Artificial Intelligence Patents: 19 Step 2A Improvement Argue vs Drop

62 on the Artificial Analysis Intelligence Index is what separates Muse Spark 1.3 (max) from its predecessors, according to Artificial Analysis, and it was driven by agentic and scientific gains rather than abstract description. That distinction is the entire Step 2A improvement fight: eligibility turns on a recited gain in computer function, not on lawyer argument that a result feels improved.

When the claim recites that kind of gain — Tau3-Bench Banking moving from 35% to 47% and then to 52%, or Terminal-Bench moving from 80% to 85% — argument preserves eligibility because the improvement is in the machine. When no such gain is recited, argument alone creates estoppel without satisfying Step 2A.

The winning move then is surgical drop-and-redraft: abandon the abstract improvement argument and redraft to the architecture that produced the gain, from CritPt moving from 18% to 26% for 8 points to reasoning that uses 28% more tokens for agentic work. Eligibility follows the recited technical effect, not the assertion.

Step 2A Prong-Two Mechanics

Argue improvement at Step 2A only when the claim language already measures it. Under the Revised Patent Subject Matter Eligibility Guidance published at 84 Fed. Reg. 50, Step 2A is two prongs: Prong One asks whether the claim recites an abstract idea grouping — mathematical concepts, mental processes, or methods of organizing human activity — and Prong Two asks whether that recitation, viewed as a whole, integrates the exception into a practical application. If Prong Two is satisfied, the claim is eligible without reaching Step 2B. If not, examination proceeds to Step 2B inventive concept. For AI claims in 2026, that Prong Two gate is where most cases are won or lost.

The improvement path lives in MPEP 2106.04(d)(1), derived from Enfish LLC v. Microsoft Corp. 822 F.3d 1327. Enfish held that a claim to a self-referential database table was not directed to an abstract idea because it recited an improvement to computer functionality itself, not use of a computer as a tool to perform an abstraction faster. The MPEP codifies that distinction: a generic recitation of a processor, memory, or training a model does not integrate. A recitation of how the computer's operation is improved — in structure, memory handling, or processing — can integrate. The status-quo myth that adding improved accuracy, improved prediction, or improved diagnosis satisfies Enfish fails every time because those are improvements to the abstract result, not to computer functionality.

The July 16, 2024 Subject Matter Eligibility Guidance Update makes that concrete for AI in Example 39, directed to AI-based neural network training for anomaly detection. The eligible variant did not claim training a neural network to detect anomalies in the abstract. It recited distributed training across nodes with specific data partitioning and synchronization to reduce training time and enable training that could not practically complete on a single machine. Eligibility turned on that architectural limitation and its measurable computer-function effect. The ineligible contrast claimed the same training objective without the distributed architecture, leaving only the mathematical optimization and the detection result.

From computational claim analysis, I teach this as a semantic-structure test you can apply before you argue. Parse the independent claim for architectural verb-noun pairs where the verb operates on computing structure. Eligible patterns look like pruning convolutional layers, reallocating memory blocks, partitioning training data across nodes, or synchronizing gradients with reduced communication overhead. Ineligible patterns look like calculating fraud scores, classifying medical images, or minimizing loss — verbs operating only on the mathematical result. If your claim has the first type plus a stated functional gain in speed, memory, or throughput, you have an Enfish argument to make. If it has only the second type, argument will not create what drafting omitted.

That is the failure condition under 35 U.S.C. 101 for generic AI claims: reciting applying a trained model to fraud scoring or medical diagnosis without reciting how computer operation itself is improved in speed, memory, or throughput. A claim that receives transaction data, applies a trained classifier, and outputs a fraud risk does not integrate under Prong Two, even if the model is highly accurate. A claim that receives radiology images, applies a neural network, and outputs a diagnosis does not integrate for the same reason. In both cases the computer is a tool for the abstraction. The fix is not stronger attorney argument about technical improvement. The fix is to drop the abstract step and redraft to the technical architecture that produces the gain — the pruning, quantization, memory reallocation, or distributed schedule — and then argue that measured gain.

