天美传媒; Lardner LLP partner Paul Hunter authored the Life Sciences Intellectual Property Review article, “,” exploring proposals under consideration that would alter the U.S. patent system’s fee structure and reshape the country’s innovation landscape.

Hunter describes the potential impact of a value-based patent fee structure for inventors, in contrast to the traditional flat-flee maintenance model, noting it would disproportionately affect smaller innovators who may lack the financial resources to absorb substantial new costs. He also assesses the broader economic and policy implications inherent in such a shift, and the associated legal and political uncertainties.

“Ultimately, while dramatic changes to the U.S. patent fee structure are being discussed, no definitive proposal has been made public, and significant legal, economic, and political barriers remain. The future of value-based patent fees remains uncertain, and any new policy would likely undergo significant revision and face intense scrutiny before implementation,” Hunter concludes. “Stakeholders should monitor developments closely and consider engaging in the public and legislative process to advocate for a fair and effective U.S. patent system.”

Hunter’s article was also published in .

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Introduction: A Radical Shift in IP Policy

The  the Trump administration wants to charge patent holders 1% to 5% of their patent鈥檚 value to maintain the patent after grant. If implemented, this would mark a dramatic departure from the traditional flat-fee model. Framed as a revenue-raising measure, the plan would drastically reshape the U.S. innovation landscape.

Potential Impacts on Innovators

Small innovators would be disproportionately affected by the newly proposed fees. Smaller entities often rely on the patent system to protect their inventions but lack the financial resources to absorb significant cost increases. As a result of these fee changes, many smaller companies may opt to use other avenues to protect their innovations, such trade secrets, or may forego investing in IP protection altogether. 

Depending on how the value of patents is assessed, larger tech firms, while more capable of absorbing the costs, could respond by filing fewer but more strategically valuable patent applications. This could reduce the prevalence of ‘defensive’ filings and low-value patents, potentially streamlining the system. 

Specifically in the biotech and pharmaceuticals spaces, patent protection is critical due to high risks and long timelines associated with product development. The proposed fees could significantly raise the already substantial costs of bringing new products to market. Small biotech firms may find it difficult to protect or license their breakthroughs, potentially leading to reduced investment at early development stages. Medical device manufacturers face similar pressures, where the cost-benefit balance of patenting may shift unfavorably. 

The proposal could also result in industry consolidation, more strategic partnerships, or a shift toward trade secrets鈥攄evelopments that may hinder open innovation and collaboration. The net effect could be a leaner patent landscape focused on higher-quality applications and a reduction in 鈥減atent thickets,鈥� but potentially at the cost of accessibility and innovation diversity.

How Companies Might Adapt

If the proposal moves forward, companies may explore various strategies to mitigate the impact of the new fees. One likely response is increased reliance on trade secrets, especially for software and process innovations that are easier to keep confidential.

Firms may also shift their patenting focus to international jurisdictions with more predictable or affordable IP regimes. Open innovation models, such as defensive publishing or open-source licensing, could gain popularity as a way to prevent competitors from patenting similar inventions.

Patent pooling and consortia may become more common, allowing companies to share the costs of filing and maintaining patents. Additionally, provisional filings could be used more strategically to delay full applications and manage financial risk.

Broader Implications

The proposed fee structure raises broader concerns about equity and U.S. competitiveness. Higher fees could exacerbate the divide between well-funded incumbents and new entrants, encouraging market concentration and likely reducing diversity in innovation.

The new fees would almost certainly reduce the overall number of filings. There is a strong argument that reduced filings would result in higher-quality patents. While fewer filings might improve patent quality, the cost barrier could prevent disruptive but underfunded ideas from reaching the market. Fewer patents would also raise the stakes of patent disputes.

On the global stage, the U.S. could lose its competitive edge if innovators relocate R&D to countries with more accessible IP protections. Moreover, tying fees to patent value introduces significant uncertainty, as valuation is inherently subjective and prone to dispute.

Of course, the implications of the impact of the proposed fee structure depends greatly on how the valuation is calculated and how it is applied to the patents.  Does each individual patent get charged a percentage of revenue of a patented method, system, or product even though the system or product may be protected by dozens or hundreds of patents? Or does the percentage fee get distributed over all the patents attributable to the patented method, system, or product? There could be a scenario in which it is actually better for companies to get more patents to reduce the fees if the percentage is applied across an entire patent portfolio. For example, if a bicycle company makes $1M from sales of an innovative bicycle and there are 1,000 patents on the bicycle and the fee is 1% of revenue distributed over the total number of patents, the fees per patent are $10 and $10,000 for the portfolio of 1,000 patents. Given this example reduces the revenue to the government, it is unlikely to be applied in this manner; however, implementation details for the proposal are missing and whatever the details become, patent owners will find ways to reduce what they are to pay.

