AI Evaluation Platform With Privacy-Preserving Persona Matching
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Solution Overview
Problem
Existing systems for evaluating individuals and providing career, health, and lifestyle goals rely heavily on human intervention, leading to biased and inaccurate advice, resulting in errors in benefit approvals and lack of personalized guidance, which negatively impacts individuals and resource efficiency.
Innovation Solution
A computing system that analyzes user data from multiple sources to create a digital representation without personal information, using machine learning models to suggest actions and pathways for improving quality of life, while ensuring privacy and security through blockchain and token technologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human intervention is used for evaluating individuals and providing career, health, and lifestyle goals, then personalized guidance can be provided, but bias and inaccuracy increase leading to errors in benefit approvals
Solution Approach 1:
The patent introduces an AI-based evaluation platform as an intermediary between users and benefit approval systems. This platform processes user data through machine learning models to generate objective evaluations, reducing human bias while maintaining personalized guidance capabilities. The system acts as a neutral mediator that combines automated analysis with user-specific information.
Solution Approach 2:
The patent replaces manual human evaluation processes with automated machine learning systems. The mechanical system of human reviewers is substituted with computational algorithms that consistently apply evaluation criteria, eliminating variability and bias inherent in human judgment while processing user data for personalized recommendations.
2Ease of operation
If human reviewers manually evaluate user data, then personalized advice can be provided, but time consumption and resource inefficiency increase
Solution Approach 1:
The patent enables users to input their own data and receive automated evaluations through the platform. The system serves itself by processing user-submitted information through pre-configured machine learning models, eliminating the need for manual reviewer intervention for each case while still providing personalized recommendations based on the accumulated data.
Solution Approach 2:
The patent transforms the evaluation process from manual parameter assessment to automated computational analysis. By changing the parameters from human cognitive evaluation to machine learning algorithm processing, the system maintains personalized advice quality while dramatically improving processing speed and resource efficiency through scalable automated operations.
3Measurement precision
If personal information is collected for creating user profiles, then personalized guidance accuracy improves, but privacy and security risks increase
Solution Approach 1:
The patent extracts and separates sensitive personal information from the core evaluation process. User data is processed in a way that removes personally identifiable information while retaining the characteristics needed for accurate personalized guidance. This extraction protects privacy by isolating sensitive data from the main evaluation system.
Solution Approach 2:
The patent introduces data anonymization and encryption mechanisms as intermediaries between personal information collection and profile creation. These intermediary layers process and protect user data, allowing accurate personalized guidance to be generated from personal information while systematically reducing privacy and security risks through controlled data handling procedures.
Data Source
AI summary
In one aspect, an example computer-implemented method includes (a) receiving user activity data, (b) receiving user agency data, (c) generating a user profile, wherein the user profile is based on the received user activity data and user agency data, and wherein the user profile contains personal information associated with the user, (d) issuing a digital representation, wherein the digital representation does not contain the personal information associated with the user, (e) receiving a plurality of predetermined persona profiles, (f) identifying a persona match using one or more machine learning models, (g) based on the identified persona match, identifying a suggested action for the user of the client computing device, and (g) transmitting instructions that cause the client computing device to display, via the user interface of the client computing device, a graphical indication of the suggested action to the user of the client computing device.