Transfer Apparatus Selection Using Performance Prognosis Ranking
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Solution Overview
Problem
Optimizing machine-learning outputs for large and varied data sets becomes untenable due to trade-offs between sophistication and efficiency, making it difficult to select an optimal transfer apparatus based on user preferences.
Innovation Solution
A system and method using a computing device to receive user preferences and generate performance prognoses for candidate transfer apparatuses through trained models, ranking these prognoses based on a loss function, and optimizing an objective function to select the most satisfying transfer apparatus.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine-learning methods are used to analyze patterns and solve problems using large quantities of data, then analysis capability and problem-solving capability are improved, but optimizing instructions for users becomes untenable due to tradeoffs between sophistication and efficiency
Solution Approach 1:
The patent extracts the optimization function from the complex machine-learning system and separates it into distinct components: a recommendation engine that generates candidate instructions, a performance prognosis system that evaluates them, and a selection module that chooses the optimal instruction. This extraction makes the system manageable by handling each aspect independently rather than optimizing everything at once.
Solution Approach 2:
The system segments the instruction optimization process into multiple discrete steps: generating candidate instructions, evaluating their performance prognoses, ranking them based on user preferences, and selecting the best one. This segmentation transforms an untenable monolithic optimization problem into a series of manageable sub-tasks that can be executed sequentially.
2Measurement precision
If multiple candidate transfer apparatuses are evaluated with detailed performance prognoses, then selection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system generates performance prognoses for multiple candidate transfer apparatuses beyond what might be strictly necessary, evaluating more options than a simple selection would require. This excessive action ensures that the best matching apparatus is found with high accuracy, even though it increases processing time. The tradeoff accepts additional computational effort to guarantee optimal selection.
Solution Approach 2:
The system performs preliminary evaluation of candidate transfer apparatuses by generating performance prognoses before the actual selection is made. This preliminary action includes predicting delivery times, costs, and other performance metrics in advance, allowing for informed comparison and selection without delaying the final decision.
3Measurement precision
If trained candidate transfer apparatus models generate performance prognoses for each candidate, then prediction accuracy is improved, but model training and inference complexity increases
Solution Approach 1:
The patent employs multiple trained candidate transfer apparatus models that can be selectively applied depending on the specific transfer scenario. Each model is specialized for evaluating particular types of transfer apparatus or conditions, but collectively they provide universal coverage for all candidate evaluations. This multi-functionality allows accurate predictions across diverse scenarios without requiring a single overly complex model.
Solution Approach 2:
The system changes parameters by using different trained models for different candidate transfer apparatus types, selecting which model to apply based on the specific characteristics of each candidate. This parameter change approach allows the system to maintain high prediction accuracy by matching the right model to the right candidate, rather than using a fixed complex model for all cases.
Data Source
AI summary
A method for determining a transfer apparatus based on user preferences includes receiving, by a computing device, at least a transfer invocation and a plurality of user preferences and generating, by the computing device, and for each candidate transfer apparatus of a plurality of candidate transfer apparatuses, a plurality of performance prognoses corresponding to the plurality of user preferences. The method includes selecting, by the computing device, a candidate transfer apparatus as a function of the plurality of performance prognoses and providing, by the computing device, the selected candidate transfer apparatus to a user.