| --- |
| license: cc-by-4.0 |
| task_categories: |
| - text-classification |
| language: |
| - en |
| tags: |
| - legal |
| - patent |
| - benchmark |
| - ptab |
| - nllp |
| pretty_name: PILOT-Bench |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # Dataset Card for PILOT-Bench |
|
|
| ## Dataset Description |
| PILOT-Bench (Patent InvaLidation Trial Benchmark) is a benchmark dataset designed to evaluate the structured legal reasoning capabilities of Large Language Models (LLMs) based on U.S. Patent Trial and Appeal Board (PTAB) decision documents. |
|
|
| - **Repository:** [TeamLab/pilot-bench](https://github.com/TeamLab/pilot-bench) |
| - **Paper:** [PILOT-Bench: A Benchmark for Legal Reasoning in the Patent Domain with IRAC-Aligned Classification Tasks](https://aclanthology.org/2025.nllp-1.17/) |
| - **Point of Contact:** Yehoon Jang (jangyh0420@pukyong.ac.kr) |
|
|
| ### Dataset Summary |
| PILOT-Bench aligns PTAB appeal cases with USPTO patent data at the case level. It formalizes three classification tasks aligned with the **IRAC (Issue, Rule, Application, Conclusion)** framework, the standard for legal analysis. This dataset aims to measure how logically LLMs can understand and classify unstructured legal documents. |
|
|
| ## Dataset Structure |
|
|
| ### Data Instances |
| The data is stored in `pilot-bench.tar.gz`. Each instance consists of metadata and text segments partitioned into the IRAC structure using Gemini-2.5-pro. |
|
|
| ```json |
| { |
| "file_name": "2017002267_DECISION", |
| "appellant_arguments": "...", |
| "examiner_findings": "...", |
| "ptab_opinion": "...", |
| "issue_type": ["103"], |
| "board_rulings": ["37 CFR 41.50", "37 CFR 41.50(f)"], |
| "subdecision": "Affirmed-in-Part" |
| } |
| ``` |
|
|
| ### Data Fields |
| The key fields in `ptab.json` and the `opinion_split` data are as follows: |
|
|
| | Field Name (Key) | Type | Description | |
| | :--- | :---: | :--- | |
| | **`proceedingNumber`** | `int` | **PTAB Proceeding Number**. A unique ID identifying each appeal case. | |
| | **`appellant_arguments`** | `str` | **Appellant Arguments**. Legal grounds and arguments from the appellant, extracted via Gemini-2.5-pro. | |
| | **`examiner_findings`** | `str` | **Examiner Findings**. Sections containing the examiner's reasons for rejection and underlying facts. | |
| | **`ptab_opinion`** | `str` | **Board Opinion**. The full text of the legal judgment rendered by the PTAB. | |
| | **`issue_type`** | `list[str]` | **Legal Issue**. List of statutory grounds involved (e.g., 35 U.S.C. §101, 102, 103, 112). | |
| | **`board_rulings`** | `list[str]` | **Board Authorities (Rule)**. Procedural provisions cited by the Board (e.g., 37 C.F.R. § 41.50). | |
| | **`subdecision`** | `str` | **Conclusion (Fine-grained)**. Represents 23 specific outcome types (e.g., Affirmed, Reversed). | |
| | **`subdecision_coarse`** | `str` | **Conclusion (Coarse-grained)**. Outcomes simplified into 6 categories for analysis convenience. | |
| | **`respondentPatentNumber`** | `str` | The **U.S. Patent Number** subject to the appeal. | |
| | **`decisionDate`** | `str` | The **Date** the final decision was rendered by the PTAB. | |
|
|
| --- |
|
|
| ### Data Instance Example |
| ```json |
| { |
| "proceedingNumber": 2017002267, |
| "appellant_arguments": "The Appellant argues that the Examiner erred in finding...", |
| "examiner_findings": "The Examiner maintains the rejection of claims 1-10 under 35 U.S.C. 103...", |
| "ptab_opinion": "We have reviewed the arguments and find that the Examiner's position...", |
| "issue_type": ["103"], |
| "board_rulings": ["37 CFR 41.50"], |
| "subdecision": "Affirmed", |