2024-2026 Allowance Math

19.3% is the number that should end attorney-argument-only Step 2A practice for AI claims. According to the Juristat 2025 AI Prosecution Report, AI claims where applicants argued improvement without reciting a technical gain allowed at 19.3% after first Step 2A rejection across analyzed office actions. As someone who parses claim language computationally, I read that as a semantic failure, not a persuasion failure: without a measurable architectural token in the claim, the examiner has nothing to map to Prong Two.

Drop and redraft to architecture reverses the math. According to the LexisNexis PatentAdvisor 2025 AI Allowance Study, applicants who dropped the abstract limitation and amended to recite technical architecture allowed at 54.7% within two office actions. That is the operational form of the central rule: argue Step 2A improvement only if the AI claim already recites a measurable computer-function gain such as faster processing, lower memory use, or fewer training cycles; if not, drop the abstract step and redraft the claim to the technical architecture that produces the gain.

Forum matters because architecture is legible in some art units and invisible in others. According to the Agency Data Visualization Center FY2024 Technology Center data, AI applications examined in TC 2100 computer architecture allowed at 58.2% versus 33.9% in TC 3600 business methods. My take from claim-construction work is direct: TC 2100 examiners credit distributed-cache structures, parallelized training pipelines, and memory-hierarchy limits as technical, while TC 3600 examiners read the same disclosure as use of AI for a business result unless the architecture is claimed.

The language that moves examiners is comparative and resource-bound, not conclusory. According to the Berkeley Center for Law & Technology 2025 Eligibility Empirical Study, Step 2A responses that added comparative efficiency language such as reduced processor cycles succeeded 47.8% of the time versus 21.4% for attorney-argument-only responses. In corpus terms, succeeded means the rejection was withdrawn or overcome without appeal, and the delta comes from adding a before-versus-after efficiency comparator tied to a claim element.

The sharpest cut is resource metrics versus accuracy. According to the Stanford CodeX 2026 Semantic Claim Corpus analysis of AI claims, claims with explicit resource metrics like training iterations or memory footprint survived Step 2A at 61.5% compared to 24.6% for accuracy-only claims. Accuracy-only language — higher precision, better prediction, improved classification — parses as abstract result. Training iterations, memory footprint, and processor cycles parse as computer-function gain because they constrain how the machine operates.

For prosecution, apply a ledger test before you argue: if you cannot point to a number or structural limit already in the claim, do not argue improvement. Amend first to add the layer, pruning rule, quantization step, or distributed-training limitation that produces faster processing, lower memory use, or fewer training cycles, then argue. That sequence is what doubles allowance likelihood.

Argue vs Drop Scorecard

Prosecutors frequently treat the Step 2A eligibility rejection as a debate to be won through persuasive argumentation, but this approach systematically inflates costs while narrowing future enforcement rights. The strategic imperative is not to argue improvement in a vacuum, but to deploy a precise decision matrix that prioritizes claim architecture over rhetorical defense. When an AI claim lacks intrinsic technical specificity, arguing for an abstract improvement is a liability; when it possesses measurable hardware gains, argumentation becomes a viable supplement to amendment.

Beyond immediate costs, the long-term risk of argument-only responses is governed by prosecution-history estoppel. Under Aylus Networks Inc. v. Apple Inc., 856 F.3d 1353 (Fed. Cir. 2014), statements made to overcome a § 101 rejection can permanently narrow the scope of claim construction. If an applicant argues that their AI model constitutes a technical improvement without amending the claims to reflect that improvement, they inadvertently concede that the original claim language was insufficient. This creates a trap where the patentee cannot later assert infringement against products that utilize the broader, unamended functionality. Estoppel adds no patentable weight to the application but severely restricts its commercial value.

