Legal Document Generation Using Probabilistic Prediction
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Solution Overview
Problem
The existing technologies for generating legal documents lack the ability to predict and select customary legal documents and clauses accurately for non-routine transactions, often requiring manual intervention and expertise, which is impractical for modest transactions, and do not utilize learning techniques to create or modify rules.
Innovation Solution
A system and method that uses supervised machine learning to predict the selection of legal documents and clauses based on transaction data, combining expert-provided rules with user input, and provides reliability ratings, enabling the generation of legally binding documents through a document management system by constructing classifiers and a rules engine that predicts the usage probability of documents and clauses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised machine learning is used to predict document selection, then document selection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent introduces a rules engine as an intermediary layer between the machine learning classifier and the document generation process. The rules engine translates probabilistic predictions into actionable document selection decisions, simplifying the overall system architecture while maintaining high accuracy through the classifier's probabilistic predictions of customary document usage based on transaction data
2Reliability
If machine learning classifiers are constructed based on training data, then prediction reliability is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary actions by constructing and training machine learning classifiers in advance using historical transaction data and document selection patterns. These pre-trained classifiers are then reused for making predictions on new transactions, significantly reducing the processing time for individual cases while maintaining high prediction reliability based on the training data
3Measurement precision
If probabilistic prediction of customary usage is implemented, then document selection precision is improved, but computational resources increase
Solution Approach 1:
The patent changes the parameter of document selection from deterministic rule-based matching to probabilistic prediction based on customary usage patterns. The machine learning classifier analyzes transaction data to predict the probability of specific document selections, improving precision by capturing nuanced patterns that traditional rules miss while optimizing computational resource usage through efficient algorithm selection
Data Source
AI summary
A system and method for the automated generation of documents for a legal transaction over a network using probabilistic prediction of customary usage. The predictions are generated by user experience, expert rules and machine learned classifiers based on user input of transaction data. The classifiers are constructed and tested on a partitioned dataset consisting of transaction data and legal document and clause selections in previous transactions. In one embodiment, such dataset is collected in a document management system.