uspto_filtered

# USPTO

## Description
In the United States, patent documents are released into the public domain as government works. Patents follow a highly standardized format with distinct required sections for background, detailed description, and claims. We include patents from the US Patents and Trademark Office (USPTO) as provided by the [Google Patents Public Data dataset](https://patents.google.com/), which includes millions of granted patents and published patent applications dating back to 1782. We processed these documents to extract clean text while preserving this structured format. Mathematical expressions and equations were converted into LaTeX.

## Dataset Statistics
| Documents | UTF-8 GB |
|-----------|----------|
| 17,030,231 | 661.1 |

## License Issues
While we aim to produce datasets with completely accurate licensing information, license laundering and inaccurate metadata can cause us to erroneously assign the incorrect license to some documents (for further discussion of this limitation, please see [our paper](https://huggingface.co/papers/2506.05209)). If you believe you have found an instance of incorrect licensing in this dataset, please [start a discussion](https://github.com/r-three/common-pile/discussions/new) on this repository.

## Other Versions
This is the "filtered" version of the USPTO dataset. If you are looking for the raw version, you can find it [here](https://huggingface.co/datasets/common-pile/uspto_raw).

## Citation
If you use this dataset, please cite:
```bibtex
@article{kandpal2025common,
title={{The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text}},
author={Nikhil Kandpal and Brian Lester and Colin Raffel and Sebastian Majstorovic and Stella Biderman and Baber Abbasi and Luca Soldaini and Enrico Shippole and A. Feder Cooper and Aviya Skowron and Shayne Longpre and Lintang Sutawika and Alon Albalak and Zhenlin Xu and Guilherme Penedo and Loubna Ben and Elie Bakouch and John David and Honglu Fan and Dashiell Stander and Guangyu Song and Aaron Gokaslan and John Kirchenbauer and Tom Goldstein and Brian R and Bhavya Kailkhura and Tyler Murray},
journal={arXiv preprint},
year={2025}
}
```

# USPTO

## 描述
在美国，专利文档作为政府作品被纳入公有领域。专利采用高度标准化的格式，包含背景技术、详细说明以及权利要求书这几个强制规定的专属章节。本数据集收录了源自[谷歌专利公共数据集合（Google Patents Public Data dataset）](https://patents.google.com/)的美国专利商标局（United States Patents and Trademark Office，USPTO）专利文档，其中包含自1782年起的数百万件已授权专利与已公开专利申请文件。我们对这些文档进行了处理，在保留其结构化格式的前提下提取纯净文本，其中数学表达式与公式均被转换为LaTeX格式。

## 数据集统计
| 文档数量 | UTF-8 存储体积（GB） |
|-----------|----------|
| 17,030,231 | 661.1 |

## 授权许可问题
尽管我们致力于提供完全准确的授权许可信息，但授权洗白与元数据不准确可能会导致我们错误地为部分文档分配了错误的授权许可。关于该局限性的进一步讨论，请参阅[我们的论文](https://huggingface.co/papers/2506.05209)。若您发现本数据集中存在授权许可标注错误的情况，请前往该仓库[发起讨论](https://github.com/r-three/common-pile/discussions/new)。

## 其他版本
本数据集为USPTO数据集的「过滤版」。若您需要原始版本，可前往[此处](https://huggingface.co/datasets/common-pile/uspto_raw)获取。

## 引用格式
若您使用本数据集，请引用如下文献：
bibtex
@article{kandpal2025common,
title={{The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text}},
author={Nikhil Kandpal and Brian Lester and Colin Raffel and Sebastian Majstorovic and Stella Biderman and Baber Abbasi and Luca Soldaini and Enrico Shippole and A. Feder Cooper and Aviya Skowron and Shayne Longpre and Lintang Sutawika and Alon Albalak and Zhenlin Xu and Guilherme Penedo and Loubna Ben and Elie Bakouch and John David and Honglu Fan and Dashiell Stander and Guangyu Song and Aaron Gokaslan and John Kirchenbauer and Tom Goldstein and Brian R and Bhavya Kailkhura and Tyler Murray},
journal={arXiv preprint},
year={2025}
}