How has USPTO Section 101 AI examination practice changed in 2025-2026, and what does it mean for patenting AI inventions?

The Direct Answer: USPTO Section 101 AI Examination Practice Has Shifted Toward Practicality

USPTO Section 101 AI examination practice underwent its most consequential recalibration since the 2019 Revised Patent Subject Matter Eligibility Guidance, driven by new USPTO leadership, a series of eligibility-focused guidance documents issued through 2025, and a clarified position on how examiners may use Rule 132 declarations—particularly so-called SMED (scientific method evidence) declarations—to establish or rebut findings of abstraction. As of August 2026, applicants prosecuting AI and machine-learning inventions face an examination environment that is measurably more navigable than the 2020-2023 period, when rejection rates for software and AI claims under 35 U.S.C. § 101 routinely exceeded 80 percent at first office action.

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The shift did not come from Congress or the courts. Despite repeated calls from across the patent community, no statutory fix to § 101 emerged, and the Supreme Court declined opportunities to revisit Alice Corp. v. CLS Bank (2014). Instead, the USPTO acted administratively: it issued guidance clarifying that claims directed to specific applications of AI—medical diagnostics using trained models, industrial process control, network security anomaly detection—are more likely to be treated as eligible improvements to technology rather than abstract ideas, provided the specification supplies enough technical detail. It also confirmed that examiners must provide evidentiary support when asserting that a claim lacks a practical application, and that applicant-submitted declarations under 37 C.F.R. § 1.132 can properly be considered in that analysis.

For practitioners, the practical takeaway is concrete: AI patent applications filed with technically grounded specifications, claims reciting specific architectural or training improvements, and strategic use of declaration evidence are clearing § 101 at meaningfully higher rates. But the environment is not uniformly favorable. Examiners still apply the two-step Alice/Mayo framework, and claims drafted as generic 'apply a neural network to data X and output Y' formulations continue to draw abstract idea rejections at high rates. Understanding where the line now sits—and how to argue on the right side of it—is the core of effective 2026 prosecution strategy.

Why the Change Happened: Leadership, Backlog, and Political Pressure

The recalibration traces to three converging pressures. First, leadership change at the USPTO in early 2025 brought officials who had publicly criticized the unpredictability of § 101 doctrine and committed to issuing clarifying guidance for AI-related inventions. Announcements covered by JD Supra and Dykema analysts Seppelt and Freire emphasized that the agency intended to reduce examiner discretion to reject AI claims as abstract without technical justification. Second, the agency faced persistent criticism that its AI strategy documents promised more than examination practice delivered; IPWatchdog's coverage of 'The AI-Enabled Patent Office' webinar highlighted the gap between the USPTO's stated support for AI innovation and the day-to-day experience of prosecutors facing boilerplate rejections.

Third, there was an internal consistency problem. Holland & Knight's review of the top § 101 stories of 2025 documented a pattern of examiner behavior inconsistent with the 2019 Guidance itself—rejections that treated any mention of a mathematical model or algorithm as dispositive, ignoring the guidance's instruction to evaluate claims as a whole for integration into a practical application. Patently-O's widely read commentary on the USPTO's April Fool's post observed that the agency used humor to acknowledge what practitioners already knew: the quiet problem was that § 101 practice had become a lottery dependent on the assigned art unit far more than on claim content.

The result was a sequence of clarification memos and training updates during 2025 that narrowed examiner discretion in two directions simultaneously. Examiners were told they could not simply assert that a claimed invention is an abstract idea; they needed to identify the abstraction with specificity and explain why the remaining claim elements do not supply significantly more. Conversely, applicants learned that the Office would entertain—and in some circumstances expect—evidentiary submissions, including expert declarations, when technical effect is disputed. This two-way street is what makes current practice different from prior guidance cycles, which tended to add requirements without adding tools.

