---
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**.