README.md

---
license: cc-by-nc-4.0
language:
- en
- zh
tags:
- patent
- design-patent
- freedom-to-operate
- fto
- image-retrieval
- cross-modal
- visual-similarity
- information-retrieval
- evaluation
- benchmark
task_categories:
- image-to-image
- image-classification
pretty_name: PatSnap Design FTO Bench
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test.parquet
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: query_img_id
    dtype: string
  - name: query_pn
    dtype: string
  - name: query_img_path
    dtype: image
  - name: target_pns
    sequence: string
  - name: target_img_ids
    sequence: string
  - name: pair_name
    dtype: string
  - name: picture_type
    dtype: string
  - name: one_level_loc
    dtype: string
  - name: two_level_loc
    dtype: string
  - name: country
    sequence: string
  - name: version
    dtype: string
  splits:
  - name: test
    num_examples: 91
---

# PatSnap Design FTO Bench

A Bench for evaluating **design patent Freedom-To-Operate (FTO)** retrieval systems on cross-modal image search. Each sample provides a query product image (or design patent figure) plus the ground truth set of target design patents that constitute infringement risk, as confirmed by patent invalidation proceedings.

> 🐙 **GitHub mirror:** This dataset is also published as part of the [`patsnap/patent-bench`](https://github.com/patsnap/patent-bench/tree/main/design-fto-bench) monorepo, where you can find the reference metric scripts (`search_metrics.py`) and additional sub-Benches.

## Dataset Overview

| Property | Value |
|----------|-------|
| **Total samples** | 91 |
| **Source** | Real patent invalidation proceedings |
| **Jurisdictions** | CN (100% for the released subset) |
| **Modality** | PNG images (product photo ↔ patent line drawing / photo) |
| **Image directory** | `data/image/<jurisdiction>/<class>/<sub>/<pn>/<file>.png` (91 PNG files, ~6.8 MB) |
| **Ground truth** | Patent pairs confirmed as infringement-equivalent through patent invalidation proceedings |
| **Locarno (LOC) coverage** | All 26 first-level LOC classes |
| **License** | CC BY-NC 4.0 |

## Quick Start

```python
from datasets import load_dataset

ds = load_dataset("PatSnap/design-fto-bench", split="test")
print(f"Total samples: {len(ds)}")

# Inspect one sample
sample = ds[0]
print(sample["query_pn"], sample["pair_name"])

# query_img_path is a PIL Image (bytes embedded in the Parquet, no external lookup needed)
img = sample["query_img_path"]
print(f"Query image: {img.size}, mode={img.mode}")

# Targets are the set of design patents whose images constitute infringement risk
print(sample["target_pns"], sample["target_img_ids"])
```

## Data Fields

| Field | Type | Description |
|-------|------|-------------|
| `id` | int64 | Sample identifier |
| `query_img_id` | string | Identifier of the query image |
| `query_pn` | string | Publication number of the query patent (PatSnap standardized PN) |
| `query_img_path` | string | Relative path to the query image under `data/image/` |
| `target_pns` | list[string] | Ground truth target design-patent PNs that constitute infringement risk |
| `target_img_ids` | list[string] | Image identifiers of the target patents |
| `pair_name` | string | Pair identifier from the invalidation proceeding |
| `picture_type` | string | Source of the GT pair (e.g. 无效 = invalidation proceeding) |
| `one_level_loc` | string | First-level Locarno classification code |
| `two_level_loc` | string | Second-level Locarno classification code (e.g. 14-03) |
| `country` | list[string] | Country/jurisdiction codes of the sample |
| `version` | string | Dataset version (e.g. 1.1) |

## How to Use the Query

The query input is the **query product image** at `data/image/<query_img_path>`. Each sample's `target_pns` lists the design patents that an FTO retrieval system should return.

## Evaluation Metrics

| Metric | Description |
|--------|-------------|
| **Hit Rate @ K** | % of samples with ≥1 GT patent in top K (K = 10, 50, 100, 200) |
| **PRES @ N** | Patent Retrieval Evaluation Score (Magdy & Jones 2010, with miss-penalty correction): single score in `[0, 1]` jointly capturing how many GT patents are retrieved within top-N and how highly they are ranked. PRES = 1.0 means every GT patent appears at the top; PRES = 0 means none are found within N. Default N = 200. |

The reference metric scripts (with `strict / leaderboard` mode by default and ranked-list schema validation) are available in the [`patsnap/patent-bench`](https://github.com/patsnap/patent-bench/blob/main/common/metrics/search_metrics.py) GitHub repo.

### Scoring Grades (Hit Rate @ Top@100)

| Grade | Hit Rate | Description |
|-------|----------|-------------|
| **A** | ≥ 90% | Excellent — suitable for direct professional use |
| **B** | ≥ 75% | Good — effective as a high-efficiency screening tool |
| **C** | ≥ 60% | Acceptable — requires human review of key results |
| **D** | < 60% | Below standard — model improvement needed |

## Distribution

### By Jurisdiction

| Jurisdiction | Count | Percentage |
|--------------|-------|------------|
| CN | 91 | 100% |

> The v1.1 public release contains only invalidation-proceeding samples (CN). Future releases (v2) will incorporate cross-jurisdiction TRO data (US/EP/JP).

### By Locarno Classification

Coverage spans all 26 first-level LOC classes.

## Limitations

- **Retrieval-only Bench**: Evaluates the search/retrieval step only; does not cover infringement adjudication or court-ruling outcomes.
- **GT based on invalidation proceedings**: This subset (v1.1) is restricted to CN invalidation-proceeding pairs. E-commerce infringement-complaint samples are retained internally for client confidentiality.
- **Single-jurisdiction**: CN only in this release.
- **Visual similarity ≠ legal infringement**: A retrieval system returning a top-1 hit does not constitute a legal infringement determination; results are inputs to professional FTO review.

## Citation

```bibtex
@dataset{patsnap_design_fto_bench_2026,
  title  = {PatSnap Design FTO Bench},
  author = {PatSnap},
  year   = {2026},
  url    = {https://huggingface.co/datasets/PatSnap/design-fto-bench},
  note   = {A Bench for evaluating design-patent freedom-to-operate image-retrieval systems}
}
```

## License

Released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) — research and non-commercial evaluation purposes only.

## Try the Production System

Experience the **PatSnap Design FTO AI Agent** — the commercial system referenced in this Bench.

🔗 [Try it on PatSnap Eureka](https://eureka.patsnap.com/ip/checking/?from=benchmark_huggingface#/design-fto)