NEWS
Federal Circuit to AI Innovators: Generic Machine Learning Isn’t Enough for a Patent
In a closely watched decision, the Federal Circuit just made it clear: simply applying machine learning to a new domain—without improving the ML itself—won’t get you a patent.
In Recentive Analytics, Inc. v. Fox Corp. (No. 2023-2437, Apr. 18, 2025), the court affirmed the dismissal of four patents held by Recentive Analytics, all centered on using machine learning for TV broadcast scheduling and event planning. While the decision acknowledges the growing importance of AI, it draws a hard line on what qualifies as patent-eligible under 35 U.S.C. § 101.
What Was at Stake?
Recentive’s patents fell into two categories:
- Machine Learning Training Patents: Claimed systems for dynamically generating optimized live event schedules based on historical data.
- Network Map Patents: Targeted how television programs are assigned to channels across regions and times.
Their core idea? Replace manual, static scheduling with real-time, dynamic optimization using machine learning.
The Court’s Message: That’s Not Enough
The court emphasized that generic machine learning methods are “abstract ideas” unless they come with a specific technological improvement. Simply applying ML to a traditionally manual task, like TV scheduling, doesn’t cut it. In the court’s own words:
“Patents that do no more than claim the application of generic machine learning to new data environments… are patent ineligible under § 101.”
Why the Patents Failed
Here’s a breakdown of why the court ruled against Recentive:
- No New ML Tech
Recentive admitted it didn’t invent a new ML algorithm—it just applied existing methods to a new problem. The court saw this as no different than saying “do it with AI.”
- No Implementation Details
The patents didn’t explain how the ML models worked or improved the technology. They lacked any specific steps or mechanisms.
- Just a Field of Use
The court rejected the idea that applying ML to broadcasting makes it patent-worthy. Limiting an abstract idea to a specific industry doesn’t make it less abstract.
- Speed ≠ Innovation
Making a process faster or more efficient—especially with computers—doesn’t make it patentable. This principle has been upheld in multiple cases, and it applied here too.
- No Inventive Concept
Even at step two of the Alice framework (used to assess patent eligibility), the court found nothing “significantly more” than the abstract idea of using ML in scheduling.
Why This Matters for AI Patents
The Federal Circuit drew a clear line in the sand: if you’re using AI or machine learning, you need more than just an idea. You must show a specific, concrete technological improvement—not just automation of a manual process or use in a new domain.
The decision leaves open the possibility that patents could be granted for innovations that actually improve machine learning techniques. But just saying “we used AI” isn’t enough.
Final Thought
This case is a reminder that as AI becomes more integrated into various industries, patent law is holding firm on its standards. Innovators in machine learning must go beyond applying known techniques—they must push the technology itself forward if they want protection.
The Fintiv Pendulum Swings Back — What Patent Litigators Need to Know
In a move that’s sending ripples through the patent world, Acting USPTO Director Coke Stewart has officially rescinded the June 2022 Vidal Memorandum. That memo had put significant limits on the PTAB’s ability to issue discretionary denials of inter partes review (IPR) petitions — especially in cases where there was parallel district court litigation. With its repeal, we’re likely to see a major uptick in those denials as the PTAB regains broader discretion.
So, what does this mean for patent stakeholders?
Essentially, the PTAB can once again lean heavily on the Fintiv framework — a set of six factors that guide whether an IPR petition should be denied when there’s overlapping litigation in district court. These factors consider things like how soon a district court trial is scheduled, how much investment has already gone into the case, and whether the same issues are being raised in both forums. In short: if a case is already moving forward in court, the PTAB might decide it’s better not to get involved.
This isn’t the first time we’ve seen this pendulum swing. Under former Director Kathi Vidal, the USPTO introduced guardrails that made it easier for petitioners to avoid discretionary denials — especially by submitting a Sotera stipulation (which limits their district court defenses to avoid duplication) or showing “compelling evidence” of unpatentability. Those rules made the IPR pathway more predictable and accessible.
But with those protections now gone, both patent owners and petitioners will need to adjust their strategies. Patent holders are likely to push their district court cases forward faster, using quick trial dates to argue against IPR institution. Petitioners, on the other hand, may need to file IPRs earlier and consider requesting early stays in court to minimize Fintiv-related risks.
The takeaway? PTAB policy is, once again, in flux — and those navigating patent disputes need to stay agile. The rules around discretionary denials may be shifting, but one thing remains clear: timing, coordination, and strategy have never been more critical.
Federal Circuit Clarifies Limits on Prosecution Disclaimer Across Patent Families
- Differentiate claim language across related patents to reduce the risk of unintended claim limitations.
- Be mindful of statements made during prosecution and IPR, ensuring they are not overly broad or ambiguous.
- Recognize that silence is not necessarily acquiescence, meaning there may not always be a need to contest an examiner’s reasons for allowance.
