Predicting User Experience from Hardware Specifications
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Solution Overview
Problem
Device manufacturers face challenges in estimating overall user experience for devices without first manufacturing a prototype, leading to inefficiencies and mismatches between performance claims and consumer experience.
Innovation Solution
The implementation of a machine learning model that predicts device performance for end-user workloads based on specific device hardware, allowing manufacturers to modify specifications as needed without building a prototype.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manufacturers build and test device prototypes to estimate user experience, then measurement precision of device performance is improved, but loss of time and productivity deteriorate
Solution Approach 1:
The patent creates virtual copies of physical devices through detailed computational models that replicate hardware specifications, software configurations, and operational characteristics. These digital twins allow manufacturers to simulate and evaluate device performance across multiple scenarios without building physical prototypes, thereby maintaining measurement precision while eliminating time-consuming iterative prototyping cycles
Solution Approach 2:
The system performs preliminary performance evaluations through automated simulations before physical device production. By pre-assessing device configurations, identifying potential performance issues, and optimizing specifications in the virtual environment, manufacturers can proceed directly to production with confidence, significantly reducing time to market while maintaining accurate performance predictions
2Reliability
If manufacturers build and test device prototypes to estimate user experience, then reliability of performance claims is improved, but device complexity increases
Solution Approach 1:
The patent replaces the mechanical system of physical prototyping and hands-on testing with an automated computational simulation system. The evaluation platform uses software-based models to replicate device behavior, automatically assess performance metrics, and generate reliability predictions without requiring physical device assembly or manual testing procedures, thereby maintaining reliability while reducing process complexity
Solution Approach 2:
The evaluation system serves multiple functions simultaneously: it models hardware specifications, simulates software performance, predicts user experience metrics, identifies optimization opportunities, and generates comprehensive reports. This multi-functional approach consolidates what would otherwise require multiple separate testing processes into a single unified system, reducing overall complexity while maintaining comprehensive reliability assessment
3Manufacturing precision
If manufacturers use microbenchmark scores to test hardware components, then manufacturing precision of individual components is improved, but measurement precision of overall user experience deteriorates
Solution Approach 1:
The patent merges individual component performance data from microbenchmarks with system-level interaction models to create a comprehensive performance prediction. By integrating CPU, GPU, RAM, storage, and other component specifications along with their interdependencies and software workload characteristics, the system achieves accurate overall device performance prediction that reflects real user experience rather than isolated component capabilities
Solution Approach 2:
The system transforms component-level parameters from microbenchmarks into system-level performance predictions by applying weighting factors, interaction coefficients, and workload-specific adjustments. This parameter transformation allows the model to convert precise component specifications into accurate overall performance metrics that reflect actual user experience across different usage scenarios
Data Source
AI summary
A method including receiving first data including a feature corresponding to an application, receiving second data including a specification of a component included in a device, analyzing a performance of the device based on the first data and the second data using a model, and modifying the specification based on the performance of the device.