Biometric Advisor Matching via Machine Learning
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Solution Overview
Problem
Locating an informed advisor who can effectively address user issues is challenging due to the need for multiple visits and is further complicated by time constraints and the effort required to find a suitable advisor.
Innovation Solution
A system and method using a computing device to identify user features through biological extraction, generate a machine-learning model from user and advisor data, and determine compatibility between users and advisors using a machine-learning algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a user manually searches for and evaluates multiple advisors through multiple visits, then the user can find a suitable informed advisor, but the process requires significant time and effort
Solution Approach 1:
The system performs preliminary analysis of user features through biological extraction and creates a machine-learning model before the user needs to meet advisors. The compatibility assessment is pre-computed using biometric data, eliminating the need for multiple trial visits to evaluate advisor suitability.
Solution Approach 2:
The machine-learning model acts as an intermediary between the user and advisors. It processes biometric data and advisor information to compute compatibility scores, serving as a mediator that eliminates the need for direct human evaluation through multiple visits.
2Reliability
If a user manually evaluates multiple advisors through multiple visits, then the user can assess advisor compatibility, but the process requires significant effort
Solution Approach 1:
The system performs the compatibility assessment automatically without requiring user effort. The machine-learning model self-evaluates advisor compatibility by processing biometric data and advisor information, eliminating the need for the user to manually assess multiple advisors.
Solution Approach 2:
The machine-learning model serves as an intermediary that automates the compatibility assessment process. It handles the complex evaluation of advisor suitability based on biometric data, freeing the user from the effort-intensive manual assessment process.
3Productivity
If the system uses biometric data and machine-learning models to assess advisor compatibility, then the matching process becomes efficient and automated, but the system complexity increases
Solution Approach 1:
The system extracts only the essential biometric features relevant to advisor compatibility from complex biometric data. The machine-learning model is trained to identify and process only the critical features needed for matching, reducing the complexity of data processing while maintaining matching efficiency.
Solution Approach 2:
The system transforms complex biometric data into simplified compatibility parameters through the machine-learning model. By changing the parameter representation from raw biometric data to compatibility scores, the system achieves efficient matching while managing complexity through dimensionality reduction.
Data Source
AI summary
A system for customizing informed advisor pairings, the system including a computing device. The computing device is configured to identify a user feature wherein the user feature contains a user biological extraction. The computing device is configured to generate using element training data and using a first machine-learning algorithm a first machine-learning model that outputs advisor elements. The computing device receives an informed advisor element relating to an informed advisor. The computing device determines using output advisor elements whether an informed advisor is compatible for a user.