Dynamic Transaction Card Power Optimization
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Solution Overview
Problem
Dynamic transaction cards face challenges in optimizing operational configurations and user experience while extending energy storage life, as various functions such as lighting and sensor configurations deplete power components and negatively impact user behavior, and existing systems fail to effectively detect device defects.
Innovation Solution
The implementation of a system that uses data inputs from dynamic transaction cards, including sensor inputs, connection data, and transaction data, to determine optimal configurations and detect defects, utilizing regression algorithms and machine learning to optimize operational settings and extend energy storage life, and to promote desired user behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If various operational configurations and functions (lighting, display, sensor) are enabled to improve user experience, then user experience is enhanced, but power components are depleted faster
Solution Approach 1:
The patent implements dynamic configuration adjustment where the transaction card automatically modifies its operational settings (lighting intensity, display refresh rate, sensor polling frequency) based on real-time usage patterns and power status. This allows the system to provide optimal user experience while adapting power consumption to available energy levels, resolving the contradiction between user experience and power depletion.
Solution Approach 2:
The system changes operational parameters (brightness levels, timeout durations, activation thresholds) based on learned user behavior patterns and power component status. By dynamically adjusting these parameters, the card maintains acceptable user experience while preventing excessive power depletion, directly addressing the technical contradiction.
2Reliability
If real-time data monitoring and analysis are implemented to optimize configurations and detect defects, then system reliability is improved, but computational resources and energy are consumed
Solution Approach 1:
The patent implements selective data monitoring where only critical parameters and anomaly indicators are analyzed in real-time, while less important data is sampled at lower frequencies or analyzed in batches. This partial action approach maintains defect detection capability while significantly reducing computational energy consumption compared to comprehensive real-time analysis of all data streams.
Solution Approach 2:
The system uses machine learning models trained offline to process and filter data on the card, with only essential analysis performed in real-time. The intermediary models pre-process data patterns, allowing the card to detect defects with minimal computational overhead during operation, thus maintaining reliability while reducing energy consumption.
Data Source
AI summary
The present disclosure relates to devices and methods relating to an optimized electronic transaction card where various data inputs associated with a dynamic transaction card optimize operational configurations and/or a user experience of the dynamic transaction card to extend an energy storage life of the dynamic transaction card, promote various behaviors, and/or detect system and/or device defects. A dynamic transaction card may include a dynamic transaction card with various configuration and/or functionality that use the power components (e.g., printed circuit board (PCB), energy storage component, battery, and/or the like) of the dynamic transaction card. The configuration and/or functionality data may include, for example, sensor input, connection data, transaction data, display data, and/or the like. The configuration and/or functionality data may then be used to determine optimal configuration settings.