Chipset-Level Device Customization Using Environmental Audio Profiles
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Solution Overview
Problem
Customizing user devices, such as handheld and portable electronic devices, to adapt to varying operating environments is cumbersome and resource-intensive, requiring manual intervention and significant time, as modifying BSP-chipset level parameters typically involves accessing proprietary chipset code and requires specialized expertise, leading to high maintenance costs and unsatisfactory end-user experiences.
Innovation Solution
An apparatus and method that adjust BSP-chipset level parameters, such as screen brightness, noise cancellation, and keypad mapping, in real-time based on environmental conditions by analyzing audio samples with a microphone and associating them with pre-loaded conditions stored in a database, using algorithms like FFT to determine background noise intensity and communicate with the chipset to modify parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual customization of BSP-chipset level parameters is performed, then device adaptability to specific environments is improved, but device complexity and time consumption increase significantly
Solution Approach 1:
The system enables devices to automatically detect their operating environment (acoustic, lighting, motion conditions) and self-customize BSP-chipset level parameters without manual intervention. The device performs environmental sensing, condition matching against pre-loaded profiles, and automatic parameter adjustment, making the complex customization process transparent to users while maintaining high adaptability.
Solution Approach 2:
Multiple pre-loaded customization conditions and parameter profiles are prepared in advance and stored in the device memory. These pre-configured profiles contain optimized BSP-chipset parameters for various environmental scenarios, allowing the device to quickly select and apply appropriate settings without performing complex real-time optimization, thus reducing both complexity and time consumption.
2Adaptability or versatility
If manual customization of BSP-chipset level parameters is performed, then device adaptability to specific environments is improved, but time consumption increases significantly
Solution Approach 1:
The system enables devices to automatically detect their operating environment (acoustic, lighting, motion conditions) and self-customize BSP-chipset level parameters without manual intervention. The device performs environmental sensing, condition matching against pre-loaded profiles, and automatic parameter adjustment, making the complex customization process transparent to users while maintaining high adaptability.
Solution Approach 2:
Multiple pre-loaded customization conditions and parameter profiles are prepared in advance and stored in the device memory. These pre-configured profiles contain optimized BSP-chipset parameters for various environmental scenarios, allowing the device to quickly select and apply appropriate settings without performing complex real-time optimization, thus reducing both complexity and time consumption.
3Reliability
If manual customization of BSP-chipset level parameters is performed, then device performance optimization is improved, but maintenance costs increase
Solution Approach 1:
The system enables devices to automatically detect their operating environment (acoustic, lighting, motion conditions) and self-customize BSP-chipset level parameters without manual intervention. The device performs environmental sensing, condition matching against pre-loaded profiles, and automatic parameter adjustment, making the complex customization process transparent to users while maintaining high adaptability.
Solution Approach 2:
A software layer is introduced between the hardware chipset and the user/environment, which handles the complex BSP-chipset parameter management. This intermediary layer includes environmental sensors, condition matching logic, and parameter application mechanisms, shielding users from technical complexity while enabling performance optimization. It also provides a framework for automated diagnostics and updates, reducing maintenance burden.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables automatic, real-time customization of user devices to optimize performance in different environments, reducing the need for manual intervention, lowering maintenance costs, and improving end-user experience by adapting device settings to specific acoustic and operational conditions.
Implementation Method 1
a microphone that receives audio recording samples of environment in which the apparatus is located
Implementation Method 2
The processor may be configured to analyze the one or more audio recording samples to determine the background noise intensity by applying a fast fourier transformation (FFT) algorithm or a similar algorithm
Data Source
AI summary
Provided herein are methods and systems for customizing user devices at the chipset level. Adjustments in BSP-chipset level parameters of the user devices may be performed depending on operating conditions of the user devices. Audio recording samples as well as other sensed conditions may be analyzed to determine a pre-loaded condition which causes self-adjustment of BSP-chipset level parameters of the user device. BSP-chipset level parameters may include any of a screen brightness, LED blinking behavior, LED color, speaker volume, microphone gain, noise cancellation, echo cancellation, battery performance, keypad mapping, touch screen calibration, Wi-Fi profile, WWAN carrier selection, scanner beep volume, the like, and combinations thereof.