The AI Patent Balance: Why Generative AI Both Weakens and Strengthens Modern Software Patents and Why Supreme Court’s Refusal to Hear USAA v. PNC Matters to Inventors & Startups

AbstractIn May 2026 the U.S. Supreme Court declined to review the Federal Circuit’s decision in USAA v. PNC Bank, leaving intact a restrictive § 101 eligibility framework that has invalidated broad software and AI patents. At the same moment, generative AI is reshaping patent strategy in two opposing directions. On one side, modern LLMs, code copilots, and pretrained models make it dramatically easier to automate real-world processes, increasing the risk that claims will be deemed abstract (§ 101) or obvious to try (§ 103/KSR). On the other side, the same tools collapse the gap between concept and reduction to practice, allowing startups to rapidly generate production-grade implementations, measurable metrics, real-world constraints, and operational evidence that can strengthen both eligibility and non-obviousness arguments.

This paper introduces the “AI Patent Balance,” the paradox that generative AI simultaneously heightens patent vulnerability while also enabling more technically grounded, durable intellectual property. Using the USAA case and KSR precedent as anchors, it explains why the most defensible AI patents in the coming decade will not be broad conceptual automation claims, but rather those that document concrete engineering bridges between AI systems and messy physical, industrial, scientific, or regulated realities. Practical implications and drafting strategies for inventors and startups are discussed. This paper is also available at:

https://doi.org/10.5281/zenodo.20277615

Figure 1. The AI Patent Balance: Generative AI creates opposing pressures on modern patentability. On one side, AI increases abstraction (§ 101) and obviousness (§ 103/KSR) risks. On the other, it accelerates implementation, reduction to practice, and concrete technical grounding that strengthens durable patents.

1. USAA v. PNC (SCOTUS Cert Denied May 18, 2026): What Happened

In May 2026, the U.S. Supreme Court declined to hear USAA’s appeal in its long-running patent dispute against PNC Bank involving mobile remote deposit capture (“MRDC”) technology.

While technically a “non-decision,” the denial may prove highly significant for AI, software, and startup patent strategy because it leaves the Federal Circuit’s increasingly restrictive patent-eligibility framework fully intact.

For founders, investors, and AI companies, the message is clear:

• Broad software patents remain vulnerable.

• AI does not automatically make an invention patentable.

• Generic “AI-powered” workflows face increasing risk under both §101 and §103.

• Durable patents increasingly require concrete technical grounding tied to real-world operational constraints or measurable technological improvements.

At the same time, generative AI is also changing patent strategy in the opposite direction: it dramatically lowers the barrier between concept and implementation, enabling startups to rapidly build functioning systems, generate operational evidence, and reduce excessive abstraction.

The result is what can be described as an “AI Patent Balance” — generative AI simultaneously increases abstraction and obviousness pressures while also accelerating implementation, reduction to practice, and operational technical grounding.

Quick Background: What Happened in USAA v. PNC?

USAA’s patents covered mobile remote deposit capture systems — allowing users to deposit paper checks using smartphone cameras and banking apps. The patents generally involved capturing check images, processing them, transmitting the data, and integrating with banking systems.

USAA obtained major jury verdicts totaling roughly $200+ million, including findings of willful infringement.

In 2025, however, the Federal Circuit reversed and invalidated the asserted claims under 35 U.S.C. §101. The court held the claims were:

1. Directed to the abstract idea of depositing a check using a handheld mobile device; and

2. Lacked an “inventive concept” sufficient to transform the abstract idea into patent-eligible subject matter.

Critically, the court emphasized:

• the claims relied on generic mobile/computing components,

• the claims were highly results-oriented,

• and technical improvements described in the specification were not sufficiently tied to the actual claim language.

The Supreme Court’s refusal to hear the case does not create new law — but it effectively leaves the Federal Circuit’s current framework untouched.

Many companies had hoped the Court might:

• clarify the confusing “abstract idea” doctrine,

• narrow Alice,

• or create a more innovation-friendly standard for software and AI patents.

Instead, the denial signals that the current rules are likely here to stay for the foreseeable future.

The Second Threat: KSR and the Expansion of Obviousness

Even if an invention survives §101 eligibility challenges, it still faces §103 obviousness analysis under the Supreme Court’s 2007 KSR v. Teleflex decision.

