Alimentary Provider Selection System Using Machine Learning Ranking
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Solution Overview
Problem
Users face challenges in efficiently selecting a convenient alimentary provider that meets their dietary needs and location requirements, leading to wasted time due to the complexity of ordering food with potential dietary restrictions and distance considerations.
Innovation Solution
A system and method utilizing a computing device to receive user inputs, generate and rank alimentary providers based on proximity, dietary compatibility, and preparation time, using machine-learning processes to determine the best options for food combinations and delivery paths, thereby optimizing the selection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If users manually evaluate multiple restaurants considering proximity and dietary restrictions, then they can find suitable food options, but the time required for decision-making increases significantly
Solution Approach 1:
The system performs automatic evaluation and ranking of restaurants based on user preferences, dietary restrictions, and location data without requiring manual comparison by the user. The computing device autonomously processes multiple factors (distance, dietary compatibility, preparation time) and generates a ranked list, allowing users to simply select from pre-evaluated options rather than manually analyzing each restaurant.
Solution Approach 2:
The patent replaces manual mechanical evaluation processes with automated computational systems. Machine learning models and algorithms automatically assess restaurant suitability based on input criteria, substituting the manual cognitive process of evaluating multiple restaurants with an automated information processing system that quickly generates ranked results.
2Measurement precision
If the system considers multiple factors (distance, dietary restrictions, preparation time) for restaurant selection, then the accuracy of recommendations improves, but the system complexity increases
Solution Approach 1:
The system divides the complex selection process into distinct functional modules: a proximity calculation module that determines distance-based rankings, a dietary compatibility module that assesses menu items against user restrictions, and a time estimation module that calculates preparation and delivery times. Each module handles a specific aspect of the evaluation, making the overall system more manageable and maintainable while comprehensively addressing multiple selection criteria.
Solution Approach 2:
The computing device is designed as a multi-functional system that simultaneously performs geospatial analysis, dietary restriction matching, preparation time estimation, and result ranking. The system integrates multiple evaluation functions into a unified platform that processes diverse input parameters (location, dietary needs, time constraints) and generates comprehensive restaurant recommendations, reducing the need for multiple separate tools.
Data Source
AI summary
A system for selecting an alimentary provider is disclosed. The system comprises a computing device configured to receive in input from a user device. Computing device is configured to generate a plurality of alimentary providers as a function of the input by identifying alimentary providers having a location within a threshold distance of the current geographical location of the user device. Computing device is configured to compute an alimentary combination factor for each alimentary provider as a function of a first machine-learning process. Computing device is configured to determine an alimentary combination assembly time and to select a transfer path to destination. Computing device is configured to output an alimentary combination total time. Computing device is configured to rank the plurality of alimentary providers as a function of decreasing alimentary combination factors and transmit the ranked plurality of alimentary providers. A method of selecting an alimentary provider is also disclosed.