The Rule 132 SMED Development: What Changed and How to Use It

The most operationally important development of the period, analyzed in detail by Reed Smith, is the USPTO's clarification regarding Rule 132 declarations containing scientific method evidence (SMED) in § 101 examinations. Historically, many practitioners assumed declarations were of limited value against § 101 rejections because eligibility is a legal question, not a factual one. The clarified position rejects that assumption in part: where an examiner's rejection rests on factual characterizations—for example, that a claimed improvement is merely 'routine data processing' or that a model's output lacks technological utility—an applicant may submit a declaration presenting experimental results, benchmark comparisons, or technical explanations demonstrating a concrete technological improvement.

The mechanics matter. A useful SMED declaration typically includes controlled test data showing performance gains attributable to the claimed invention (for instance, reduced inference latency, improved model convergence, lower false-positive rates in a detection system), an explanation of why those gains flow from the specifically claimed features rather than from generic computing hardware, and a comparison against prior approaches. Declarations should be tied element-by-element to the claim language; a declaration about an unclaimed embodiment carries little weight. Practitioners should also anticipate examiner pushback: some Technology Centers have historically discounted declarations as attorney-drafted advocacy, so third-party or inventor-executed declarations with reproducible methodology fare best.

There are costs and risks to weigh. Preparing credible test evidence can add tens of thousands of dollars in outside technical work, and submitting a declaration locks the applicant into a factual record that can resurface in litigation or IPR proceedings. For high-value AI portfolios—autonomous systems, drug discovery platforms, financial risk engines—the investment is frequently justified by the alternative: abandonment or years of appeal. For lower-stakes filings, careful claim drafting remains the cheaper first line of defense, with declarations held in reserve for a response to a final rejection or before appeal.

Comparing Prosecution Strategies Under Current Practice

Applicants today generally choose among three strategies, each with distinct tradeoffs. The table below summarizes them as commonly applied to AI and ML filings in 2025-2026.

The strategies are not mutually exclusive. Sophisticated prosecutors layer them: draft claims around technical improvements from the outset, respond to first-action rejections with targeted arguments citing the 2019 Guidance and subsequent clarifications, deploy declarations only when the examiner disputes a factual predicate, and reserve appeal for cases where the record supports reversal. Massachusetts Lawyers Weekly's analysis of the USPTO shift made a similar point—what changed is not that rejections disappeared, but that the toolkit for overcoming them became more predictable and evidence-based.

An alternative worth noting is design-around drafting toward hardware-integrated claims. Claims that tie model training or inference to specific processor architectures, memory hierarchies, or sensor systems have historically cleared § 101 at higher rates than pure software formulations. The tradeoff is scope: such claims are easier to design around by competitors running equivalent models on commodity infrastructure. Portfolio owners must balance eligibility certainty against enforcement breadth, and the right balance differs sharply between, say, an edge-device inference patent and a cloud-based recommendation engine.

Common Mistakes That Still Trigger Abstract Idea Rejections

Despite friendlier guidance, certain drafting and prosecution errors continue to generate avoidable § 101 rejections. The most common is claiming the AI at too high a level of generality: 'a computer-implemented method comprising receiving data, applying a machine learning model, and outputting a prediction' describes nothing beyond the abstract concept of prediction itself. Examiners correctly treat such claims as covering the idea rather than an implementation. The fix is specificity that goes to how—the loss function architecture, the feature extraction pipeline, the training regime, the error-correction loop—not merely what domain the prediction applies to.

A second mistake is treating the specification as an afterthought. Under current practice, examiners look to the specification to confirm that the claimed improvement is real and technologically grounded. Applications with one-paragraph descriptions of the model, no training details, and no performance discussion invite the characterization that the applicant is seeking to monopolize an abstract concept. Filing AI applications with genuine engineering disclosure—including at least one worked example with quantifiable results—costs little at drafting time and pays dividends throughout prosecution.

