Computer-Vision Component Scoring for Computing Product Layouts
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing information handling systems lack efficient methods for analyzing and optimizing the layout and configuration of computing products, particularly in terms of component identification, size approximation, proximity, and weight distribution, which are crucial for optimal performance and design.
Innovation Solution
A method involving computer-vision techniques to analyze computing product layouts by segmenting images, approximating component sizes, determining layout weights, and calculating scores based on size, weight, and proximity, followed by iterative permutation and optimization of layouts to determine the optimal configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If computer-vision and machine learning models are used to analyze computing product layouts, then measurement precision of component properties is improved, but device complexity increases
Solution Approach 1:
A classification model serves as an intermediary between the input images and the layout analysis system. The model segments images to identify computing components, converting unstructured visual data into structured component information that the analysis system can process. This intermediary layer improves measurement precision while managing system complexity by encapsulating the complexity within the model itself.
2Productivity
If iterative permutation and optimization of layouts is performed, then productivity of layout optimization is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-calculating component scores based on size, weight, and proximity metrics before full optimization. Layouts are pre-evaluated using these scores to identify promising candidates for iterative permutation. This preliminary scoring reduces the search space for optimization, improving productivity while limiting time loss by avoiding exhaustive permutation of all possible layouts.
3Area of stationary object
If multiple computing components are placed in close proximity to maximize density, then area utilization is improved, but harmful factors increase due to thermal and signal interference
Solution Approach 1:
The system applies local quality by assigning different weights to different components based on their thermal and signal characteristics. High-heat-generating components receive higher thermal weights, and high-speed signal components receive higher SI weights. The scoring system locally adjusts component placement recommendations to maintain appropriate spacing for heat-generating components while allowing denser placement of low-interference components, thus maximizing area utilization while managing harmful factors.
Data Source
AI summary
A method of analyzing a particular computing product, including: receiving a plurality of images of a particular layout of the particular computing product; segmenting, using a classification model, the plurality of images to identify computing components of the particular computing product; analyzing the particular layout, including, for each computing component of the particular layout: approximating a physical size of the computing component; identifying a predetermined layout weight of the computing component; determining a proximity of the component to each other computing component; calculating a computing component score for the computing component for the particular layout based on i) the physical size of the computing component, ii) the predetermined layout weight of the computing component, and iii) the proximity of the computing component to each other computing component; and determining a layout score of the particular layout based on the computing component score of each of the computing components.