Parameter Calibration for Information Handling System Device Settings
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Solution Overview
Problem
Information handling systems face challenges in maintaining a cohesive user experience when users switch between different peripheral devices, such as changing from a computer monitor and keyboard to a television and game controller, due to varying configurations that can lead to undesirable changes in user experience and resource inefficiencies.
Innovation Solution
The system automatically propagates changes in settings from one device to another based on a calibration relationship, using a parameter matrix and machine learning to determine corresponding changes across different device types, reducing the need for individual configuration and minimizing signaling transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users manually configure settings for each peripheral device individually, then each device can be optimized for its specific function, but the complexity of configuration increases and user experience consistency deteriorates when switching between devices
Solution Approach 1:
The system creates a universal configuration profile that works across multiple device types (mouse, keyboard, game controller, display). The calibration relationship establishes universal parameter mappings where settings defined once can be applied to different devices, making the configuration system multi-functional and device-agnostic while maintaining device-specific optimization
Solution Approach 2:
The system uses parameter transformation through calibration relationships to convert settings between different device types. When a user changes a parameter on one device, the system automatically transforms and applies the corresponding parameter change to other devices using pre-established calibration mappings, eliminating manual reconfiguration while maintaining device-specific performance
2Reliability
If the system automatically propagates settings across multiple device types, then user experience consistency improves, but the computational resources and signaling transmission required increase
Solution Approach 1:
The system performs preliminary calibration to establish parameter relationships between different device types before actual use. This pre-computed calibration data is stored and reused, so when settings need to be propagated, the system only needs to apply pre-determined transformations rather than performing complex real-time calculations, significantly reducing computational resource consumption during operation
Solution Approach 2:
The system creates simplified copies of configuration profiles that can be rapidly replicated across devices. Instead of performing complex real-time computations for each setting propagation, the system uses pre-calibrated parameter mappings that allow fast copying and application of settings, reducing both computational load and signaling transmission requirements
3Ease of operation
If the system establishes calibration relationships between different device types, then automatic setting propagation improves user experience, but the initial configuration time and processing required increase
Solution Approach 1:
The system performs self-calibration by automatically detecting device characteristics and establishing parameter relationships without requiring extensive manual user input. The calibration process is automated and can be completed in the background, reducing the time users need to spend on initial configuration while still establishing accurate device-specific parameter mappings for automatic setting propagation
Data Source
AI summary
An apparatus includes a memory and one or more processors coupled to the memory. The one or more processors are configured to determine a first setting of a first parameter associated with a first device. The first device is associated with a first device type. The one or more processors are further configured to detect an event. The event is associated with one of the first parameter or a second device that is associated with a second parameter, and the second device is associated with a second device type. The one or more processors are further configured to determine, based on detecting the event, a second setting of the second parameter for the second device based on a calibration relationship between the first parameter and the second parameter.