Market-Driven Computing Product Layout Generation
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Solution Overview
Problem
Existing information handling systems lack the ability to efficiently generate targeted computing products tailored to market trends and user-specific needs, leading to suboptimal configurations and inefficiencies in processing, storage, and communication of data.
Innovation Solution
A system and method utilizing a market prediction model to identify target computing components and features, iteratively generating and permutating layouts, and creating a data table for targeted computing products, incorporating modules for web crawling, configuration determination, computer vision analysis, and sentiment accreditation to optimize product design and configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If information handling systems are configured for specific users or applications, then the system can be optimized for specific processing needs, but the system loses general applicability and requires multiple specialized configurations
Solution Approach 1:
The patent implements dynamic configuration generation that adapts to market trends and user requirements in real-time. The system uses iterative permutation of computing components based on market prediction model outputs, allowing the same information handling system to generate optimized configurations for different target markets without requiring permanent specialized hardware modifications.
Solution Approach 2:
The system changes configuration parameters (computing components, features, layouts) based on market trend data and product profiles. By iteratively permutating layouts and selecting optimal combinations based on market predictions, the system achieves specialized optimization for different markets while maintaining a general-purpose platform.
2Quantity of substance
If information handling systems process and store large amounts of information, then the system capacity increases, but the complexity of managing and processing the data increases
Solution Approach 1:
The patent segments the information handling system into distinct functional modules: market prediction model, product profile database, configuration generator with iterative permutation capability, and layout optimization components. This modular segmentation allows each module to handle specific aspects of data processing independently, reducing overall system complexity while maintaining high information processing capacity.
3Measurement precision
If the system generates highly customized computing products, then product suitability for specific markets improves, but the time and computational resources required for product development increase
Solution Approach 1:
The system performs preliminary actions by pre-building a comprehensive database of product profiles with computing components and features, and pre-training the market prediction model on historical data. When generating targeted configurations, the system iteratively permutes layouts based on market trends, selecting optimal combinations from pre-validated options, which significantly reduces development time compared to creating configurations from scratch.
Solution Approach 2:
The market prediction model provides feedback on which computing component combinations and layouts are most suitable for target markets. The system uses this feedback iteratively to refine configuration selections, adjusting the permutation process based on predicted market success metrics, thereby achieving high market targeting accuracy efficiently.
Data Source
AI summary
Generating targeted computing products, including: generating market trend data associated with the computing products; comparing the market trend data with the product profiles of each of the computing products; identifying, based on the comparing, target computing components and target features of the market trend data absent from the product profiles of the computing products; iteratively generating, based on the target computing components, a plurality of layouts of a targeted computing product; iteratively permutating each of the plurality of layouts of the targeted computing product based on a plurality of combinations of the target features of each of the target computing components of each of the plurality of layouts; generating, for the targeted computing product, a data table indicating each of the plurality of permutated layouts and each of the combinations of the target features of each of the target computing components of each of the plurality of permutated layouts.