Efficiency Inference Using Multi-Source Biological Data
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Solution Overview
Problem
Existing methods for inferring learning or work efficiency based on line of sight data are insufficient, as they cannot accurately determine if a subject is in a high productivity concentration state, and only provide a state evaluation rather than a productivity indicator.
Innovation Solution
An efficiency inference apparatus that includes a biological information acquisition unit and an inference unit, using a trained model to infer productivity efficiency from a range of biological information such as line of sight, brain waves, heartbeat, body temperature, and facial expressions, which can indicate learning or work efficiency based on correct answers, time taken, or work accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If only line of sight data is used for inference, then the device complexity is reduced, but the measurement precision of efficiency evaluation deteriorates
Solution Approach 1:
The patent combines multiple biological information acquisition devices (line of sight camera, brain wave sensor, heartbeat sensor, body temperature sensor, body movement sensor, facial expression camera, face orientation camera) into an integrated efficiency inference system. This merging of multiple data sources resolves the contradiction by maintaining low device complexity through integration while significantly improving measurement precision through multi-parameter analysis.
Solution Approach 2:
The patent uses a composite approach by combining multiple types of biological information (line of sight position, brain wave signals, heartbeat data, body temperature, body movement, facial expressions, face orientation) to create a comprehensive efficiency evaluation model. This composite information approach resolves the contradiction by achieving high measurement precision through diverse data sources while managing device complexity through unified processing.
2Loss of information
If only line of sight data is used for inference, then the information processing load is reduced, but the reliability of efficiency inference deteriorates
Solution Approach 1:
The patent merges multiple biological information sources into a unified inference model that processes line of sight data, brain wave signals, heartbeat data, body temperature, body movement, facial expressions, and face orientation simultaneously. This integration resolves the contradiction by distributing information processing across multiple standardized inputs while improving reliability through cross-validation of multiple physiological indicators.
Solution Approach 2:
The patent introduces a trained model as an intermediary that processes and integrates multiple biological information types. This intermediary component resolves the contradiction by managing the complexity of multi-source data processing while extracting reliable efficiency indicators, reducing the overall information processing load through intelligent data synthesis.
3Measurement precision
If multiple biological information types are acquired, then the measurement precision of efficiency evaluation is improved, but the device complexity increases
Solution Approach 1:
The patent implements a universal efficiency inference apparatus that handles multiple biological information types (line of sight, brain waves, heartbeat, body temperature, body movement, facial expressions, face orientation) through a single integrated system. This multi-functional design resolves the contradiction by achieving high measurement precision through diverse data collection while maintaining manageable device complexity through unified processing architecture.
Solution Approach 2:
The patent combines multiple specialized sensors and cameras into an integrated efficiency inference system with a single trained model that processes all inputs. This merging approach resolves the contradiction by improving measurement precision through comprehensive data collection while reducing device complexity through centralized processing and standardized data handling protocols.
Data Source
AI summary
An efficiency inference apparatus infers an efficiency of a subject. The efficiency inference apparatus includes a biological information acquisition unit configured to acquire biological information of the subject, and an inference unit configured to infer the efficiency of the subject, based on the biological information. The inference unit may include a trained model to infer the efficiency of the subject from the biological information. The trained model may be trained with a training dataset including the biological information and the efficiency of the subject.