to connect to their patent before. “Someone types in a patent they have lived with for years, and they know in seconds whether what comes back is real,” Singh said. For a tool being judged by people who know their own intellectual property better than anyone, the fact that many are now in trials may be the most significant verdict of all.