The Harsh Reality of § 101 Appeals: Why Fighting a Rejection at the PTAB Is an Uphill Battle
For inventors and patent practitioners, securing a patent has always been a challenge—but when it comes to overcoming a § 101 rejection, the odds are stacked against applicants. The latest data from 2023 confirms a troubling trend: the Patent Trial and Appeal Board (PTAB) upheld examiner § 101 rejections a staggering 91% of the time. This marks a steady increase from 87.1% in 2021 and 88.4% in 2022, making it increasingly clear that appealing a § 101 rejection is more likely to end in disappointment than success.
Why Is the PTAB So Tough on § 101 Appeals?
The high affirmance rate isn’t just a reflection of weak applications being weeded out—it’s a symptom of a broken system. Ever since the Supreme Court’s Alice Corp. v. CLS Bank decision, the framework for determining patent eligibility has been a mess. The Federal Circuit has issued conflicting rulings, and the PTAB itself often ignores the USPTO’s own guidelines. This inconsistency has led to bizarre decisions, including rulings that a diamond-encrusted drill bit and a camera phone were somehow “abstract ideas.”
In practice, this legal chaos means that applicants face unpredictable outcomes, with decisions often feeling arbitrary rather than based on sound legal principles.
Where Do § 101 Appeals Get Hit the Hardest?
Looking at the numbers, it’s clear that some USPTO Technical Centers (TCs) are far harsher than others when it comes to § 101 rejections.
- TCs 3600 & 3700 (Business Methods & Financial Tech): These have the worst outcomes, with affirmance rates exceeding 95%. Since business method patents frequently land in these TCs, inventors in these fields face particularly steep odds.
- TC2100 (Computing Technologies): The affirmance rate here climbed to 85% in 2023, up from 80% in 2022. This suggests that even general software patents—not just business method patents—are struggling more than ever.
- Other TCs: While some technical centers have lower affirmance rates, there isn’t enough data to draw strong conclusions. That said, TCs 2400 and 2600 may offer more favorable outcomes for software-related applications.
What Types of “Abstract Ideas” Get Rejected Most Often?
The PTAB’s reasoning for § 101 rejections usually falls into three main categories:
- Mathematics (17% of affirmances)
- Mental Processes (48%)
- Methods of Organizing Human Activity (68%)
The overlap in these categories means that some applications get rejected for multiple reasons. Mental processes are the most common issue in TC2100, while TC3600 predictably has the highest percentage of “organizing human activity” rejections due to its focus on business methods.
The Hidden Danger: PTAB’s Surprise § 101 Rejections
If you think your appeal is safe because your examiner didn’t issue a § 101 rejection, think again. The PTAB has the power to introduce a new § 101 rejection, even if it wasn’t raised during examination. In 10% of cases, the PTAB issued fresh § 101 rejections, catching applicants off guard. This risk is especially high in TC2100, where examiners tend to be more lenient with § 101, only for the PTAB to later step in and shut down applications.
What’s the Best Strategy Moving Forward?
With such daunting statistics, appealing a § 101 rejection is rarely the best course of action. Instead, applicants should:
- Work with the examiner to amend claims and find a path to allowance, rather than immediately pursuing an appeal.
- Use TC steering tools to predict where an application will be assigned. Avoiding TC3600 and TC3700 can dramatically improve chances of success.
- Be prepared for an uphill battle if a § 101 appeal is unavoidable, and consider all possible claim amendments before taking the risk.
Final Thoughts
The PTAB’s 91% affirmance rate for § 101 rejections in 2023 is a wake-up call for inventors and patent attorneys. The current system is unpredictable, inconsistent, and incredibly challenging to navigate. While legislative or judicial reform may be necessary to fix the problem, for now, the best approach is to strategize early in prosecution—because once an application lands at the PTAB, the chances of success are slim.
AI and Copyright: Court Rules Against Fair Use in Training AI Models
On February 11, 2025, the U.S. District Court for the District of Delaware delivered a landmark decision in Thomson Reuters Enterprise Centre GmbH v. Ross Intelligence Inc., ruling that the use of copyrighted materials to train AI models does not qualify as fair use. This ruling represents a significant development in the ongoing legal debate over AI and intellectual property rights.
Background of the Case
Thomson Reuters, the owner of the legal research platform Westlaw, provides users with legal texts, statutes, and editorial content such as headnotes that summarize key aspects of cases. Westlaw also employs a proprietary classification system called the ‘Key Number System’ to organize legal materials.
Ross Intelligence, an AI-powered legal research platform, sought to develop a competing tool and initially attempted to license Westlaw’s data for training purposes. When Reuters declined, Ross turned to a third-party company, LegalEase, which compiled ‘Bulk Memos’—legal questions and answers based on Westlaw’s headnotes. Ross then used these memos to train its AI system.
