Legal Document Understanding Dataset for Trade Secret Misappropriation Cases
- Records
- 5000
- Size
- 0.2GB
- Format
- JSON
- Provider
MOBIUSI INC- Updated
- v1.02026-11-05
This dataset covers pleadings, evidence review opinions, and judicial decisions in trade secret misappropriation cases, addressing issues such as secrecy, reasonable confidentiality measures, access, use or disclosure, causation, and damages. Its structured information includes document types, case parties, claims and defenses, evidence, element analysis, and judgments, supporting models that interpret fact-intensive IP dispute documents and connect evidence with legal elements.
Overview
Dataset Insights
Preview
Sample Examples
Schema
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| case_parties | array | Parties extracted from the document, including their litigation roles and positions. |
| document_text | string | Original text of a pleading, evidence review opinion, or judicial decision from a trade secret misappropriation case. |
| document_type | string | Document type identified from the text, such as a complaint, answer, evidence review opinion, judgment, or ruling. |
| evidence_items | array | Evidence materials, their stated purposes, and the parties' opinions on authenticity, relevance, and admissibility. |
| element_findings | array | Analysis connecting case facts and evidence to elements such as secrecy, confidentiality measures, access, use or disclosure, and causation. |
| damage_assessment | object | Structured representation of claimed losses, calculation bases, causation, and the compensation amount ultimately determined by the court. |
| claims_and_defenses | object | Structured representation of the rights holder's claims, the accused party's defenses, and the issues in dispute. |
| judgment_conclusion | object | Liability findings, judgment outcome, remedies, and reasoning extracted from a judicial decision. |
Trust
Compliance Statement
Authorization Type
Proprietary - Commercial AI Training License (No Redistribution)
Commercial Use
Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization
No PII, no real company names, simulated scenarios follow industry standards
Compliance System
Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs
FAQ
Frequently Asked Questions
- What types of case documents are included?
- The dataset covers litigation documents, evidence review opinions, and judicial decisions in trade secret misappropriation cases. Examples may include complaints, answers, judgments, and rulings; the actual document types depend on the individual records.
- What information is structured in the dataset?
- Structured information includes case parties, claims and defenses, evidence and review opinions, and analysis of issues such as secrecy, confidentiality measures, access, use or disclosure, damages, and judicial conclusions.
- What tasks can this dataset support?
- It can support legal document understanding, extraction of claims and defenses, evidence-to-element analysis, identification of damages information, and judgment outcome extraction. It may also be used for fine-tuning and evaluating models for IP law.
- How can I obtain the dataset, and what licensing applies?
- Please check the product page or contact the data provider for availability, delivery format, and licensing terms. Before use, verify the applicable license and comply with relevant privacy, data security, and legal document usage requirements.
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Cite this Work
@dataset{Mobiusi2026,
title={Legal Document Understanding Dataset for Trade Secret Misappropriation Cases},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/1c166e1eec2863355cf3501a036de682?dataset_scene_id=8},
urldate={2026-10-15},
keywords={trade secret misappropriation dataset,trade secret case documents,legal document understanding,IP litigation data,claims and defenses extraction,legal evidence analysis,judicial decision dataset,legal language model training},
version={1.0}
}Using this in research? Please cite us.