Adaptive Power Management for Wearable Devices

Publication ID: 24-11857337_0005_PTD
Published: October 28, 2025
Category:Future Evolutions & Paradigm Shifts

Legal Citation

pr1or.art Inc., “Adaptive Power Management for Wearable Devices,” Published Technical Disclosure No. 24-11857337_0005_PTD, Published October 28, 2025, available at https://archive.pr1or.art/24-11857337_0005_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

An advanced power management system for wearable devices, leveraging AI, machine learning, and kinetic energy harvesting to optimize power consumption, enhance user experience, and provide sustainable wearable technology.

Background and Problem Solved

The original patent, 'Power management for wearable devices', addressed the need for efficient power management in wearable devices. However, it relied on traditional processing devices and sensors, limiting its ability to adapt to changing user behavior and environmental conditions. The new inventive concept addresses this limitation by introducing advanced machine learning algorithms, neural networks, and kinetic energy harvesting to optimize power consumption and enhance user experience.

Detailed Description of the Inventive Concept

The new inventive concept comprises a wearable device with an integrated artificial intelligence module that learns and adapts to user behavior and environmental conditions. The AI module dynamically adjusts power consumption based on real-time physiological and environmental data, ensuring optimal performance and prolonging battery life. Additionally, the system utilizes advanced machine learning algorithms to optimize energy harvesting and reduce energy consumption, enabling sustainable and eco-friendly wearable technology. The wearable device features a self-sustaining power source that leverages kinetic energy harvesting and advanced nanotechnology to provide perpetual power and eliminate the need for battery recharging.

Novelty and Inventive Step

The new inventive concept's use of advanced machine learning algorithms, neural networks, and kinetic energy harvesting to optimize power consumption and enhance user experience is novel and non-obvious compared to the original patent. The integration of AI and machine learning enables the wearable device to adapt to changing user behavior and environmental conditions, providing a significant improvement over traditional power management systems.

Alternative Embodiments and Variations

Alternative embodiments of the inventive concept could include wearable devices with modular AI modules, enabling users to upgrade or replace the AI module as needed. Another variation could involve the use of different machine learning algorithms or neural network architectures to optimize power consumption and enhance user experience.

Potential Commercial Applications and Market

The new inventive concept has significant commercial potential in the wearable technology industry, particularly in the fields of fitness tracking, health monitoring, and smartwatches. The advanced power management system could also be applied to other battery-powered devices, such as smartphones and laptops, providing a sustainable and eco-friendly solution for the broader technology market.

Field of Art

Wearable device technology, specifically power management systems and sensor-based health monitoring devices, requiring expertise in electrical engineering, computer science, and biomedical sensor technologies

Person of Ordinary Skill (PHOSITA) Profile

A skilled practitioner with a bachelor's or master's degree in electrical engineering or computer science, familiar with sensor integration, machine learning techniques, and power optimization strategies for portable electronic devices

Obviousness Rationale

A person having ordinary skill in the art would recognize that integrating machine learning and adaptive power management techniques into wearable devices is a natural progression from existing sensor-based health monitoring systems. The source patent's framework of activity and physiological sensing provides a clear foundation for implementing more sophisticated power optimization strategies. The proposed variations represent predictable extensions of known techniques in sensor-based device management.

Obvious Combinations & Variations

Source Patent Element
Processing device configured to compare activity and physiological sensor data for predicting user events
PTD Variation
Neural network that dynamically adjusts power consumption based on real-time physiological and environmental data
Obviousness Reasoning
Implementing machine learning for predictive analysis is a known technique for enhancing sensor data interpretation, representing an obvious design improvement with predictable results
Source Patent Element
Multiple sensor types including accelerometer, GPS, and physiological sensors
PTD Variation
Kinetic energy harvesting system integrated with existing sensor array
Obviousness Reasoning
Incorporating energy harvesting techniques is a well-established approach for extending battery life in sensor-rich portable devices, representing a logical optimization strategy
Source Patent Element
Processing device configured to analyze user profile data for activity prediction
PTD Variation
AI module that learns and adapts to user behavior and environmental conditions
Obviousness Reasoning
Extending profile-based analysis to machine learning represents a straightforward technological progression using known algorithmic techniques
Source Patent Element
Wearable device with activity and physiological monitoring capabilities
PTD Variation
Self-sustaining power source utilizing advanced nanotechnology for perpetual power generation
Obviousness Reasoning
Developing alternative power generation methods is a predictable solution for addressing battery limitations in portable electronic devices
Source Patent Element
System for comparing and predicting user activities and health events
PTD Variation
Modular AI architecture enabling user-upgradeable machine learning capabilities
Obviousness Reasoning
Creating modular, upgradeable system architectures is a common design approach in consumer electronics, representing an obvious variation to a skilled practitioner
35 U.S.C. § 103 Summary: Based on the teachings of US Patent 11857337 and the disclosed technical variations, a person having ordinary skill in the art would find the proposed power management and machine learning techniques for wearable devices to be obvious extensions of existing sensor-based monitoring systems. The combination of known sensor technologies, predictive analysis methods, and power optimization strategies renders the claimed innovations prima facie obvious under 35 U.S.C. ยง 103.

Original Patent Information

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