Motivation DNA Profiling With LLMs for Intrinsic Motivation Analysis
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Solution Overview
Problem
Existing tools in psychology lack the ability to understand individual intrinsic motivations due to difficulties in devising user input systems and interpreting user inputs, particularly in the field of motivation science.
Innovation Solution
A cloud-based big data and AI platform using Large Language Models (LLMs) to analyze user descriptions and generate motivational quotient (MQ) profiles by identifying key motivational factors and assigning intensity levels, integrating with a Motivation DNA system to provide precise analysis of intrinsic motivations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing tools are used to collect and integrate external information, then information integration capability is improved, but understanding of individual intrinsic motivations deteriorates
Solution Approach 1:
The patent introduces a Motivation DNA system as an intermediary framework that bridges external information collection and intrinsic motivation understanding. This system uses a standardized set of motivational factors as intermediaries to translate diverse external data into meaningful insights about individual intrinsic motivations, resolving the contradiction between information integration and deep motivational understanding
Solution Approach 2:
The patent transforms the approach by changing from collecting raw external information to measuring specific motivational parameters (MQ factors). By using a structured parameter system that directly captures intrinsic motivation dimensions, the system achieves both information integration and precise motivational understanding simultaneously
2Measurement precision
If complex user input systems are designed to capture motivational data, then motivation analysis accuracy is improved, but system complexity and ease of operation deteriorate
Solution Approach 1:
The patent makes the Motivation DNA system universal by allowing it to accept multiple types of input (self-descriptions, observer descriptions, performance data) and automatically interpret them through the same framework. This multi-functionality maintains high analysis accuracy while simplifying user interaction, as users can provide information in their natural way without navigating complex input protocols
Solution Approach 2:
The system enables users to describe themselves in their own words using simple natural language, and the automated system performs the complex analysis through the Motivation DNA framework. This self-service approach eliminates the need for users to engage with complex input systems while maintaining accurate motivational analysis
Data Source
AI summary
A method for a web platform trains a large language model platform (LLM) to select a special LLM and also generates a user profile based on a simple user input through this selected special LLM.