20240079112.PHYSIOLOGICAL PREDICTIONS USING MACHINE LEARNING simplified abstract (apple inc.)
Contents
- 1 PHYSIOLOGICAL PREDICTIONS USING MACHINE LEARNING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 PHYSIOLOGICAL PREDICTIONS USING MACHINE LEARNING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
PHYSIOLOGICAL PREDICTIONS USING MACHINE LEARNING
Organization Name
Inventor(s)
Andrew Miller of Brooklyn NY (US)
Gregory Darnell of Palo Alto CA (US)
Guillermo R. Sapiro of Durham NC (US)
Seyedeh Sana Tonekaboni of North York (CA)
You Ren of Mercer Island WA (US)
Achille Nazaret of New York City NY (US)
PHYSIOLOGICAL PREDICTIONS USING MACHINE LEARNING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240079112 titled 'PHYSIOLOGICAL PREDICTIONS USING MACHINE LEARNING
Simplified Explanation
The subject technology provides a framework for generating physiological predictions for a user of an electronic device. The predictions may include user-specific predictions of a heartrate, a heartrate range, a number of steps, a number of calories, or other physiological conditions that may occur if the user engages in a future activity, such as a workout. The predictions are generated by a machine learning model that incorporates a physiological state equation and user-specific embedding, along with user-agnostic parameters of the future activity.
- Physiological predictions for users of electronic devices
- Generated by a machine learning model incorporating a physiological state equation
- Includes user-specific embedding and user-agnostic parameters of future activities
Potential Applications
This technology could be applied in:
- Fitness tracking devices
- Health monitoring systems
- Personalized workout programs
Problems Solved
- Lack of personalized physiological predictions
- Inaccurate fitness tracking data
- Difficulty in predicting future physiological conditions
Benefits
- Improved accuracy in predicting user-specific physiological data
- Enhanced user experience with electronic devices
- Personalized health and fitness recommendations
Potential Commercial Applications
Optimizing Fitness Predictions for Users
Possible Prior Art
No prior art known at this time.
Unanswered Questions
The article does not address the privacy implications of collecting and utilizing user-specific physiological data.
What are the potential limitations of the machine learning model in generating accurate physiological predictions?
The article does not discuss any potential limitations or challenges that the machine learning model may face in accurately predicting physiological data.
Original Abstract Submitted
the subject technology provides a framework for generating physiological predictions for a user of an electronic device. the physiological predictions may include user-specific predictions of a heartrate, a heartrate range, a number of steps, a number of calories, or other physiological conditions or aspects that may occur if the user engages in a future activity, such as a future workout. the physiological predictions may be generated by a machine learning model that incorporates a physiological state equation, and that generates, and utilizes, a user-specific embedding, along with user-agnostic parameters of the future activity, to make the predictions.
