PatentBench

PatentBench-Mini v0.1.0

Initial Benchmark Results
The First Reproducible Benchmark for Patent Prosecution AI
92.3%
Overall Accuracy
298
Test Cases
82
Real Office Actions
8
Technology Centers
Author: Roger Hahn, USPTO Registered Patent Attorney
Salt Holdings, LLC

Date: March 20, 2026
Version: 0.1.0 (Initial Release)

Table of Contents

1. Executive Summary

PatentBench-Mini v0.1.0 is the first reproducible, open benchmark designed to evaluate AI systems on real-world patent prosecution tasks. Drawing from 82 genuine USPTO Office Actions across 8 Technology Centers, the benchmark tests four core competencies that patent practitioners perform daily: classifying Office Action types, computing response timelines, calculating USPTO fees, and reasoning through substantive rejections.

The benchmark achieves an overall accuracy of 92.3% across 298 individual test cases, scoring 941 out of 1,020 total points. Three of four task categories achieved perfect 100% accuracy. The sole source of errors — Deadline Calculation at 81.1% — traces to JavaScript date arithmetic edge cases rather than substantive legal reasoning failures.

92.3%
Overall Accuracy
941/1020
Points Scored
3 of 4
Perfect Task Scores
321
USPTO Apps in Dataset

Key Finding: Classification, timeline, and fee tasks are solved — the remaining challenge is deadline date arithmetic, which is an implementation bug, not a reasoning gap. Fixing the underlying JS date library would bring overall accuracy near 100%.

2. Methodology

2.1 Data Collection

The benchmark dataset was constructed from 321 real USPTO patent applications, of which 268 received at least one Office Action. From this pool, 82 Office Actions were selected to ensure coverage across all eight Technology Centers, diverse rejection types (35 U.S.C. §101, §102, §103, §112), and varying procedural postures (non-final, final, advisory).

2.2 Task Tiers

Tests are organized into three tiers of increasing complexity:

Tier Task Category Description Tests
Tier 1 Action Classification Identify action type: Non-Final, Final, Advisory, Allowance, Restriction 82
Tier 1 Timeline Analysis Extract mailing date, response period, and statutory deadlines 81
Tier 2 Fee Computation Calculate filing, search, examination, and extension fees by entity size 10
Tier 2 Deadline Calculation Compute exact response deadlines accounting for weekends and holidays 125
Tier 3 Legal Reasoning Draft traversal arguments for §101/102/103/112 rejections 25
Total 298*

*Tier 3 reasoning tasks are evaluated qualitatively and are presented as samples rather than scored numerically in the overall accuracy figure.

2.3 Scoring

Each test awards points for correct sub-components. For example, an Action Classification test awards 4 points: 1 for action type, 1 for finality, 1 for rejection basis, and 1 for claim count identification. Points are summed and divided by the maximum to yield accuracy percentages. All tests are deterministic and reproducible.

2.4 Technology Center Coverage

Office Actions were sampled from all eight major Technology Centers to ensure the benchmark is not biased toward any single patent domain:

TC 2800 — Electrical
19 tests
TC 3700 — Mechanical
14 tests
TC 3600 — Business
11 tests
TC 2400 — Networking
10 tests
TC 1700 — Chemical
10 tests
TC 2600 — Communications
9 tests
TC 1600 — Biotech
6 tests
TC 2100 — Software
3 tests

3. Results

3.1 Results by Task Type

Task Category Tests Points Earned Points Possible Accuracy
Action Classification 82 328 328 100.0%
Timeline Analysis 81 243 243 100.0%
Fee Computation 10 30 30 100.0%
Deadline Calculation 125 340 419 81.1%
Overall 298 941 1,020 92.3%

3.2 Accuracy by Task Type

Action Classification
100.0%
Timeline Analysis
100.0%
Fee Computation
100.0%
Deadline Calculation
81.1%

Note on Deadline Calculation Errors: All 79 lost points in Deadline Calculation trace to JavaScript date arithmetic edge cases — specifically, incorrect handling of month-end boundaries and federal holiday adjustments. The underlying legal reasoning (identifying the correct statutory period and extension rules) is correct in every case. These are implementation bugs, not reasoning failures.

3.3 Results by Technology Center

All Technology Centers achieved 100% accuracy on non-deadline tasks (Action Classification, Timeline Analysis, and Fee Computation), confirming that the benchmark generalizes across patent domains.

Technology Center Domain Tests Classification Timeline
TC 2800 Semiconductors, Electrical & Optical Systems 19 100% 100%
TC 3700 Mechanical Engineering, Manufacturing 14 100% 100%
TC 3600 Transportation, Construction, E-Commerce 11 100% 100%
TC 2400 Computer Networks, Multiplex, Security 10 100% 100%
TC 1700 Chemical & Materials Engineering 10 100% 100%
TC 2600 Communications, A/V, Measuring 9 100% 100%
TC 1600 Biotechnology & Organic Chemistry 6 100% 100%
TC 2100 Computer Architecture, Software 3 100% 100%

3.4 Point Distribution

The following chart shows the proportion of total points contributed by each task type, illustrating that Deadline Calculation dominates the scoring weight — and is the sole source of errors.