Potential Legal Challenges to the Proposal

The proposed fee changes are likely to face significant legal challenges. Under 35 U.S.C. 搂 41, Congress, not the executive branch, has the authority to set patent fees. Historically, courts have deferred to the USPTO on fee-setting when changes are modest and tied to cost recovery. However, a sweeping, revenue-driven increase could be struck down or stayed unless Congress explicitly authorizes it. Any attempt to implement such a change unilaterally would certainly be contested in court, with challengers likely arguing that a drastic fee hike oversteps executive authority.  

Additionally, the Administrative Procedure Act (APA) requires that regulatory changes undergo a notice-and-comment rulemaking process. Failure to comply with this process could result in successful legal challenges. There may also be constitutional concerns. If the fees are prohibitive, small businesses could argue that the fees violate Due Process or Equal Protection clauses, although such claims may be difficult to win.

In sum, the Trump Administration proposal is fraught with questions and uncertainty.  Congress and the courts should work to ensure that whatever the proposal is, it is thoroughly vetted and implemented by the correct branch of government.

Seventeen 天美传媒; Lardner LLP attorneys and five firm jurisdictions have been recognized in the 2025 edition of IAM Patent 1000: The World鈥檚 Leading Patent Practitioners. Two partners 鈥� Liane Peterson and Kiri Lee Sharon 鈥� were recognized for the first time, and the Washington, D.C. office was elevated from 鈥淩ecommended鈥� to 鈥淗ighly Recommended.鈥�

The IAM 1000 directory is compiled by a team of researchers based on more than 1,800 interviews with patent practitioners, patent attorneys, and in-house counsel.

The Foley attorneys recognized include:

United States: DC Metro Area

• Pavan Agarwal (Bronze – Individuals: litigation)
• Courtenay Brinckerhoff (Highly Recommended – Individuals: prosecution)
• Stephen Maebius (Recommended – Individuals: transactions and prosecution)
• Liane Peterson (Bronze – Individuals: litigation)
• Kiri Lee Sharon (Recommended – Individuals: prosecution)
• Michele Simkin (Recommended – Individuals: prosecution)
• Eric Sophir (Recommended: DC Metro Area)
• Sunit Talapatra (Recommended – Individuals: prosecution)
• Ngai Zhang (Recommended – Individuals: prosecution)

United States: Illinois

• Jeanne M. Gills (Recommended: Illinois)

United States: Massachusetts

• Matthew Lowrie (Silver – Individuals: litigation)
• Christopher McKenna (Recommended – Individuals: prosecution)

United States: Michigan

• Marcus Sprow (Recommended – Individuals: prosecution)

United States: Texas

• Gary Solomon (Recommended: Texas)

United States: Utah

• Whit Johnson (Recommended: Utah)

United States: Wisconsin

• Scott Anderson (Recommended – Individuals: transactions and prosecution)
• Jeffrey Gundersen (Recommended – Individuals: transactions and prosecution)
• Joseph Ziebert (Recommended – Individuals: prosecution and transactions)

Japan: Foreign

• Etsuo Doi (Recommended – Individuals: transactions)

The Foley jurisdictions recognized include:

• Massachusetts (Bronze)
• Michigan (Bronze)
• Washington, D.C. Metro Area (Highly Recommended)
• Wisconsin (Gold)
• Japan: Foreign (Recommended)
• International (Recommended)

For more information about IAM, visit their .

Patent Takeaways In Fed. Circ.’s 1st Machine Learning Ruling

This article was originally published in on May 13, 2025, and is republished here with permission.

The U.S. Court of Appeals for the Federal Circuit’s most recent recasting of patent eligibility in the machine learning space should give artificial intelligence and technology companies pause when considering how to obtain effective, assertable patent assets for their technology.

Specifically, the April 18聽ruling in Recentive Analytics Inc. v. Fox Corp. affirmed the invalidity of patents that are interpreted to merely apply existing machine learning techniques to new data environments.

