Personal Data Query Platform for Secure Cross-Source Insights
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Individuals face challenges in managing and deriving insights from their diverse and distributed personal data, particularly financial data, due to its sensitivity and the difficulty in accessing and sharing it across different organizations.
Innovation Solution
A learning and personal data management platform that integrates various types of personal data, allows users to derive insights, and facilitates sharing with others to achieve common goals, using a large language model to generate recommendations and rewards for achieving financial goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If personal data is stored in multiple different sources across various organizations, then data security and reliability are improved, but data accessibility and ease of management deteriorate
Solution Approach 1:
The patent implements a centralized data management platform that acts as an intermediary between users and multiple data sources. This platform aggregates personal data from various organizations while maintaining security protocols, allowing users to access and manage their distributed data through a single interface without compromising the security measures employed by individual data holders.
Solution Approach 2:
The system creates a universal data management interface that can handle multiple types of personal data (financial, health, personal information) from different sources through standardized protocols. This multi-functional approach allows users to manage diverse data types and access patterns through a single system, improving accessibility without sacrificing the specialized security requirements of each data type.
2Reliability
If personal data is distributed across multiple organizations, then data security is improved, but the ability to derive insights and recommendations deteriorates
Solution Approach 1:
The patent merges data from multiple distributed sources into a unified view while maintaining the security boundaries of each source. The system combines financial data, health data, and personal information into an integrated profile that enables comprehensive analytics and insights, allowing users to derive recommendations that span across different data types without requiring data to physically leave secure environments.
Solution Approach 2:
The centralized platform serves as an intermediary that performs analytics and insight generation on aggregated data while maintaining security protocols. The system can derive recommendations and patterns across distributed data sources without compromising the security measures of individual organizations, by processing data through secure channels and returning only necessary insights to users.
3Reliability
If stringent cybersecurity measures are employed for sensitive personal data, then data security is improved, but ease of sharing and coordination deteriorates
Solution Approach 1:
The system implements self-service capabilities that allow users to control their own data sharing preferences and permissions through the centralized interface. Users can selectively authorize access to specific data types and share them with designated parties or applications, eliminating the need for complex security coordination between multiple organizations while maintaining stringent security controls through automated permission management.
Data Source
AI summary
Methods, systems, and techniques for processing natural language queries. A natural language query related to personal information of the user is obtained. Query vectors that include embeddings of the query are generated and matched with document vectors generated from chunks of reference documents related to the personal data. The document chunks are retrieved from a database and used as context for a prompt to a large language model that is used to respond to the natural language query. The query itself is also included in the prompt. The query may be in respect of a particular goal, and the large language model's response may include recommendations to help the user achieve that goal.