Classification (328 pts)
32.2%
Deadline (419 pts)
41.1%
Timeline (243 pts)
23.8%
Fee (30 pts)
2.9%

4. Tier 3 Reasoning Samples

Tier 3 evaluates the system's ability to generate substantive legal arguments in response to real USPTO rejections. The benchmark includes 25 reasoning tasks spanning the major rejection types:

Rejection Type Statute Count Description
Obviousness §103 8 Multi-reference combination arguments, motivation to combine
Anticipation §102 5 Single-reference novelty arguments, claim differentiation
Indefiniteness / Enablement §112 4 Specification support, written description, definiteness
Subject Matter Eligibility §101 4 Alice/Mayo two-step analysis, abstract idea arguments
Amendment Drafting 4 Claim narrowing to overcome prior art or eligibility rejections
Total 25

Below are two representative samples demonstrating the quality of generated arguments.

Sample 1: §103 Obviousness Traversal

Office Action Context

Application: Semiconductor packaging method with thermal management layer
TC: 2800 (Electrical)
Rejection: Claims 1–15 rejected under 35 U.S.C. §103 as obvious over Smith (US 10,XXX,XXX) in view of Chen (US 2021/0XXXXXX).

Generated Traversal Argument

Applicant respectfully traverses the rejection of Claims 1–15 under 35 U.S.C. §103.

The Examiner relies on Smith for the base semiconductor package structure and Chen for the thermal interface layer. However, the proposed combination fails for at least two independent reasons.

First, no motivation to combine. Smith's package design is explicitly optimized for minimal vertical profile (Smith, col. 4, ll. 30–45), and adding Chen's thermal layer would increase the Z-height by 15–25%, directly defeating Smith's stated objective. A person of ordinary skill would not be motivated to combine references where the combination destroys the principal benefit of the primary reference. See In re Gordon, 733 F.2d 900 (Fed. Cir. 1984) (no motivation to combine where combination would defeat the purpose of the primary reference).

Second, missing claim limitation. Neither Smith nor Chen, alone or in combination, teaches or suggests the claimed "graded thermal conductivity gradient decreasing from the die-attach surface to the board-attach surface" (Claim 1, lines 8–11). Chen's thermal layer has uniform conductivity throughout. Smith does not address thermal conductivity at all. The Examiner has not identified where this specific limitation is disclosed, and Applicant submits that it is not found in the cited art.

For at least these reasons, Applicant requests withdrawal of the §103 rejection.

Sample 2: §101 Alice/Mayo Eligibility Argument

Office Action Context

Application: Machine learning system for real-time fraud detection in payment networks
TC: 3600 (Business Methods / E-Commerce)
Rejection: Claims 1–20 rejected under 35 U.S.C. §101 as directed to the abstract idea of "detecting fraud," a method of organizing human activity.

Generated Traversal Argument

Applicant respectfully traverses the rejection of Claims 1–20 under 35 U.S.C. §101.

Step 2A, Prong One: The Examiner characterizes the claims as directed to "detecting fraud," but this improperly generalizes the claims at too high a level of abstraction. The claims are not directed to fraud detection in the abstract. Rather, Claim 1 recites a specific technical implementation: a trained neural network with a particular architecture (a temporal convolutional network with attention layers) that processes sequential transaction embeddings in real time. The Federal Circuit has repeatedly cautioned against oversimplifying claims at Step 2A. See Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1337 (Fed. Cir. 2016).

Step 2A, Prong Two: Even if the claims recite an abstract idea, they are integrated into a practical application. The claimed system produces a concrete technical improvement: sub-millisecond fraud scoring that enables real-time transaction authorization, which is impossible with conventional rule-based systems (Specification, ¶¶ [0031]–[0035]). The claims are analogous to those found eligible in DDR Holdings, LLC v. Hotels.com, 773 F.3d 1245 (Fed. Cir. 2014), where the court found eligibility because the claimed solution was "necessarily rooted in computer technology in order to overcome a problem specifically arising in the realm of computer networks."

Step 2B: Should the Office reach Step 2B, the specific combination of a temporal convolutional network with cross-transaction attention applied to payment network streams constitutes significantly more than any abstract idea. The Specification demonstrates this architecture achieves a 340x latency reduction over prior approaches (Specification, Table 3), confirming the claims recite an inventive concept.

For at least these reasons, Applicant requests withdrawal of the §101 rejection.

5. Limitations & Next Steps

5.1 Current Limitations

5.2 Planned Improvements

Priority Improvement Expected Impact
P0 Fix JS date arithmetic edge cases in deadline calculation Overall accuracy ~98%+
P1 Expand dataset to 200+ Office Actions with balanced TC representation Stronger statistical validity
P1 Automated Tier 3 evaluation using rubric-based LLM scoring Reproducible reasoning assessment
P2 Add design patent and PCT tasks Broader prosecution coverage
P2 Multi-round prosecution strategy benchmark End-to-end evaluation
P3 Comparative evaluation across multiple AI models Model selection guidance

5.3 Reproducibility

All test cases, expected outputs, and scoring logic are included in the PatentBench-Mini repository. Tests can be executed with a single command and produce deterministic results. The dataset of 321 USPTO applications is derived entirely from public USPTO records via the Patent Examination Data System (PEDS) and PAIR APIs.

6. Appendix

A. Expanded Dataset Statistics

Metric Count
Total USPTO applications in dataset 321
Applications with at least one Office Action 268
Office Actions used in benchmark 82
Technology Centers represented 8
Tier 1 test cases 163
Tier 2 test cases 135
Tier 3 reasoning samples 25

B. Rejection Type Distribution (Tier 3)

§103 Obviousness
8 cases
§102 Anticipation
5 cases
§112 Indefiniteness
4 cases
§101 Alice/Mayo
4 cases
Amendment Drafting
4 cases

C. Scoring Methodology Detail

Action Classification (4 points per test)

Timeline Analysis (3 points per test)

Fee Computation (3 points per test)

Deadline Calculation (variable points per test)

D. References