Unstructured Data Entity Extraction for Contract Generation
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Solution Overview
Problem
Small business owners face inefficiencies and errors when creating invoices and contracts from unstructured email data, as manually copying information is time-consuming and prone to errors.
Innovation Solution
A system and method that extracts entities from unstructured data sources using natural language processing, generates a contract model, and predicts contract details by correlating structured data, providing a proposed contract with confidence scores that can be edited and refined by users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual copying of information from email threads into invoices is performed, then the invoice can be created, but the process is time-consuming and error-prone
Solution Approach 1:
The patent replaces the mechanical manual copying process with an automated natural language processing system that extracts entities from unstructured email data and populates invoice fields automatically, eliminating the need for manual transcription while maintaining accuracy
Solution Approach 2:
The system enables the invoice creation process to serve itself by automatically extracting relevant information from email threads and populating the invoice template without human intervention, allowing the process to complete autonomously
2Reliability
If manual copying of information from email threads into invoices is performed, then the invoice can be created, but errors may occur in the invoice
Solution Approach 1:
The patent replaces error-prone manual copying with an automated NLP-based entity extraction system that systematically identifies and extracts relevant information from unstructured email data, reducing human error while managing complexity through algorithmic processing
Solution Approach 2:
The patent introduces an intermediary natural language processing layer between the unstructured email data and the invoice template, which acts as a mediator to extract, validate, and map entities appropriately, ensuring accuracy while handling the complexity of data transformation
3Productivity
If similar issues arise when sending contracts based on unstructured data sources, then the contract can be generated, but the process remains inefficient and error-prone
Solution Approach 1:
The patent creates a universal entity extraction system that can handle multiple document types (invoices, contracts, agreements) from unstructured data sources, making the system multi-functional and applicable across different business documentation needs without requiring separate manual processes for each document type
Data Source
AI summary
A method may include extracting first entities from a first portion of an unstructured data source associated with a user, obtaining, based on the first entities, a contract model including elements and a contract type, generating, by applying the contract model to the first entities, a proposed contract including a contract score and, for each element, element values. Each element value may include an element value score. The method may further include identifying a structured data source associated with the user, obtaining, from the structured data source, structured data corresponding to the first entities, correlating the structured data with an element value of the proposed contract, and modifying, by applying the contract model to the structured data, the element value score of the element value.