Device Recommendation Engine Using ML Normalization
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Solution Overview
Problem
Users face difficulties in determining the most suitable computing devices to access applications due to the complexity of modern software and the variety of devices available, leading to inefficiencies in resource utilization and user experience.
Innovation Solution
A system that recommends computing devices and applications based on resource requirements and device attributes, using machine learning techniques to normalize and weight these factors, allowing for optimal device and application selection for specific tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually evaluate multiple computing devices and applications to determine the most suitable combination for task completion, then they can make informed decisions, but the process becomes time-consuming and complex due to the variety of devices and software capabilities
Solution Approach 1:
The system automatically performs device and application selection by comparing device attributes with application resource requirements, eliminating the need for users to manually evaluate multiple options. The recommendation engine self-services the matching process by autonomously analyzing compatibility metrics and generating optimal device-app pairings.
Solution Approach 2:
The patent replaces the manual mechanical process of evaluating devices with an automated computational system. The recommendation engine uses algorithmic comparisons of device attributes against application requirements, substituting human decision-making with machine-based automated matching and ranking systems.
2Measurement precision
If the system provides detailed recommendations based on multiple device attributes and application requirements, then the quality of recommendations improves, but the system complexity increases
Solution Approach 1:
The system segments the recommendation process into distinct components: device attribute extraction, application requirement identification, compatibility metric calculation, and recommendation generation. This modular segmentation allows complex matching logic to be broken down into manageable, independent modules that can be developed and maintained separately.
Solution Approach 2:
The patent introduces compatibility metrics as an intermediary layer between device attributes and application requirements. These metrics serve as mediators that translate heterogeneous device characteristics and application demands into a unified comparison framework, simplifying the overall system architecture while maintaining high recommendation quality.
3Adaptability or versatility
If the system normalizes and weights resource requirements and device attributes using machine learning techniques, then comparability between different devices is improved, but the computational processing requirements increase
Solution Approach 1:
The system performs preliminary normalization and weighting of device attributes and application requirements before the actual matching process. By pre-processing data to establish standardized comparison metrics and weights, the system reduces the computational burden during real-time recommendation generation, as the heavy normalization work has already been completed in advance.
Solution Approach 2:
The patent transforms device attributes and application requirements into standardized, normalized parameters through machine learning techniques. This parameter transformation converts heterogeneous device characteristics into a unified numerical framework, enabling efficient comparisons while the learned weights optimize the balance between attribute importance and computational efficiency.
Data Source
AI summary
Methods and systems for recommending one or more computing devices for accessing one or more applications are described herein. Resource requirements may be determined for at least one application. Such resource requirements may be, e.g., a display resolution. Computing device attributes may be determined for computing devices capable of executing the application. The resource requirements and/or the computing device attributes may be normalized and/or modified based on machine learning techniques. The machine learning techniques may modify the application resource requirements and/or computing device attributes based on user feedback. Distances between the resource requirements and the computing device attributes may be determined. A recommendation to use a particular preferred computing device may be transmitted based on the distance comparison. The recommendation may be based on the minimum or maximum distance calculated. User feedback regarding the recommendation may be received.