KSR dramatically broadened obviousness analysis by rejecting rigid “teaching-suggestion-motivation” tests and emphasizing:

• common sense,

• ordinary creativity,

• predictable combinations,

• and “obvious to try” reasoning.

This already made software patents more vulnerable.  Generative AI may amplify that vulnerability even further.

2. The AI Patent Balance

Here’s the uncomfortable truth most AI founders have not fully internalized yet:

Generative AI is simultaneously creating opposing pressures on modern patentability.

On one side, AI increases:

• abstraction risk under §101,

• obviousness risk under §103,

• and the appearance of predictable combinations.

On the other side, AI dramatically lowers implementation barriers and accelerates reduction to practice, enabling startups to rapidly build functioning systems, document technical constraints, and generate evidence of real-world operational complexity.

The future of durable AI patents may therefore depend less on broad conceptual automation and more on technically grounded implementation.

Side One of the Balance:

AI Increasing Patent Vulnerability

§101 – Abstraction Risk

Modern generative AI makes it trivial to automate almost any real-world workflow using:

• cloud APIs,

• LLMs,

• generic mobile devices,

• and standard software infrastructure.

As a result, many inventions increasingly resemble:  “use AI/software to perform a known activity.”

Courts are becoming more willing to classify these claims as:

• abstract,

• functional,

• or results-oriented.

The more generic the implementation appears, the greater the risk.

§103 – Obviousness Risk

At the same time, generative AI dramatically compresses experimentation and implementation cycles.

A modern “person of ordinary skill in the art” now effectively has access to:

• GitHub,

• Hugging Face,

• coding copilots,

• pretrained models,

• automated experimentation pipelines,

• and large-scale technical search tools.

Combinations that once required substantial experimentation may now appear:

• predictable,

• routine,

• or “obvious to try.”

What felt inventive several years ago may increasingly look like straightforward recombination of known tools.

Side Two of the Balance:

AI Increasing Implementation and Technical Grounding

At the same time, generative AI is creating a countervailing force that may strengthen certain categories of patents.

Historically, many software inventions existed largely at the architectural or conceptual level because building production-grade systems required:

• large engineering teams,

• substantial capital,

• long development cycles,

• and specialized technical expertise.

Patent filings often described high-level workflows or aspirational architectures long before fully functioning implementations existed.  Generative AI changes this dynamic.

Modern AI tools now allow startups and inventors to:

• rapidly prototype complex systems,

• generate production-quality code,

• build functioning interfaces and workflows,

• integrate APIs and cloud infrastructure,

• automate data pipelines,

• create operational enterprise systems,

• and iterate on implementation details at unprecedented speed.

As a result, the distance between concept and reduction to practice is compressing dramatically.  Ironically, this may reduce one of the traditional weaknesses of many software patents: excessive abstraction.

When inventors rapidly move from concept to functioning system, they naturally generate:

• implementation detail,

• technical architecture decisions,

• engineering tradeoffs,

• measurable operational metrics,

• validation data,

• deployment constraints,

• reproducibility challenges,

• edge-case handling,

• and real-world documentation.

These details often strengthen patent applications because they help demonstrate:

• practical application,

• concrete technological implementation,

• non-generic engineering solutions,

• and tangible technical improvements rather than purely conceptual automation.

In many cases, the act of implementation itself also generates evidence supporting non-obviousness.

Real-world deployment frequently reveals:

• unexpected technical obstacles,

• failed approaches,

• integration complexity,

• scaling limitations,

• interoperability problems,

• latency constraints,

• environmental variability,

• or surprising operational behavior.

Those discoveries may help distinguish a genuine invention from a merely predictable combination of known tools under KSR.

Figure 1. The AI Patent Balance: Generative AI creates opposing pressures on modern patentability. On one side, AI increases abstraction (§ 101) and obviousness (§ 103/KSR) risks. On the other, it accelerates implementation, reduction to practice, and concrete technical grounding that strengthens durable patents.

The Emerging Shift

This creates a major paradox in the generative AI era: The same AI systems that make broad software combinations appear more routine and “obvious to try” may also expose the hidden complexity of successfully implementing those combinations in real-world environments.

In other words, AI is shifting value away from broad conceptual claiming and toward operationally grounded implementation.

The patents most likely to survive long-term may not be those that merely describe high-level AI automation, but those that document:

• concrete engineering constraints,

• measurable technical improvements,

• deployment-specific solutions,

• real-world operational behavior,

• and the specific bridge between AI systems and the messy realities of physical, industrial, scientific, or regulated environments.