Third, applicants often misread the guidance as eliminating § 101 risk entirely. It did not. Alice remains controlling law, and district courts continue to invalidate AI patents that fail to claim more than the abstract idea. A grant from a friendly art unit is not litigation insurance. Fourth, some practitioners overuse declarations reflexively, spending significant money on SMED evidence in cases where a well-crafted argument would suffice, or worse, submitting declarations whose data do not map onto the actual claim limitations—inviting both evidentiary discounting and potential inequitable-conduct questions if the mismatch appears deliberate. Finally, waiting until final rejection to build the technical record is usually a mistake; the strongest responses integrate argument and evidence from the first action.

When to Act: Timing Considerations Through 2026

Timing decisions differ depending on where an applicant sits in the pipeline. For new filings, act now under the current framework rather than waiting for further guidance or legislative reform. The probability of near-term statutory change remains low—Congress has debated § 101 reform since 2017 without producing a bill—and pending applications benefit from the clarified examination posture immediately. Applicants with pending AI applications facing abstract idea rejections should cite the 2025 clarification memoranda directly in responses; examiner compliance improves when rejections are framed against explicit agency instructions rather than general principles.

For portfolios built during the harsher 2018-2023 era, consider proactive measures. Applications abandoned after § 101 rejections can sometimes be revived through continued examination with amended claims and new evidence, though revival windows and fees constrain options. Issued patents vulnerable under Alice should be audited for claim-scope flexibility—continuation practice that adds technically specific fallback positions before any challenge arises is cheap relative to post-grant defense. Companies planning licensing programs should note that counterparty diligence increasingly probes § 101 vulnerability explicitly, so strengthening the record now protects deal value later.

One caution on timing: the current posture reflects administrative policy, which can change with leadership. Guidance issued in 2025 is not statute, and a future administration could recalibrate again. Applicants should therefore prioritize getting valuable claims allowed while the window is favorable rather than assuming the environment is permanent. History suggests these pendulums swing on roughly three-to-five-year cycles.

Cost Realities and Budget Planning

Budgeting for AI prosecution under current practice requires honest numbers. A well-drafted AI application with adequate technical disclosure typically runs $12,000-$25,000 in preparation fees at experienced firms, versus $8,000-$15,000 for a thin disclosure that will likely need supplementation anyway. Prosecution through allowance, including one or two office action responses, adds $5,000-$20,000 depending on complexity and art unit. If a SMED declaration becomes necessary, expect $15,000-$75,000 or more, covering expert engagement, experimental design, testing, and declaration drafting. Appeals to the PTAB range from $30,000 to well over $100,000 for complex AI cases.

These figures justify selectivity. Not every AI invention warrants maximum investment. A triage approach works well: classify the portfolio into core assets (full investment including evidence readiness), defensive filings (moderate investment, accept narrower claims), and exploratory filings (minimal spend, abandon quickly if rejections persist). Companies filing internationally should note that European practice under Articles 52 and 56 EPA presents its own AI eligibility hurdles—inventions must show a technical effect—which sometimes favors parallel claim sets tailored to each regime. Coordinating US and EP claim strategy from the outset avoids expensive divergence later.

The Bottom Line for AI Innovators

Current USPTO Section 101 AI examination practice rewards substance over form. Applicants who disclose real engineering, claim specific technical mechanisms, and marshal evidence when challenged are obtaining allowances at rates unthinkable three years ago. Those who file vague, domain-wrapped abstractions continue to fail, and rightly so under any coherent reading of the law. The administrative improvements are welcome but partial—they cannot substitute for the doctrinal clarity that only Congress or the Supreme Court could provide, and practitioners should keep advocating for it. In the meantime, the playbook is clear: draft for technology, document your improvements, use the evidentiary tools the Office has now acknowledged, and move decisively while the climate holds. Sites like AI Trademark Review will continue tracking whether this recalibration stabilizes into durable practice or proves another swing of the pendulum.