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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-benchmonorepo, 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
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 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
@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 — 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.
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