Sleep Number Corporation (20240268763). BED SYSTEM WITH EDGE-BASED AND REMOTE-COMPUTING BASED MODEL DEPLOYMENT FOR DETERMINING USER HEALTH METRICS simplified abstract

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BED SYSTEM WITH EDGE-BASED AND REMOTE-COMPUTING BASED MODEL DEPLOYMENT FOR DETERMINING USER HEALTH METRICS

Organization Name

Sleep Number Corporation

Inventor(s)

Faisal Mushtaq of San Diego CA (US)

Sajeev Mayandi of Minneapolis MN (US)

BED SYSTEM WITH EDGE-BASED AND REMOTE-COMPUTING BASED MODEL DEPLOYMENT FOR DETERMINING USER HEALTH METRICS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240268763 titled 'BED SYSTEM WITH EDGE-BASED AND REMOTE-COMPUTING BASED MODEL DEPLOYMENT FOR DETERMINING USER HEALTH METRICS

Simplified Explanation: The patent application describes techniques for generating sleeper information based on user data collected at a bed system. This involves using edge computing devices, service-providing servers, and a cloud-based computing system to analyze sensor signals from bed sensors and generate health metrics for the user.

  • The system includes edge computing devices, servers, and a cloud-based computing system.
  • Models are executed on servers to determine sleeper information based on sensor signals.
  • Servers in the cloud-based system and at the edge computing device work together to process the data.
  • The cloud-based system receives sensor signals, executes models, and generates health metrics for the user.

Key Features and Innovation:

  • Use of edge computing devices, servers, and a cloud-based system for analyzing sleeper information.
  • Collaboration between servers in the cloud-based system and at the edge computing device.
  • Generation of health metrics based on user data collected from bed sensors.

Potential Applications: The technology can be applied in the healthcare industry for monitoring patient sleep patterns and health metrics. It can also be used in smart home devices to provide personalized sleep recommendations.

Problems Solved: The technology addresses the need for efficient analysis of sleeper information and health metrics based on data collected from bed sensors. It streamlines the process of generating personalized insights for users.

Benefits:

  • Improved monitoring of sleep patterns and health metrics.
  • Personalized recommendations for better sleep quality.
  • Enhanced user experience with smart bed systems.

Commercial Applications: Title: Advanced Sleep Monitoring Technology for Healthcare and Smart Home Devices This technology can be commercialized in the healthcare industry for patient monitoring systems and in the consumer market for smart home devices. It offers a unique solution for analyzing sleeper information and generating health metrics.

Prior Art: Readers can explore prior research on sleep monitoring systems, edge computing in healthcare, and cloud-based data analysis for personalized recommendations.

Frequently Updated Research: Researchers are continually exploring advancements in sleep monitoring technology, edge computing applications in healthcare, and cloud-based data analysis for personalized insights.

Questions about Sleep Monitoring Technology: 1. How does this technology improve the accuracy of health metrics compared to traditional methods? 2. What are the potential privacy concerns associated with collecting and analyzing user data from bed sensors?


Original Abstract Submitted

disclosed are techniques for generating sleeper information based on user data collected at a bed system. a system can include an edge computing device, service-providing servers, and a cloud-based computing system. the servers can execute models for determining particular sleeper information based on sensor signals generated by bed sensors. a first subset of servers run in a cloud-based system and a second subset run at the edge computing device. the cloud-based computing system can receive the sensor signals, receive a request to execute a model having a relationship with a server, wrap the model with model data, transmit the wrapped model and data to the server for execution, receive model output once the wrapped model is executed, and generate at least one health metric about a user of the bed system based on correlating the model output with other model outputs or other data about the user.

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