Machine Learning Transfer Time Estimation
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Solution Overview
Problem
Current systems lack accurate estimation of alimentary element transfer time, considering user-specific dietary restrictions and dynamic environmental factors like inclement weather, which affects the reliability of food delivery timelines.
Innovation Solution
A computing device uses machine-learning models to determine estimated transfer times by analyzing alimentary element limitations, including dietary restrictions, and numerical data on transfer apparatuses traversing various paths, generating accuracy measures and updating estimates based on retraining data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional transfer time estimation methods are used, then the system is simple to operate, but the accuracy of transfer time estimation is insufficient and does not account for user-specific dietary restrictions and environmental factors
Solution Approach 1:
A machine learning model is introduced as an intermediary component between the transfer time calculation system and the user requirements. This model processes multiple input factors (dietary restrictions, environmental conditions, transfer apparatus data) and outputs refined transfer time estimates, thereby improving accuracy without requiring direct complex integration of all factors into the core system logic
Solution Approach 2:
The patent replaces traditional mechanical or rule-based transfer time estimation methods with an intelligent machine learning system. Instead of using fixed algorithms or manual calculations, the system employs trained machine learning models that automatically process complex inputs and generate accurate transfer time predictions, substituting mechanical computation with intelligent processing
2Reliability
If comprehensive factors including dietary restrictions and environmental conditions are considered, then the reliability of transfer time estimation is improved, but the computational requirements and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and organizing input data (dietary restrictions, environmental factors, transfer apparatus specifications) before feeding them into the machine learning model. This preparation work is done in advance to enable faster and more efficient inference when transfer time estimation is actually needed, reducing the computational burden during critical operations
Solution Approach 2:
The patent utilizes parameter changes by training machine learning models with varying input parameters and conditions. The system adjusts and optimizes model parameters during training to achieve reliable transfer time estimates while minimizing computational energy consumption during deployment. The model learns to efficiently process different combinations of input parameters without requiring exhaustive real-time calculations
Data Source
AI summary
A system for determining estimated alimentary element transfer time, the system comprising a computing device adapted to receive a plurality of alimentary elements and a plurality of destinations, determine an estimated transfer time, wherein determining includes retrieving a plurality of locations of a plurality of transfer apparatuses wherein the plurality of locations are associated with a plurality of transfer paths, generate a plurality of transfer times, determine an estimated transfer time as a function of the plurality of transfer times, generate an accuracy measure based on the estimated transfer time, wherein generating includes computing a plurality of transfer time variations, generate an accuracy measure based on a plurality of statistical parameters, and provide an estimated transfer time, wherein providing includes receiving a new alimentary element request, retrieving an estimated transfer time, retrieving the accuracy measure, and provide an estimated transfer time accuracy message.