Multi-Channel Decision Engine for Emotional State Detection
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Solution Overview
Problem
Conventional technologies poorly evaluate emotional and behavioral states of individuals, leading to inappropriate actions and worsening of states, as they often rely on inaccurate self-reporting or single-channel data inputs.
Innovation Solution
A system and method utilizing multi-channel data inputs, including image, voice, geolocation, biometric, and transactional data, processed by sentiment analysis and behavior analysis machine learning models to determine responsive actions based on user-configured criteria, ensuring accurate emotional and behavioral state evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If self-report emoji or mood is used to detect emotional state, then the implementation is simple, but the accuracy of emotional state detection deteriorates
Solution Approach 1:
The patent combines multiple data input channels (biometric data, behavioral data, contextual data) to evaluate emotional and behavioral states. Instead of relying on a single self-report method, the system merges data from multiple sources including wearables, mobile devices, and environmental sensors to achieve more accurate and reliable state detection.
Solution Approach 2:
The patent introduces machine learning models as intermediaries that process raw multi-channel data and translate it into accurate emotional and behavioral state evaluations. These models act as mediators between the complex multi-source data and the final state determination, improving accuracy while maintaining ease of use.
2Device complexity
If conventional single-channel data input is used, then the system complexity is low, but the reliability of decision-making deteriorates
Solution Approach 1:
The system merges multiple data channels including biometric data from wearables, behavioral data from mobile devices, and contextual data from environmental sensors. This multi-channel approach significantly improves the reliability of emotional and behavioral state evaluation while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent creates a universal evaluation system that can process multiple types of data (biometric, behavioral, contextual) through a common machine learning framework. This multi-functional approach allows the system to reliably evaluate various emotional and behavioral states using a single integrated platform.
3Productivity
If inaccurate emotional state evaluation is performed, then the processing speed is fast, but the appropriateness of responsive actions deteriorates
Solution Approach 1:
The system performs preliminary evaluation of emotional and behavioral states using pre-trained machine learning models that have already learned from extensive data. This preliminary processing enables fast, accurate evaluation without requiring complex real-time analysis, thus maintaining both speed and accuracy in determining appropriate responsive actions.
Data Source
AI summary
Systems and methods for decision-making with multi-channel data inputs are provided. The system includes a plurality of devices, and a server in data communication with the plurality of devices. The server includes a decision engine, a sentiment analysis machine learning model, and a behavior analysis machine learning model. The server is configured to: receive the at least one data input from each of the plurality of device; perform, using the sentiment analysis machine learning model, a sentiment analysis on the at least one data input to generate sentiment information indicative of an emotional state of a user; perform, using the behavior analysis machine learning model, a behavior analysis on the at least one data input to generate behavior information indicative of a behavioral state of the user; determine, using the decision engine, a responsive action based on the sentiment information and the behavior information; and perform the responsive action.