17933712. OPTIMIZING BATTERY CHARGING WITH SYNCHRONIZED CONTEXT DATA simplified abstract (Apple Inc.)
Contents
- 1 OPTIMIZING BATTERY CHARGING WITH SYNCHRONIZED CONTEXT DATA
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 OPTIMIZING BATTERY CHARGING WITH SYNCHRONIZED CONTEXT DATA - 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 Original Abstract Submitted
OPTIMIZING BATTERY CHARGING WITH SYNCHRONIZED CONTEXT DATA
Organization Name
Inventor(s)
Gina B. Lu of San Francisco CA (US)
Kartik R. Venkatraman of San Francisco CA (US)
Aaron Cotter of San Francisco CA (US)
Alexander D. Palmer of San Jose CA (US)
OPTIMIZING BATTERY CHARGING WITH SYNCHRONIZED CONTEXT DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 17933712 titled 'OPTIMIZING BATTERY CHARGING WITH SYNCHRONIZED CONTEXT DATA
Simplified Explanation
The abstract describes a patent application for an electronic device that can receive synchronized context data from other devices associated with a user, determine battery charging intervals based on this data, and charge the battery during these intervals.
- The electronic device includes a power system with a battery and a processor.
- The processor uses synchronized context data to determine battery charging intervals.
- Machine learning models can be used to determine these intervals.
- The synchronized context data can indicate the user's location.
- If the user is away from the device, the intervals are based on the expected return time.
Potential Applications
This technology could be applied in smart devices, wearables, and IoT devices to optimize battery charging based on user behavior and location.
Problems Solved
This technology solves the problem of inefficient battery charging by adapting the charging intervals to the user's activities and location.
Benefits
The benefits of this technology include improved battery life, convenience for users, and reduced energy consumption.
Potential Commercial Applications
A potential commercial application for this technology could be in smart home devices, where battery optimization is crucial for seamless operation.
Possible Prior Art
One possible prior art for this technology could be smartwatches that use location data to adjust settings, including battery management.
Unanswered Questions
How does this technology impact user privacy?
This technology relies on synchronized context data, which may raise concerns about user privacy and data security. Implementing robust privacy measures and obtaining user consent are essential to address these concerns.
What are the potential limitations of using machine learning models for determining battery charging intervals?
While machine learning models can provide accurate predictions, they may require significant computational resources and data processing. Additionally, the effectiveness of these models may depend on the quality and quantity of training data available.
Original Abstract Submitted
An electronic device can include a power system including a battery and a processor programmed to: receive synchronized context data from one or more other devices associated with a user of the device, determine, at least in part based on the synchronized context data, one or more battery charging intervals, and operate the power system to charge the battery from the external power source during the identified one or more battery charging intervals. The processor can be programmed to determine the one or more battery charging intervals using a machine learning model. The synchronized context data can provide indication of the user's location. If the synchronized context data indicates that the user is at a different location than the device, the one or more battery charging intervals determined based at least in part on an expected time for the user to return to the location of the device.
