Emotional Pattern Matching With User State Classifiers
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Solution Overview
Problem
The complexity and multiplicity of data in automated analysis systems lead to inaccuracies and inefficiencies in transmitting relevant emotional data to users, exacerbating user frustration and wasting time in correcting issues.
Innovation Solution
A system and method for emotional pattern matching that utilizes a user state classifier to analyze current emotional activity data, match it to emotional therapy data, and generate a personalized emotional wellbeing model using machine-learning, enabling accurate emotional therapy transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If automated analysis systems process complex and multiple data, then data coverage is improved, but accuracy and transmission efficiency deteriorate
Solution Approach 1:
The system segments emotional data analysis into distinct components: emotion detection module, pattern recognition module, and therapy matching module. Each module processes specific aspects of emotional data independently, improving overall accuracy while maintaining comprehensive coverage. The segmentation allows the system to handle complex data without sacrificing precision by breaking down the analysis into manageable, specialized components.
2Quantity of substance
If automated analysis systems process complex and multiple data, then data coverage is improved, but transmission efficiency deteriorates
Solution Approach 1:
The system extracts only the most relevant emotional patterns and features from the complex data set for transmission and processing. Instead of transmitting all raw emotional data, the pattern recognition module identifies and extracts key emotional indicators, reducing data transmission volume while maintaining analysis effectiveness. This extraction principle improves transmission efficiency without compromising comprehensive emotional data coverage.
3Adaptability or versatility
If personalized emotional therapy is provided, then user satisfaction is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by pre-processing emotional data and pre-identifying patterns during data collection phases. The pattern recognition module pre-processes emotional indicators before therapy matching occurs, reducing the computational complexity required during real-time personalized therapy delivery. This preliminary processing enables personalization while managing system complexity by preparing data in advance.
Data Source
AI summary
A system for emotional pattern matching is disclosed. The system includes at least a computing device, the computing device receives current user emotional activity data from a user. The computing device generates a user state classifier as a function of state training data, where the state training data comprises a plurality of elements of past user state emotional data. The computer device identifies a current user emotional state as a function of the user state classifier and the current user emotional state data, where the current user emotional state is a function of the emotional wellbeing of the user. The computing device matches the current user emotional state to an emotional therapy. The computing device transmits the emotional therapy to a user. The method for emotional pattern matching is also disclosed.