--- 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/////.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/`. 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)