Feature-Weighted Computing Product Configurations for User Needs
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Solution Overview
Problem
Existing information handling systems lack an efficient method to determine optimal configurations based on varying user needs and requirements, leading to suboptimal performance and resource utilization.
Innovation Solution
A method involving identifying computing components with pre-defined weights, determining maximum and minimum weights, creating configurations based on these weights, and updating product specifications using machine learning models to optimize configurations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple computing components with various features are configured to meet different user needs, then adaptability is improved, but device complexity increases
Solution Approach 1:
The system segments the configuration determination process into distinct components: identifying computing components with their features, retrieving pre-defined weights for each feature, determining maximum and minimum weights, generating configurations based on weight criteria, and evaluating configurations. This segmentation allows each aspect to be handled independently, reducing overall system complexity while maintaining adaptability.
Solution Approach 2:
The system performs preliminary actions by pre-defining weights for each feature of computing components before configuration determination. These pre-defined weights (including maximum and minimum values) are stored and readily available when configuration needs arise, eliminating the need for complex real-time calculations and simplifying the configuration process.
2Measurement precision
If exhaustive configuration combinations are evaluated to find optimal configurations, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system uses pre-defined weight values as templates or copies that represent the importance of different features. Instead of performing complex real-time analyses, the system retrieves these pre-established weight copies and uses them to quickly evaluate configuration combinations, maintaining accuracy while reducing time consumption.
Solution Approach 2:
The system changes the evaluation parameter from complex performance modeling to simple weight comparison. By using pre-defined maximum and minimum weights for each feature, the system can quickly determine which configurations meet the criteria without exhaustive simulation, significantly reducing evaluation time while maintaining sufficient precision.
Data Source
AI summary
A method of determining a configuration of a computing product, including identifying a list of computing components associated with the computing product, the list includes features of each computing component; for each computing component: determining, based on the pre-defined weight associated with each feature of the computing component, a maximum and a minimum pre-defined weight of pre-defined weights associated with respective features for the computing component; determining, based on the maximum and the minimum pre-defined weights, combinations of the features for the computing component; creating configurations of the computing components based on each of the combinations of features of each of the computing components; for each configuration: determining a total weight of the configuration based on the pre-defined weights of the combination of features for each of the computing components of the configuration; updating, based on the total weight for the configuration, product specification data associated with the computing product.