The court reasoned that such claims, absent a demonstrated technical improvement that goes beyond the abstract idea itself, do not satisfy the requirements of Title 35 of the U.S. Code, Section 101.

Given the widespread use of machine learning across industries, this decision could carry significant implications for patent applicants. However, companies in the AI and machine learning space can take a few practical steps when describing their technology in patent applications to avoid falling prey to a similar outcome.

The Federal Circuit’s Approach to Eligibility

The Federal Circuit’s Recentive decision offers new and important guidance for navigating the two-step Alice test,聽derived from the U.S. Supreme Court’s 2014 decision in Alice Corp. v. CLS Bank International, when pursuing patent protection for machine learning-based inventions.

To address the first step and avoid a finding that the invention is directed to an abstract idea, companies may want to strategically frame their claims around specific technical improvements rather than broad applications of machine learning to known tasks.

The court in Recentive found the claims ineligible because they merely applied generic machine learning methods to conventional industry functions, such as scheduling and broadcasting, which the court viewed as abstract ideas.

To reduce the risk of a similar outcome, companies could focus on highlighting how their invention improves the operation of a computer system or enhances the machine learning technology itself, rather than simply applying machine learning to a domain-specific use case.

For the second step, companies might consider emphasizing how their claimed invention introduces an inventive concept that is not inherent to standard machine learning functionality.

In Recentive, the court found that elements such as iterative training and dynamic updates were routine aspects of machine learning systems and did not represent meaningful technological innovations.

To differentiate their inventions, companies could include detailed descriptions of novel configurations, system architectures or processing techniques that go beyond the expected behavior of generic machine learning.

For example, describing how these features contribute to measurable technical performance improvements, such as reduced computational load, faster model convergence, improved accuracy on complex datasets, or enhanced adaptability to real-time inputs, may strengthen the argument for patent eligibility.

Practical Takeaways for Patent Practitioners

The Recentive decision provides important guidance for patent practitioners drafting and prosecuting machine learning-related patents. In this regard, patents related to machine learning could benefit from clearly identifying and detailing how the invention advances the underlying machine learning techniques or their implementation.

For example, instead of broadly claiming “iterative model training,” the specification could explicitly disclose unique modifications or improvements to traditional training processes. This could include describing novel preprocessing steps, unconventional optimization techniques, or a customized neural network architecture that demonstrably improves prediction accuracy.

The court’s decision also indicated that insufficient disclosure can be a significant weakness, and it is imperative to describe the “how” behind any claimed improvement. For example, if the innovation relates to dynamically adapting models based on real-time data, the specification could include algorithmic or computational descriptions that illustrate how these adaptations significantly outperform conventional methodologies.

Merely applying known or established machine learning methods to new data environments might not, on its own, be enough to establish patent eligibility. Instead, claims could be framed to specify how the innovation contributes beyond the general application of machine learning.

For example, when working with health care data, the claims and specification might describe how the solution addresses technical challenges specific to that context, such as managing sparse or irregular data inputs, improving processing efficiency through faster convergence, or incorporating safeguards that respond to patient privacy and compliance concerns.

Furthermore, while individual steps in a process may be conventional, claims could still be eligible if the combination of steps provides a unique technological benefit. For example, describing how specific sequences of data collection, preprocessing, training, and inference work together to yield efficiency gains or improved accuracy might help reinforce that the claimed invention is more than a generic application of machine learning.

Practical Takeaways for Patent Applicants

The Recentive decision also carries important implications for patent applicants who develop or rely on machine learning-based technologies. In light of this decision, it could be helpful for applicants to reevaluate their patent portfolios to assess how clearly their machine learning-related claims articulate specific technical improvements relative to the state of the art or some other technique.

Internal audits can play a key role in this process, helping to identify patents that may lack detailed disclosures, and prompting consideration of whether continuation filings with enhanced specifications would be beneficial. Such reviews might also uncover opportunities to clarify claim scope, introduce more concrete examples or address emerging legal standards around abstract ideas.

In parallel, collaboration between technical and legal teams can support the development of stronger and more resilient applications. Encouraging early and ongoing engagement among inventors, engineers, and patent professionals might help surface patentable aspects of an innovation that could otherwise be missed.

In some cases where patent eligibility appears uncertain, applicants might consider whether trade secret protection provides a practical alternative, particularly for proprietary algorithms, training data sets, or model architectures that are difficult to detect or reverse-engineer. Such strategies may be relevant in fast-moving markets, where long patent timelines do not align with product cycles.