Reuters filed a lawsuit in May 2020, alleging copyright infringement, arguing that Ross used proprietary Westlaw content without authorization to build its AI-driven research tool.
Court Ruling and Rejection of Fair Use
Judge Stephanos Bibas granted Reuters’ motion for partial summary judgment, rejecting Ross’s fair use defense. The court applied the four-factor test under U.S. copyright law:
- Purpose and Character of the Use: The court found Ross’s use to be commercial and non-transformative. Ross used Reuters’ copyrighted material to create a directly competing product, and the AI training process did not add a new or distinct purpose.
- Nature of the Copyrighted Work: This factor slightly favored Ross, as the Westlaw headnotes, while creative, contain factual elements.
- Amount and Substantiality of the Work Used: The court ruled in Ross’s favor on this factor since the headnotes were only used in training and not displayed to end-users.
- Market Effect: The ruling heavily favored Reuters, as Ross’s tool directly competed with Westlaw, threatening its market share. The court emphasized that Ross could have developed its AI training data independently or through licensed sources.
Given the emphasis on the fourth factor, the court ruled against Ross, stating that fair use does not protect the unauthorized use of copyrighted content for AI training.
Implications for AI and Copyright Law
This ruling is a major victory for copyright holders, affirming their rights over proprietary content and potentially opening new licensing revenue streams. On the flip side, AI developers now face stricter limitations on sourcing training data, emphasizing the need to either use non-copyrighted works or obtain proper licensing agreements.
However, this decision is not the final word on AI copyright disputes. Judge Bibas explicitly noted that Ross’s system was not a generative AI tool but rather a legal search engine. Future cases involving generative AI may see courts take a different stance, particularly on the transformative use factor.
As AI continues to evolve, the legal landscape will likely shift. This case sets a precedent but leaves open questions about how courts will treat AI-generated content moving forward. For now, AI companies should tread carefully, ensuring their training data complies with copyright laws to avoid similar litigation battles.
Federal Circuit Reverses ITC Decision, Strengthening Patent Eligibility for Composition Claims
The Federal Circuit has issued a significant ruling in US Synthetic Corp. v. Int’l Trade Comm’n, reversing the ITC’s controversial decision that had invalidated composition of matter claims as abstract ideas. This case provides an important clarification on patent eligibility under Section 101 and limits the expansive application of the abstract idea doctrine in composition claims.
The Case at a Glance
US Synthetic Corp. (USS) had patented polycrystalline diamond compacts (PDCs) used in drill bits, with claims defining the PDCs by their material properties, such as coercivity and thermal stability. The ITC had determined that these claims were abstract because they described the PDCs through their functional properties rather than specific manufacturing steps. This decision faced criticism from industry groups, including PhRMA, for expanding the abstract idea analysis into composition claims, a move that could have undermined long-standing patent protections.
The Federal Circuit’s Reversal
Writing for a unanimous panel, Judge Chen rejected the ITC’s reasoning, affirming that the claimed material properties were concrete and measurable rather than abstract. The court emphasized that these properties are inherently tied to the PDC’s physical structure, stating that they are “integrally and necessarily intertwined” with the composition itself. In contrast to the ITC’s position that these characteristics were mere “side effects” of manufacturing, the Federal Circuit ruled that they meaningfully define the PDC’s structure and composition.
A key takeaway from the decision is that defining a composition of matter by its properties is distinct from claiming an abstract idea. While functional limitations alone may be problematic in software patents, in the realm of chemistry and materials science, such properties often provide crucial insights into the nature of the invention.
Implications for Patent Law
This decision preserves decades of precedent allowing composition claims to be defined by their physical and material properties. The ruling clarifies that:
- Material properties can be valid claim limitations: When properties correlate with structure, they can serve as legitimate definitional elements rather than abstract concepts.
- A perfect correlation is not required: The court acknowledged that while no property is a perfect proxy for a composition’s structure, a reasonable correlation is sufficient to support eligibility.
- Patent validity includes eligibility: The court reaffirmed that issued patents are presumed valid, including under Section 101, and criticized the ITC for imposing an incorrect burden of proof on USS.
A Narrow but Important Ruling
While this ruling provides reassurance for those in chemical and material sciences, it does not offer much relief for software-related patent eligibility. The court explicitly distinguished this case from software claims, noting that software’s functional limitations are often untethered to physical structures.
Additionally, the decision leaves open the broader question of when, if ever, a composition of matter claim could be considered an abstract idea. The court provided guidance by example rather than establishing a strict test, maintaining flexibility in future cases.
Final Thoughts
The Federal Circuit’s reversal of the ITC decision is a win for those who rely on composition of matter patents, particularly in the pharmaceutical and material sciences industries. By reinforcing that material properties can define a composition’s structure, the ruling ensures that such patents remain valid and enforceable. For patent practitioners, this case highlights the importance of clearly explaining how claimed properties relate to structure within the patent specification—a practice that is now more crucial than ever.