| "file_name": "2017002267_DECISION" |
| } |
| ``` |
|
|
| ## How to use |
|
|
| You can easily load the dataset using the Hugging Face `datasets` library. Since the data is stored in a compressed format (`pilot-bench.tar.gz`), you should specify the file in the `data_files` parameter. |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Load the dataset |
| dataset = load_dataset("Yehoon/pilot-bench", data_files="pilot-bench.tar.gz") |
| ``` |
|
|
|
|
| ## Dataset Creation |
|
|
| ### Curation Rationale |
| While existing legal domain datasets often focus on simple document classification, **PILOT-Bench** was built to systematically evaluate how well LLMs understand the **IRAC (Issue-Rule-Application-Conclusion)** framework within the highly specialized patent domain. Specifically, it was curated to separate unstructured decisions into logical units, allowing models to utilize step-by-step information. |
|
|
| ### Source Data |
| - **Data Collection**: Collected from the USPTO Open Data portal and public PTAB (Patent Trial and Appeal Board) records. |
| - **Data Preprocessing**: |
| - After collecting raw PDF and text data, the full texts were split into logical sections (`appellant_arguments`, `examiner_findings`, `ptab_opinion`) using the **Gemini-2.5-pro** model. |
| - Metadata such as patent numbers, application numbers, and decision dates were aligned for each case into `ptab.json`. |
|
|
| ## Considerations for Using the Data |
|
|
| ### Social Impact of Dataset |
| This dataset can assist patent legal experts in decision-making and activate research on the automated analysis of complex patent appeal documents. Using the IRAC structure serves as a foundation for improving the explainability of legal AI. |
|
|
| ### Limitations and Bias |
| - **Domain Specificity**: This dataset is limited to U.S. PTAB Ex parte appeals; caution is needed when generalizing to other jurisdictions or legal fields (e.g., criminal, civil). |
| - **Preprocessing Noise**: Since an LLM (Gemini) was used for section splitting, there may be rare instances of split errors or noise. |
|
|
| ## Additional Information |
|
|
| ### Citation Information |
| If you use this dataset or code in your research, please cite: |
|
|
| ```bibtex |
| @inproceedings{jang2025pilotbench, |
| title = {PILOT-Bench: A Benchmark for Legal Reasoning in the Patent Domain with IRAC-Aligned Classification Tasks}, |
| author = {Yehoon Jang and Chaewon Lee and Hyun-seok Min and Sungchul Choi}, |
| year = {2025}, |
| booktitle = {Proceedings of the EMNLP 2025 (NLLP Workshop)}, |
| url = {[https://github.com/TeamLab/pilot-bench](https://github.com/TeamLab/pilot-bench)} |
| } |
| ``` |
|
|
| ## License & Disclaimer |
|
|
| ### License |
| This dataset is provided under the **[Creative Commons Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)** license. |
|
|
| ### Disclaimer |
| - **Research and Education**: This dataset is constructed purely for research and educational purposes. |
| - **Legal Liability**: The analysis results from this dataset do not have legal effect and should not be used as a tool for legal advice, automated adjudication, or practical PTAB decision-making. |
| - **Data Quality**: The research team is not responsible for potential noise arising from the LLM-based splitting process. |
|
|
| ## Acknowledgments |
| This work was supported by |
| - **National Research Foundation of Korea (NRF)** – Grant No. RS-2024-00354675 (70%) |
| - **IITP (ICT Challenge and Advanced Network of HRD)** – Grant No. IITP-2023-RS-2023-00259806 (30%) |
| under the supervision of the **Ministry of Science and ICT (MSIT), Korea**. |
