Advanced Power Management for Wearable Devices

Publication ID: 24-11857337_0001_PTD
Published: October 28, 2025
Category:Direct Improvements & Enhancements

Legal Citation

pr1or.art Inc., “Advanced Power Management for Wearable Devices,” Published Technical Disclosure No. 24-11857337_0001_PTD, Published October 28, 2025, available at https://archive.pr1or.art/24-11857337_0001_PTD
This technical disclosure describes improvements that would be readily apparent to a Person Having Ordinary Skill In The Art (PHOSITA) when considered in combination with the foundational architecture disclosed in U.S. Patent No. 11,857,337.

Summary of the Inventive Concept

A novel power management system for wearable devices that optimizes power consumption based on user activity levels, extends battery life, and predicts power drain to ensure seamless user experience.

Background and Problem Solved

The original patent, 'Power management for wearable devices,' addressed the need for efficient power management in wearable devices. However, it had limitations in terms of power consumption, battery life, and user experience. The new inventive concept builds upon the original patent by providing advanced power management features that overcome these limitations.

Detailed Description of the Inventive Concept

The new inventive concept comprises a low-power mode that reduces power consumption by at least 50% when the user is in a resting state. It also includes a method for optimizing power usage by monitoring user activity levels and adjusting power consumption accordingly. Additionally, the system dynamically switches between different power modes based on the user's activity levels, ensuring optimal power usage. The wearable device features a rechargeable battery and a power management system that extends the battery life by at least 20% by optimizing power consumption during periods of low user activity. Furthermore, the system predicts power drain by monitoring user behavior and activity patterns, generating alerts to the user when the power drain is predicted to exceed a predetermined threshold. A power management module with a machine learning algorithm learns the user's behavior and activity patterns to optimize power consumption and extend battery life.

Novelty and Inventive Step

The new inventive concept's novelty lies in its ability to optimize power consumption based on user activity levels, predict power drain, and extend battery life. The inventive step is the integration of these features into a single system that provides a seamless user experience.

Alternative Embodiments and Variations

Alternative embodiments of the inventive concept could include using different machine learning algorithms, integrating with other wearable devices, or incorporating additional sensors to monitor user activity. Variations could include implementing the system in different wearable devices, such as smartwatches or fitness trackers.

Potential Commercial Applications and Market

The advanced power management system has significant commercial potential in the wearable devices market, particularly in the fitness and health industries. It could be integrated into various wearable devices, providing users with a seamless and efficient experience. The market for wearable devices is expected to continue growing, and the new inventive concept is well-positioned to capitalize on this trend.

Field of Art

Wearable device technology, specifically power management systems for fitness trackers and smartwatches, requiring expertise in electrical engineering, sensor integration, and machine learning algorithms for activity tracking

Person of Ordinary Skill (PHOSITA) Profile

A person with a bachelor's or master's degree in electrical engineering or computer science, with 3-5 years of experience in wearable device design, familiar with power optimization techniques, sensor integration, and basic machine learning principles

Obviousness Rationale

A person skilled in the art would recognize that the power management techniques disclosed in the PTD are logical extensions of the source patent's activity tracking and sensor monitoring system. The source patent already describes monitoring user activity and physiological parameters, making the power optimization based on activity levels a predictable and obvious improvement. The integration of machine learning for predicting power consumption follows directly from the source patent's data analysis and prediction capabilities.

Obvious Combinations & Variations

Source Patent Element
Activity sensor configured to measure user activity levels
PTD Variation
Low-power mode that reduces power consumption by 50% during resting states
Obviousness Reasoning
A PHOSITA would recognize that reducing power consumption during low activity periods is a standard optimization technique in battery-powered devices, representing a predictable design choice
Source Patent Element
Processing device configured to analyze user profile data and predict events
PTD Variation
Machine learning algorithm that learns user behavior patterns to optimize power consumption
Obviousness Reasoning
Extending predictive analytics to power management is an obvious application of existing machine learning and data analysis techniques in wearable technology
Source Patent Element
Physiological and activity sensors for monitoring user parameters
PTD Variation
Dynamic power mode switching based on real-time activity level monitoring
Obviousness Reasoning
Adapting power consumption based on sensor inputs is a known technique for improving battery efficiency in portable electronic devices
Source Patent Element
System for comparing and predicting user events based on sensor data
PTD Variation
Power drain prediction system generating user alerts when consumption exceeds threshold
Obviousness Reasoning
Providing user notifications about device performance is a standard feature in modern electronic devices, representing an obvious design improvement
35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11857337 and the disclosed power management techniques, a person having ordinary skill in the art would find the claimed variations obvious. The power optimization methods represent predictable extensions of existing activity tracking and sensor monitoring technologies, combining known techniques in a manner that would be apparent to a skilled practitioner in wearable device design.

Original Patent Information

Patent NumberUS 11,857,337
TitlePower management for wearable devices
Assignee(s)Tula Health Inc