The only scenario where arguing improvement is defensible is the "safe harbor" condition: the claim must already recite concrete hardware integration. Examples include parallel FPGA pipeline execution, on-chip SRAM reallocation, or specific reductions in training cycles. If the claim language does not explicitly tie the AI process to these physical constraints, the abstract limitation remains exposed. In such cases, the drafter must drop the abstract step entirely. The global AI market's projected growth from $28.42 billion in 2020 to $40.74 billion by 2026, per MarketsandMarkets (Ironhack), underscores the urgency of securing enforceable patents quickly; protracted battles over abstract eligibility erode competitive advantage faster than prior art rejections ever could.

Aggregate allowance statistics for AI claims are structurally deceptive because they obscure the variance between technical and business-method implementations. In FY2024, Art Unit 2121 (computer AI applications) allowed at a rate of 71.2%, whereas Art Unit 3620 (business-method AI) allowed at only 31.4%. This 40-point swing demonstrates that aggregate rates mask the reality that eligibility outcomes depend entirely on whether the claim recites a measurable architectural gain or merely an abstract result.

The legal landscape introduces further unpredictability through the *Berkheimer v. HP Inc.* rule (881 F.3d 1360), which requires examiners to provide evidentiary support if an improvement is deemed well-understood, routine, and conventional. This creates an unpredictable Step 2B fallback even after a Step 2A loss, as the examiner's factual assertions regarding conventionality can be challenged with contrary evidence. Prosecutors must anticipate this evidentiary burden rather than assuming a mechanical rejection.

What the Data Doesn't Tell You

Speed alone does not satisfy Prong Two if it derives from abstract mathematics rather than machine architecture. The Federal Circuit in *Electric Power Group LLC v. Alstom S.A.* (830 F.3d 1350) and *SAP America Inc. v. InvestPic LLC* (898 F.3d 1161) held that claimed faster data analysis was ineligible because the speed came from abstract math, not an improved machine. These counter-examples confirm that arguing technical improvement fails when the claim language does not tie the gain to specific computer functionality.

Recent data is also distorted by the Deferred Subject Matter Eligibility Response Pilot (2022-2025), which deferred 101 decisions until after 102/103 examination. This policy inflated recent allowance figures for AI cases that never faced a substantive Step 2A review, creating a false sense of security for applicants relying on post-pilot statistics.

Accuracy-only gains, such as 94.5% diagnostic precision or reduced false positives, fail Prong Two when the specification lacks comparative computer-resource data. Without metrics like CPU cycles, RAM bytes, or kilowatt-hours, these improvements remain abstract results. To survive examination, claims must redraft to the technical architecture producing the gain, ensuring the improvement is tied to tangible resource reduction rather than algorithmic output quality.

Application No. 17/456,789 illustrates the mechanical failure of arguing abstract AI limitations when no architectural gain is present. The original Claim 1 recited training a convolutional neural network on annotated scans to diagnose tissue anomaly. The examiner rejected this under Step 2A as an abstract mental process and mathematical calculation. The Prong Two finding explicitly stated that the claim used generic computer components operating at a high level without integrating anomaly detection into a technical improvement to image processor operation.

The drop strategy deleted the diagnostic correlation limitation. It inserted a pruned 4-layer CNN architecture with channel-wise sparsity mask. This cut model weights from 18.4 million to 10.7 million parameters. The amendment added measured computer gain of 42% lower graphics memory from 8.6GB to 5.0GB. Inference latency dropped from 380 milliseconds to 165 milliseconds. This achieved a 2.3x speedup on an NVIDIA A100 testbed.

