you can access the working prototype here: https://huggingface.co/spaces/Zayed024/AI-driven-research-engine-for-commercial-courts

AI-Driven Research Engine for Commercial Courts

Overview

This project is a prototype of an AI-Driven Research Engine designed to assist judges and judicial officers in India’s commercial courts. The goal is to ease the legal research process, leading to faster dispute resolution. The engine aggregates and processes legal data, generates customized research results, and uses Natural Language Processing (NLP) techniques to identify key legal principles, precedents, and case outcomes.

This project is built for Smart India Hackathon 2024, Problem Statement ID: 1701, focusing on leveraging AI technology to streamline legal research and support the Indian judiciary's efforts to expedite commercial dispute resolution.

Features

• PDF Document Processing: Extracts and processes text from PDF legal documents.
• NLP-based Analysis: Uses Named Entity Recognition (NER), Part-of-Speech (POS) tagging, and dependency parsing to analyze legal texts.
• Text Segmentation: Segments documents using TextTiling to create coherent text chunks.
• Customizable Query System: Allows users to ask legal questions, with responses generated based on the content of uploaded documents.
• Legal Text Embedding: Generates embeddings for legal texts using the InLegalBERT model.
• Predictive Analytics (Planned): The system will forecast case outcomes based on historical data and trends.
• Data Localization (Planned): Results tailored to the specific laws and rules of different Indian High Courts.
• Multilingual Support (Planned): Support for multiple Indian languages using NLP techniques.
• Ethical AI: Focuses on transparency, ensuring that the engine acts as a research facilitator, not a decision-maker.

Problem Statement

The project addresses the problem of faster resolution of commercial disputes in India, in line with the Commercial Courts Act, 2015. The research engine will help streamline the legal research process for commercial courts by:

• Aggregating data sources like case laws, statutory provisions, and rules.
• Extracting relevant legal information and identifying key principles and precedents.
• Customizing results based on the specific needs of each commercial suit.
• Ensuring localization of results for different Indian High Courts.
• Supporting multiple languages and upholding ethical considerations in the use of AI.

Technologies Used

• Python: Core programming language for the project.
• Gradio: Used for building the user-friendly web interface.
• Cohere API: For generating natural language responses based on legal queries.
• spaCy: For NLP tasks like NER, POS tagging, and dependency parsing.
• transformers: For BERT-based embeddings using the InLegalBERT model.
• PDFMiner: For extracting text from PDF documents.
• TextTilingTokenizer: For segmenting legal documents into coherent chunks.
• Torch: For handling transformer models and embeddings.
• nltk: For tokenization, POS tagging, and additional NLP tasks.

How It Works

1. Upload a PDF Document: The user uploads a legal document in PDF format.
2. Ask a Legal Question: The user inputs a legal query based on the document.
3. NLP and Text Processing: The engine processes the document, extracts legal information, and segments the text for deeper analysis.
4. BERT Embeddings: Legal text is transformed into embeddings using the InLegalBERT model.
5. Generate Response: The system generates a response based on the document content and the user query, leveraging Cohere’s natural language generation.
6. Display Results: The results, including legal principles, precedents, and a custom answer, are displayed in a user-friendly format.

Planned Enhancements

• Predictive Analytics: Implementing machine learning models to forecast case outcomes based on historical data.
• Multilingual Support: Adding support for multiple Indian languages to make the engine accessible to diverse users.
• Data Localization: Providing localized results based on the specific High Court’s laws and rules.
• Advanced Customization: Allowing users to specify the nature of the case (e.g., contract law, intellectual property) for more relevant results.
• Ethical Transparency: Ensuring explainability and bias mitigation in AI-based decision-making.

License

This project is licensed under the MIT License. See the LICENSE file for more details.