DAG-Based Device Setting Determination
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Solution Overview
Problem
Current techniques for adjusting parameters in products, such as computer games and software, require manual adjustments by customers, leading to suboptimal settings and time-consuming trial and error, highlighting a need for a more efficient method to determine appropriate settings.
Innovation Solution
A system and method that identifies a plurality of parameters associated with a device and uses a directed acyclic graph (DAG) to determine optimal settings by mapping device parameters to nodes, where faster nodes represent better performance, allowing for automated determination and return of settings that meet predefined criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual parameter adjustment is used, then customers can customize settings, but it results in suboptimal settings and time-consuming trial and error
Solution Approach 1:
The system pre-calculates and stores optimal parameter settings for different device configurations in a database before runtime. When a device connects, the system retrieves pre-determined optimal settings based on the device's parameters, eliminating the need for real-time trial and error adjustment by customers.
Solution Approach 2:
The system automatically determines optimal settings by analyzing device parameters and querying the database without requiring manual customer intervention. The automated system serves itself by selecting and applying appropriate parameter combinations based on device capabilities, freeing customers from manual adjustment tasks.
2Ease of operation
If manual parameter adjustment is used, then customers can customize settings, but it leads to suboptimal product performance
Solution Approach 1:
The system automatically analyzes device parameters and selects optimal settings from the database without relying on customer expertise. This self-service mechanism ensures that optimal performance parameters are applied based on objective device characteristics rather than subjective customer judgment.
Solution Approach 2:
The system uses device parameter information as feedback to query the database and retrieve appropriate optimal settings. This feedback loop ensures that the settings applied are scientifically determined to be optimal for the specific device configuration, rather than relying on customer trial and error.
3Reliability
If automated setting determination is implemented, then optimal settings can be achieved, but system complexity increases
Solution Approach 1:
The complex task of determining optimal settings is performed in advance and stored in a database. The runtime system only needs to query and retrieve pre-computed results, significantly reducing the computational complexity during actual operation while maintaining optimal setting determination.
Solution Approach 2:
A database serves as an intermediary between device parameters and optimal settings. Instead of implementing complex real-time optimization algorithms, the system uses the database as a mediator to map device parameters to pre-determined optimal settings, simplifying the overall system architecture.
Data Source
AI summary
A system, method, and computer program product are provided for determining settings for a device. In use, a plurality of parameters associated with a device is identified. Additionally, one or more settings associated with the device are determined, based on the plurality of parameters.