Nec corporation (20240185124). LEARNING DEVICE, STRESS ESTIMATION DEVICE, LEARNING METHOD, STRESS ESTIMATION METHOD, AND STORAGE MEDIUM simplified abstract
Contents
- 1 LEARNING DEVICE, STRESS ESTIMATION DEVICE, LEARNING METHOD, STRESS ESTIMATION METHOD, AND STORAGE MEDIUM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 LEARNING DEVICE, STRESS ESTIMATION DEVICE, LEARNING METHOD, STRESS ESTIMATION METHOD, AND STORAGE MEDIUM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Stress Estimation Technology
- 1.13 Original Abstract Submitted
LEARNING DEVICE, STRESS ESTIMATION DEVICE, LEARNING METHOD, STRESS ESTIMATION METHOD, AND STORAGE MEDIUM
Organization Name
Inventor(s)
Masanori Tsujikawa of Tokyo (JP)
Yoshiki Nakashima of Tokyo (JP)
LEARNING DEVICE, STRESS ESTIMATION DEVICE, LEARNING METHOD, STRESS ESTIMATION METHOD, AND STORAGE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240185124 titled 'LEARNING DEVICE, STRESS ESTIMATION DEVICE, LEARNING METHOD, STRESS ESTIMATION METHOD, AND STORAGE MEDIUM
Simplified Explanation
The patent application describes an information processing device that sorts and selects feature values for stress estimation based on observation data of a target person.
Key Features and Innovation
- First sorting means sorts observation feature values based on the target person's attributes and environment.
- Second sorting means sorts observed feature values based on the observation target and activity state of the target person.
- Feature value selection means selects stress estimation feature values from the sorted observed feature values.
- Learning means trains a stress estimation model using the selected feature values and correct stress values for each cluster of observed feature values.
Potential Applications
This technology can be applied in various fields such as healthcare, psychology, and human resource management for stress estimation and management.
Problems Solved
This technology addresses the need for accurate stress estimation by sorting and selecting relevant feature values from observation data.
Benefits
The benefits of this technology include improved stress management, personalized interventions, and better understanding of individual stress levels.
Commercial Applications
- Title: "Advanced Stress Estimation Technology for Personalized Interventions"
- This technology can be commercialized in healthcare settings, wellness programs, and employee assistance programs.
- It can also be integrated into wearable devices and mobile applications for real-time stress monitoring.
Prior Art
There is no specific information provided about prior art related to this technology.
Frequently Updated Research
There is ongoing research in the field of stress estimation using machine learning and data analysis techniques to enhance the accuracy and efficiency of stress prediction models.
Questions about Stress Estimation Technology
Question 1
How does the information processing device differentiate between various stress levels based on the observed feature values?
The information processing device uses machine learning algorithms to train a stress estimation model that can distinguish between different stress levels by analyzing the selected feature values and corresponding correct stress values.
Question 2
What are the potential implications of integrating this technology into wearable devices for stress monitoring?
Integrating this technology into wearable devices can provide users with real-time feedback on their stress levels, enabling them to take proactive measures to manage stress effectively.
Original Abstract Submitted
an information processing device x mainly includes first and sorting means x and x, a feature value selection means x, and a learning means x. the first sorting means x performs a first sorting for sorting observation feature values of a target person based on attribute and/or environment of the target person. the second sorting means x performs a second sorting for sorting the observed feature values based on an observation target of the observed feature values and/or an activity state of the target person. the feature value selection means x selects stress estimation feature values for stress estimation from the observed feature values sorted based on the first sorting and the second sorting. the learning means x trains a stress estimation model based on the stress estimation feature values and corresponding correct stress values for each cluster of the observed feature values sorted by the first sorting.
