Intelligent Contract Analysis System for Data Privacy Control
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Solution Overview
Problem
Consumers lack control and transparency over their personal data, making it difficult for them to understand and agree to data usage terms, especially in the healthcare industry where personal identifiable information is often used for research without proper compensation to the individuals.
Innovation Solution
The development of systems and methods for intelligent contract analysis and organization, which utilize natural-language processing (NLP) algorithms to summarize contracts, and machine-learning models to provide users with recommendations on accepting or rejecting contract terms based on historical patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If consumers are provided with full contract text for review, then they can understand data usage terms, but the time required to review and comprehend contracts becomes excessively long
Solution Approach 1:
The system extracts and highlights only the most relevant data usage provisions from lengthy contracts, separating critical information from boilerplate text. This allows consumers to quickly understand key data usage terms without reading entire contracts, resolving the contradiction between comprehensive understanding and time efficiency.
Solution Approach 2:
The contract analysis system segments contracts into distinct sections based on data usage relevance, organizing provisions by topic and priority. This segmentation enables consumers to focus on specific data-related clauses rather than reviewing contracts as undifferentiated blocks of text, reducing review time while maintaining understanding.
2Reliability
If businesses request consent from customers for each individual third-party data sharing request, then data privacy control is maintained, but the consent process becomes unmanageably complex and cumbersome
Solution Approach 1:
The system merges multiple individual consent requests into consolidated data usage agreements grouped by third-party category or data type. This consolidation maintains granular privacy control while reducing the number of separate consent actions customers must take, making the process manageable without sacrificing privacy protection.
Solution Approach 2:
The consent management system creates universal consent frameworks that cover multiple data sharing scenarios under single agreed-upon terms. This allows businesses to share data with multiple third parties within defined categories based on one customer authorization, reducing repetitive consent requests while maintaining privacy control.
3Loss of information
If detailed contract provisions are presented to users, then transparency of data usage is improved, but user comprehension and ability to make informed decisions deteriorates due to complexity
Solution Approach 1:
The system applies different levels of detail and presentation quality to different contract sections based on their importance and complexity. Critical data usage provisions receive simplified, prominent presentation with clear explanations, while less important boilerplate text receives standard presentation. This local differentiation maintains transparency for key issues while improving overall comprehensibility.
Solution Approach 2:
The system introduces an intermediary layer of plain-language summaries and explanations between the original legal contract text and the user. This intermediary translation layer preserves the transparency of detailed provisions while making them comprehensible to non-legal experts, enabling informed decision-making without being overwhelmed by complexity.
Data Source
AI summary
Examples of the present disclosure describe systems and methods for intelligent contract analysis and data organization. In example aspects, input data may be received into the system in the form of a legal document, such as a contract. The contract may be processed by a natural-language processor, and a summarized version of the contract and/or particular contract provisions may be provided. In other aspects, a machine-learning engine may process the input data in combination with a user's decisions to accept or reject particular contract provisions. The ML engine may provide an intelligent recommendation to a user regarding whether the user should consent or not consent to a particular contract provision. In other example aspects, a data dashboard displaying how a user's personally identifiable information (PII) is shared with third parties may be displayed. Aggregated PII may further be displayed in a dashboard.