The outcome was a Notice of Allowance mailed 3.2 months after amendment. There was no further office action. This preserved 412 days of patent term adjustment. It avoided a second rejection cycle.

| Metric | Original Claim | Redrafted Claim |

|---|---|---|

| Model Weights | 18.4 million | 10.7 million |

| Graphics Memory | 8.6GB | 5.0GB |

| Inference Latency | 380 ms | 165 ms |

| Speedup Factor | 1.0x | 2.3x |

| Time to Allowance | N/A | 3.2 months |

| PTA Preserved | N/A | 412 days |

| Rejection Cycles | 1+ | 0 |

The data confirms that dropping the abstract step and redrafting to the technical architecture doubles allowance likelihood. Arguing improvement only works if the claim already measures it. Otherwise, the examiner sees only generic computer use. The drop strategy forces the claim to recite the measurable gain. This satisfies Step 2A by showing integration into a technical improvement. The result is faster allowance and more PTA.

Argue Step 2A improvement only when the independent claim already recites the architecture that produces it. That is the entire choice in 2026 examination: if the claim measures a computer-function gain in structural terms, argue; if it only claims a better prediction, drop the abstract step and redraft to the technical means.

17/456,789

From a claim-construction perspective, eligibility turns on what the dependency structure actually limits. Run a ClaimMaster 2026 dependency check for hardware verbs before you write a word of argument. If the independent claim ties a pruned attention block, quantized weight store, or parallel inference pipeline to at least 15% measured latency reduction or at least 20% memory reduction, you have a Prong Two anchor. The examiner can locate improved functionality in faster processing or lower memory use, not in an abstract correlation. Without that verb-plus-structure linkage, argument does not cure the defect, it entrenches it.

How to Choose Well

The myth to kill is that high accuracy equals technical improvement. It does not under Step 2A. When the claim's only gain is prediction accuracy above 90% without reciting processor cycles, training epochs, or bandwidth savings, the Office reads that as an abstract data-collection step with a result attached. Drop that step. Redraft to the technical means that produces the gain: the specific layer reduction, memory-mapped cache, or training-cycle limit that makes the system itself operate differently. That redraft is what doubles allowance likelihood, because it moves the claim from what the model predicts to how the computer functions.

Do not choose argue text in the blind. Request an examiner interview within 30 days of the Step 2A rejection and ask for the specific Prong Two deficiency framed as 101 eligibility versus 103 prior-art obviousness. That framing matters for semantic scope: if the examiner says the architecture is eligible but obvious, you argue narrowly and amend for prior art; if the examiner says the architecture itself is still abstract, you must redraft to hardware operation before any persuasion will help. Get that distinction on the record before choosing language.

If the examiner maintains Step 2A after interview and the art-unit allowance rate is below 40%, file a narrow technical amendment through the Pre-Appeal Brief Conference fee path. That conference forces a second look without the cost and estoppel load of a full appeal, and it works best when your amendment adds measurable structure rather than explanatory argument. In parallel, preserve a continuation-in-part with the original broad diagnostic claim while pursuing drop-and-redraft in the parent, maintaining the 20-year term from the earliest filing date under Section 120 priority. You keep the broad diagnostic scope alive for enforcement while the parent issues on technical architecture.

Do not choose argue text in the blind. Request an examiner interview within 30 days of the Step 2A rejection and ask for the specific Prong Two deficiency framed as 101 eligibility versus 103 prior-art obviousness. That framing matters for semantic scope: if the examiner says the architecture is eligible but obvious, you argue narrowly and amend for prior art; if the examiner says the architecture itself is still abstract, you must redraft to hardware operation before any persuasion will help. Get that distinction on the record before choosing language.

If the examiner maintains Step 2A after interview and the art-unit allowance rate is below 40%, file a narrow technical amendment through the Pre-Appeal Brief Conference fee path. That conference forces a second look without the cost and estoppel load of a full appeal, and it works best when your amendment adds measurable structure rather than explanatory argument. In parallel, preserve a continuation-in-part with the original broad diagnostic claim while pursuing drop-and-redraft in the parent, maintaining the 20-year term from the earliest filing date under Section 120 priority. You keep the broad diagnostic scope alive for enforcement while the parent issues on technical architecture.

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