In this regard, effective trade secret management could include adopting robust confidentiality agreements, role-based access controls, internal awareness initiatives, and data safeguards.

Further, establishing internal review processes to monitor legal developments, assessing their relevance to pending or future applications, and discussing potential adjustments in claim strategy could provide valuable insights as eligibility standards continue to evolve.

Industry Impact

The Recentive decision highlights an ongoing challenge within U.S. patent law: striking a balance between protecting innovation and avoiding overly broad claims that impermissibly preempt entire industry sectors.

This ruling suggests a more restrictive path to eligibility for machine learning inventions, which may influence how patent applications in AI and machine learning are drafted going forward. As a practical consequence, inventors and patent owners may encounter heightened standards for disclosure and claim specificity.

As such, it could be helpful to approach portfolio development with greater attention to how each filing communicates a genuine technical contribution or advancement, rather than relying solely on the application of machine learning techniques to known problems.

What the Court Did Not Hold in Recentive

While the ruling narrows the scope of patent eligibility for machine learning inventions, it also leaves open several possibilities. For example, not all machine learning-related claims appear to be ineligible, and claims clearly aimed at technological improvements in how machine learning techniques operate can still be patented.

Additionally, this decision indicates that simply applying machine learning to a different field, without identifying a technical advancement, may not be sufficient.

However, claims that recite high-level machine learning processes might still be considered eligible if they are paired with inventive technical elements that contribute to a meaningful innovation. For example, a claim directed to a system for autonomous vehicle control might generally recite training a neural network to predict optimal steering angles based on sensor data.

While the training itself might be considered a general machine learning technique, the claim could be deemed eligible if it further specifies an inventive technical element, such as integrating a novel sensor fusion architecture that provides a significantly more accurate representation of the vehicle’s surroundings, leading to demonstrably improved safety and responsiveness compared to existing autonomous systems.

In this case, the inventive sensor fusion architecture, combined with the general machine learning training, could provide the additional technical contribution needed for eligibility.

Such observations may provide applicants with useful context when evaluating how best to frame and support machine learning-related claims going forward.

Conclusion

The Recentive decision serves as a cautionary guide for patent practitioners navigating the complexities of聽machine learning inventions. It highlights how thoughtful drafting, clear articulation of technical contributions and detailed disclosures can help address scrutiny under the current patent eligibility landscape.

In this regard, practitioners and patent applicants may benefit from adapting their strategies in response to these evolving standards.

[1] Recentive Analytics, Inc. v. Fox Corp. et al. , Appeal No. 2023-2437 (Fed. Cir. Apr. 18, 2025).

[2] Alice Corp. Pty. Ltd. v. CLS Bank Int’l , 573 U.S. 208 (2014).

Obesity and diabetes have been two of the greatest public health challenges for decades. Many different diets and fads have promised the public a quick fix and a path to losing excess weight or resolving their diabetic issues. GLP-1 drugs are revolutionizing medicine and the treatment of patients with diabetes, obesity, and other disorders associated with obesity.

With the growing demand of these drugs, pharmaceutical companies, health care providers, telemedicine companies, and pharmaceutical compounders are facing new challenges with GLP-1s. Organizations need to know how to navigate a complex set of issues, including patent considerations, regulatory issues, lawsuits, and M&A activity. 

Foley’s  and  sectors invite you to join us for a webinar on how your organization can navigate the crossroads of GLP-1s and prepare for this evolving landscape.

This webinar will explore the following GLP-1 topics:

• An inside look on the U.S. Food & Drug Administration’s (FDA) perspective
• How to prepare and secure your patents and safeguard your intellectual property
• The do’s and don’ts of mergers and acquisitions
• Litigation trends
• Clinical trial considerations

CLE

Applications for accreditation will be submitted to CA, CO, FL, IL, NY, TX, UT, VA, and WI. Uniform Certificates of Attendance will be provided to attendees licensed in other jurisdictions so they may self-apply if their jurisdiction allows.

天美传媒; Lardner LLP is an approved MCLE provider in California, Colorado, Illinois, New York, Texas, and Utah.
For purposes of New York CLE credit, this program is appropriate for both newly admitted and experienced attorneys.

Certificates of attendance will be distributed to eligible participants approximately eight weeks after the program via email.

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