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language:
- en
license: cc-by-sa-4.0
task_categories:
- image-to-text
- text-to-image
- image-classification
- zero-shot-image-classification
tags:
- patent
- design-patent
- technical-drawing
- wireframe
- multimodal
- retrieval
- reconstruction
- USPTO
dataset_info:
features:
- name: figure_id
dtype: string
- name: patent_id
dtype: string
- name: image
dtype: image
- name: patent_title
dtype: string
- name: caption
dtype: string
- name: drawing_description
dtype: string
- name: detailed_description
dtype: string
- name: brief_summary
dtype: string
- name: claims
dtype: string
- name: viewpoint
dtype: string
- name: figure_number
dtype: int32
- name: n_figures_in_patent
dtype: int32
- name: sibling_figure_ids
sequence: string
- name: reference_numerals
sequence:
struct:
- name: numeral
dtype: string
- name: label
dtype: string
- name: locarno_class
dtype: string
- name: patent_date
dtype: string
- name: year
dtype: int32
splits:
- name: train
num_examples: 500
---
# Patent Wireframes
Structured patent figure dataset for multimodal understanding — generation, reconstruction, retrieval, and cross-modal association.
## Overview
Each record is a single patent figure paired with:
- Full patent text (drawing description, detailed description, brief summary, claims)
- IMPACT-generated figure caption
- Extracted reference numeral mappings (numeral → component label)
- Sibling figure links (other views of the same object)
- Classification metadata (Locarno codes, dates)
## Sources
- **Images + captions**: [IMPACT](https://huggingface.co/datasets/AI4Patents/IMPACT) (CC-BY-SA-4.0)
- **Patent text**: [PatentsView](https://patentsview.org/) (CC-BY-4.0)
- **Scope**: US design patents, initially 2022
## Schema
| Field | Type | Description |
|---|---|---|
| `figure_id` | string | Unique: `{patent_id}_{figure_number}` |
| `patent_id` | string | USPTO design patent number |
| `image` | image | Figure raster |
| `patent_title` | string | Patent title |
| `caption` | string | IMPACT-generated caption |
| `drawing_description` | string | Drawing description from patent text |
| `detailed_description` | string | Full detailed description |
| `brief_summary` | string | Brief summary |
| `claims` | string | Patent claims |
| `viewpoint` | string | front / side / perspective / etc. |
| `figure_number` | int32 | Figure index within patent |
| `n_figures_in_patent` | int32 | Total figures in patent |
| `sibling_figure_ids` | list[string] | Other figure IDs from same patent |
| `reference_numerals` | list[{numeral, label}] | Extracted component mappings |
| `locarno_class` | string | Locarno classification code |
| `patent_date` | string | Grant date |
| `year` | int32 | Grant year |
## Intended uses
- **Reconstruction**: predict figure from text description (or vice versa)
- **Retrieval**: text-to-figure, figure-to-figure, component-to-figure
- **Generation**: produce patent figures from structured descriptions
- **Multi-view reasoning**: predict unseen views given other views
- **Component understanding**: reference numeral grounding
## Citation
If you use this dataset, please cite:
```bibtex
@dataset{patent_wireframes,
title={Patent Wireframes: A Structured Dataset for Multimodal Patent Figure Understanding},
year={2026},
url={https://huggingface.co/datasets/midah/patent-wireframes},
}
```
Built on [IMPACT](https://openreview.net/forum?id=l0Ydsl10ci) (NeurIPS 2024) and [PatentsView](https://patentsview.org/).
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