Datasets:

ExponentialScience
/

DLT-Patents

dataset_info:
  features:
    - name: text
      dtype: string
    - name: Patent Number
      dtype: string
    - name: Document ID
      dtype: string
    - name: Date Published
      dtype: string
    - name: Family ID
      dtype: int64
    - name: Title
      dtype: string
    - name: CPCI
      dtype: string
    - name: CPCA
      dtype: string
    - name: Inventor
      dtype: string
    - name: Assignee
      dtype: string
    - name: Application Number
      dtype: string
    - name: Filing Date
      dtype: string
    - name: Primary Examiner
      dtype: string
    - name: Assistant Examiner
      dtype: string
    - name: OR
      dtype: string
    - name: XREF
      dtype: string
    - name: Applicant Name
      dtype: string
    - name: Notes
      dtype: string
    - name: Notes/Tagged
      dtype: string
    - name: Relevancy
      dtype: float64
    - name: Database
      dtype: string
    - name: total_tokens
      dtype: int64
  splits:
    - name: train
      num_bytes: 6273796410
      num_examples: 49023
  download_size: 2287664239
  dataset_size: 6273796410
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
language:
  - en
tags:
  - DLT
  - Blockchain
  - Cryptocurrencies
  - Bitcoin
  - Ethereum
  - Crypto
  - Patents
pretty_name: Distributed Ledger Technology (DLT) / Blockchain Patents
size_categories:
  - 10K<n<100K

DLT-Patents

Dataset Description

Dataset Summary

DLT-Patents is a comprehensive corpus of patent documents related to Distributed Ledger Technology (DLT). This dataset is part of the larger DLT-Corpus collection, designed to support NLP research, innovation studies, and patent analysis in the DLT domain.

The dataset contains 49,023 patent documents with 1,296 million tokens (1.296 billion tokens), spanning patents from 1990 to 2025. All documents are in English and sourced from the United States Patent and Trademark Office (USPTO).

This dataset is part of the DLT-Corpus collection. For the complete corpus including scientific literature and social media data, see: https://huggingface.co/collections/ExponentialScience/dlt-corpus-68e44e40d4e7a3bd7a224402

Languages

English (en)

Dataset Structure

Data Fields

Each patent document in the dataset contains the following fields:

Patent Number: USPTO patent number (e.g., US10123456B2)
Document ID: Unique document identifier
Title: Title of the patent
text: Complete patent text including abstract, claims, and description
Date Published: Date the patent was published
Filing Date: Date the patent application was filed
Family ID: Patent family identifier
Application Number: Patent application number
Inventor: List of inventors
Assignee: Patent assignees (companies or individuals holding the patent)
Applicant Name: Name of the patent applicant
Primary Examiner: Name of the primary patent examiner
Assistant Examiner: Name of the assistant examiner
CPCI: Cooperative Patent Classification Invention codes
CPCA: Cooperative Patent Classification Additional codes
OR: Original reference information
XREF: Cross-reference information
Relevancy: Relevancy score or classification
Notes: Additional notes about the patent
Notes/Tagged: Tagged notes or annotations
Database: Source database (USPGPUB or USPAT)
total_tokens: Total number of tokens in the document

Data Splits

This is a single corpus without predefined splits. Users should create their own train/validation/test splits based on their specific research needs. Consider temporal splits to avoid data leakage in time-series analyses.

Dataset Creation

Curation Rationale

DLT-Patents was created to address the lack of large-scale, domain-specific patent corpora for NLP and innovation research in the Distributed Ledger Technology field. Patents provide unique insights into:

Commercial applications of DLT technology
Technical innovations and their evolution
Industry trends and competitive landscapes
The transition from research to practical implementation

Source Data

Data Collection

Patents were retrieved from USPTO public databases, specifically:

USPGPUB: Published patent applications
USPAT: Granted patents

Data Processing

The collection process involved:

Text extraction: Extracting text from USPTO XML and full-text databases
Formatting standardization: Normalizing text format and structure
Encoding correction: Fixing character encoding errors and special characters
Deduplication: Removing duplicate entries and ensuring unique patents
Quality filtering: Removing incomplete or corrupted documents

Personal and Sensitive Information

This dataset contains only publicly available patent documents from the USPTO. Inventor and assignee names are retained as they appear in official patent records, which is standard practice for patent documentation. No personal or confidential information beyond what is in public patent records is included.

Considerations for Using the Data

Discussion of Biases

Potential biases include:

Geographic bias: Only US patents are included; international patents are not represented
Language bias: Only English-language patents are included
Temporal bias: More recent years have significantly more patents due to the growth of DLT technology
Entity bias: Large corporations may be over-represented compared to individual inventors
Technology bias: Certain DLT applications (e.g., cryptocurrency) may be over-represented compared to others

Other Known Limitations

USPTO only: Dataset only includes US patents, missing international innovations
Temporal lag: Recently filed patents may not yet be published (18-month publication delay)
Keyword limitations: Some relevant patents may be missed due to evolving terminology
Legal complexity: Patent language is highly technical and legally precise, which may limit general NLP applicability

Additional Information

Dataset Curators

Walter Hernandez Cruz, Peter Devine, Nikhil Vadgama, Paolo Tasca, Jiahua Xu

Licensing Information

Public Domain under USPTO's Terms of Service (TCS).

Patent text is typically not subject to copyright restrictions per USPTO's Terms of Service. Users are free to use, reproduce, and distribute this data. However, users should note:

The content of patents (the inventions themselves) may be protected by patent rights
Using patented technologies may require licensing from patent holders
This dataset provides access to patent text for research purposes, not rights to use patented technologies

For more information, see: https://www.uspto.gov/terms-use-uspto-websites

Citation Information

@article{hernandez2025dlt-corpus,
  title={DLT-Corpus: A Large-Scale Text Collection for the Distributed Ledger Technology Domain},
  author={Hernandez Cruz, Walter and Devine, Peter and Vadgama, Nikhil and Tasca, Paolo and Xu, Jiahua},
  year={2025}
}