Adaptive Code Management for Device Resource Optimization
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Solution Overview
Problem
Conventional web code execution systems fail to adapt to varying user device resources, leading to inefficient performance across different devices and network environments, as they are often designed as a one-size-fits-all solution or segmented by device type without considering the complexities of individual device and operating environments.
Innovation Solution
An adaptive code management system that identifies and measures available resources on user devices, generating adaptive code to adjust its level of activity based on resource availability, allowing for dynamic optimization across any device type and network environment without prior knowledge of the web environment or static pre-configured settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If code is configured to execute the same on all user devices (one-size-fits-all approach), then ease of deployment is improved, but performance efficiency deteriorates due to resource waste on constrained devices
Solution Approach 1:
The patent segments code execution into multiple tiers based on device resource availability. The system divides the codebase into core essential functions and optional enhanced functions, allowing selective execution based on device capabilities. This segmentation enables the same code to be deployed universally while adapting execution behavior to different device types, resolving the contradiction between deployment simplicity and performance efficiency.
Solution Approach 2:
The patent implements dynamic code execution that adjusts behavior based on real-time device resource conditions. The system continuously monitors device resources and dynamically modifies which code modules execute, enabling the code to adapt its resource consumption patterns. This dynamic approach maintains ease of deployment while optimizing performance efficiency across diverse devices.
2Productivity
If code is pre-configured for specific device types (desktop, mobile, tablet), then performance efficiency is improved for targeted devices, but device complexity increases due to multiple code configurations
Solution Approach 1:
The patent applies local quality by assigning different execution priorities and resource allocation strategies to different device types rather than creating entirely separate code configurations. Each device type receives customized execution parameters (e.g., caching strategies, rendering priorities, network request patterns) while sharing the same core codebase. This approach improves performance efficiency for targeted devices without proportionally increasing code configuration complexity.
Solution Approach 2:
The patent optimizes performance by changing execution parameters (thread pool sizes, memory allocation, timeout values, batch processing sizes) based on device type rather than creating separate code configurations. This parameter-based adaptation allows the same code to achieve device-specific performance optimization while minimizing the complexity increase associated with multiple code versions.
3Adaptability or versatility
If code executes comprehensive functions on all devices, then functionality is improved, but resource consumption increases burdening devices with limited resources
Solution Approach 1:
The patent implements partial action by executing only the subset of functions necessary for each device type. The system identifies core essential functions that must run on all devices and optional enhanced functions that execute only on devices with sufficient resources. This approach maintains comprehensive functionality across the device ecosystem while reducing resource consumption on constrained devices by executing only partial function sets.
4Productivity
If code is optimized for high-resource devices, then performance is improved, but adaptability deteriorates as the code fails to perform well on low-resource devices
Solution Approach 1:
The patent creates a universal code execution framework that functions across all device types by implementing a resource-aware execution engine. This engine automatically detects device capabilities and adjusts execution strategies accordingly, allowing the same code to achieve optimal performance on high-resource devices while maintaining acceptable functionality on low-resource devices. The universal framework eliminates the need for device-specific code versions, thereby maintaining adaptability while preserving performance optimization.
Data Source
AI summary
Systems and methods are disclosed for generating adaptive code based on resource availability. The systems and methods measure, by a code set executed via a web browser of a first user device, a first set of data associated with the first user device of multiple user devices. The code set further measures a second set of data associated with a computing environment of the first user device. A first value for a resource availability function is calculated in view of the first set of data and the second set of data. The first value is compared to a set of values for the resource availability function associated with the plurality of user devices to determine a classification for the first user device. A level of activity of the code set is adjusted in view of a rule associated with the classification of the first user device.