Contract Term Recognition and Favorability Analytics for Faster Decisions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Consumers often sign contractual documents without fully understanding the terms, leading to resource wastage and potential unfavorable agreements due to lack of time and understanding.
Innovation Solution
A document management platform that uses image data processing and machine learning to identify key terms in contractual documents, determine their favorability, and generate recommendations for users on whether to accept or modify the terms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a purchaser reads and understands the contractual document manually, then the purchaser can understand the terms, but it consumes significant time and may still lead to misunderstanding
Solution Approach 1:
The patent introduces an intermediary system (image processing device) that mediates between the contractual document and the purchaser. The device captures images of the document, processes them through OCR and NLP techniques, and generates summaries with key terms and recommendations. This intermediary handles the time-consuming analysis task, allowing the purchaser to quickly understand essential terms without manually reading the entire document.
Solution Approach 2:
The patent replaces the mechanical human reading and comprehension process with an automated computational system. Instead of relying on human eyes to read and brains to comprehend lengthy contractual documents, the system uses optical character recognition (OCR), natural language processing (NLP), and machine learning algorithms to automatically extract, analyze, and summarize key information, substituting mechanical human cognitive processes with automated computational mechanisms.
2Productivity
If the purchaser signs the document quickly, then the transaction process is efficient, but the purchaser may not understand unfavorable terms
Solution Approach 1:
The patent performs preliminary analysis of the contractual document before the purchaser makes a decision. The system pre-processes the document by extracting key terms, analyzing their favorability, and generating recommendations in advance. This preliminary action provides the purchaser with essential information ahead of time, enabling informed decision-making without delaying the transaction process.
Solution Approach 2:
The patent implements a feedback mechanism that provides the purchaser with analyzed information about key terms and their favorability. The system outputs summaries highlighting important terms, potential unfavorable clauses, and specific recommendations. This feedback loop allows the purchaser to quickly understand critical aspects of the agreement and make better-informed decisions while maintaining transaction efficiency.
3Reliability
If the system analyzes all terms in detail, then the analysis is thorough, but the processing time and complexity increase
Solution Approach 1:
The patent extracts only the essential and relevant information from the contractual document rather than analyzing every single term in equal detail. The system identifies and extracts key terms that have significant impact on the purchaser, such as financial obligations, time constraints, and critical conditions. By focusing on extracting only the most important elements, the system maintains analysis thoroughness for critical terms while reducing overall processing complexity.
Solution Approach 2:
The patent segments the contractual document into distinct components and analyzes them with different levels of detail. The system divides the document into sections, identifies key terms within each section, and applies targeted analysis techniques. This segmentation allows the system to provide thorough analysis of critical terms while using more efficient processing for less important sections, thereby balancing analysis depth with system complexity.
Data Source
AI summary
A device receives image data of a contractual document that includes an offer including terms of a proposed transaction, converts the image data to text data that identifies text within the contractual document, and receives preferences information for a recipient of the offer. The device identifies key terms within the contractual document by using term identification to analyze the text. The key terms may include a first key term that identifies subject matter of the proposed transaction and other key terms that are part of the offer. The device determines term scores that correspond to likelihoods of the other key terms being favorable to the recipient by using a data model to analyze the key terms and the preferences information. The device, based on the term scores, generates and provides another device with a recommendation to be used in determining whether the accept the offer.