Generative AI therefore compresses not only development timelines, but also the gap between abstract idea and tangible technological implementation.  Startups that leverage this shift effectively may build stronger and more defensible intellectual property than was possible in earlier generations of software development.

3. Actionable Survival Guide for AI Founders & Startups

The “AI Patent Balance” is not fatal it is navigable. Startups that understand the dual pressures can draft stronger, more durable patents by shifting from high-level conceptual claims to technically grounded implementations.

Here is a practical playbook (from an Inventor, not a Lawyer):

Note: Inventors/Startup Founders may be motivated by differently and have different perspectives in patent development and filing strategies.

1. Claim Drafting – Move from “AI Does X” to “AI Solves This Specific Technical Problem

in This Specific Way”

• Recite the concrete technical bridge inside the claims themselves, not just the specification.

• Include tangible metrics, sensor integration, physical or regulatory constraints, calibration logic, edge-case handling, or measurable performance improvements that generic AI tools do not automatically deliver.

• Avoid purely functional or results-oriented language (“the system determines…”, “the AI analyzes…”). Courts (and the PTAB) now read claims for what they actually recite.

2. Specification as Your Non-Obviousness Armor

• Document the real technical problem a modern POSITA (armed with today’s LLMs, copilots, and pretrained models) would actually face.

• Explicitly describe why generic or obvious combinations would fail or fall short.

• Highlight unexpected results, engineering tradeoffs, failed approaches, integration complexity, latency constraints, environmental variability, and real-world deployment data.

• Include flowcharts, pseudocode, performance benchmarks, and quantitative examples — these become critical evidence in both § 101 and § 103 fights.

3. Portfolio & Protection Strategy

• File a mix of broad conceptual claims (for licensing) and narrow, metric-heavy claims (for enforcement).

• Use continuations aggressively to emphasize real-world implementation details as they emerge.

• Protect core model weights, proprietary training pipelines, and sensitive datasets as trade secrets.

• Consider layering design patents or copyright protection for distinctive user interfaces or visual elements where appropriate.

4. Prosecution & Litigation Readiness

• When responding to § 101 rejections, point directly to specific claim limitations that provide a technological improvement or practical application (USPTO 2024–2025 AI guidance still favors well-drafted claims that tie AI to concrete technical solutions).

• Prepare expert declarations on the “specific bridge” and secondary considerations (commercial success, long-felt need, unexpected results) before you ever assert the patent.

• Expect early § 101 motions and parallel IPRs — have your operational evidence and technical grounding ready from day one.

5. One-Page Founder Self-Audit Checklist Before filing, ask:

• Does the claim read like “apply AI to a known process”?

• Are there quantifiable technical metrics or real-world phenomena recited in the claims?

• Does the invention solve a problem that generic AI tools do not automatically handle?

• Is the AI layer tied to physical sensors, deployment constraints, regulatory requirements, or measurable operational challenges?

• Have I clearly explained why a modern POSITA, with today’s AI tools, would not have found this obvious to try?

Teams that consistently answer “yes” to the last four questions are building the patents most likely to survive the AI Patent Balance.

4. Conclusion

The Supreme Court’s refusal to hear USAA v. PNC on May 18, 2026, did not change the law — it simply confirmed that the current, stricter patent-eligibility framework is here to stay for the foreseeable future. At the same moment, generative AI is creating a genuine paradox: it makes broad conceptual automation both easier to replicate (raising § 101 abstraction and § 103 obviousness risks) and easier to implement with real technical grounding (strengthening the very patents that survive both tests).

This is the AI Patent Balance.

The winners in the next decade will not be the teams that claim the broadest automation ideas. They will be the teams that use generative AI to move fastest from concept to production-grade system and then capture the resulting implementation details, measurable metrics, engineering tradeoffs, and real-world constraints in their patent applications.

Durable AI intellectual property will belong to those who document the specific bridge between powerful but generic AI tools and the messy realities of physical systems, scientific experiments, industrial processes, or regulated environments.

The tools have changed. The legal standards have stabilized. The opportunity is now clearer than ever: build fast, implement concretely, and patent the technical grounding that generic AI cannot automatically provide. Startups that master this balance will not only survive, but they will emerge with stronger, more defensible moats than any previous generation of software innovators.