18243338. Smart Context Subsampling On-Device System simplified abstract (GOOGLE LLC)

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Smart Context Subsampling On-Device System

Organization Name

GOOGLE LLC

Inventor(s)

Alexander Varshavsky of Summit NJ (US)

Bhaskar Mehta of Menlo Park CA (US)

Brian Coopersmith of Millburn NJ (US)

Giovanni Botta of Brooklyn NY (US)

Smart Context Subsampling On-Device System - A simplified explanation of the abstract

This abstract first appeared for US patent application 18243338 titled 'Smart Context Subsampling On-Device System

Simplified Explanation

The present disclosure is about a system that intelligently samples information on a device, such as location and activities, using machine learning to optimize battery usage and maintain or improve the quality of reported metrics.

  • The system intelligently samples information on a device, such as location and activities.
  • Machine learning is used to optimize the sampling and uploading of background context.
  • The optimization reduces battery usage while maintaining or improving the quality of reported metrics.
  • A policy is generated based on the machine learning, dictating how scanning and upload rates should change in response to device conditions.

Potential Applications

This technology can be applied in various fields where intelligent sampling of information is required, such as:

  • Location-based services: Enhancing the accuracy and efficiency of location tracking for navigation, delivery services, and geolocation-based applications.
  • Activity tracking: Improving the monitoring and analysis of user activities for fitness tracking, healthcare, and productivity applications.
  • Data analytics: Providing high-quality data for analysis and insights in fields like market research, urban planning, and transportation optimization.

Problems Solved

This technology addresses several problems related to sampling information on a device:

  • Battery drain: By optimizing the sampling and uploading process, the system reduces battery usage, extending device battery life.
  • Data quality: The machine learning-based approach ensures that the reported metrics maintain or improve their quality, providing more accurate and reliable information.
  • Resource efficiency: The system intelligently adjusts scanning and upload rates based on device conditions, optimizing resource utilization and minimizing unnecessary data collection.

Benefits

The benefits of this technology include:

  • Extended battery life: By reducing battery usage, users can enjoy longer device usage without the need for frequent charging.
  • Improved data accuracy: The machine learning optimization enhances the quality of reported metrics, leading to more accurate and reliable information.
  • Efficient resource utilization: The system intelligently adjusts sampling rates, optimizing resource usage and reducing unnecessary data collection, resulting in improved device performance.


Original Abstract Submitted

The present disclosure provides a system for intelligently sampling information, such as location, activities, etc. on device. Sampling and uploading of background context is optimized using machine learning, such that battery usage is reduced, and quality of metrics based on the reported information is maintained or improved. A policy is generated based on the machine learning, the policy dictating how scanning and upload rates should change in response to conditions on the device.

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