Machine Learning Provider Classification for Food Delivery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data management systems using artificial intelligence and machine-learning struggle to effectively handle situations where a requested alimentary combination is not available from the first selected provider, leading to user dissatisfaction and unmet culinary needs.
Innovation Solution
A system and method that utilize machine-learning processes to classify and rank alternative alimentary providers based on cuisine type, dieting methods, and user preferences, computing an alimentary combination score to select a suitable replacement provider, ensuring the user receives their desired food combination even if it's not available from the initial choice.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system selects only the first alimentary provider based on initial criteria, then the selection process is simple and fast, but the user may not receive the desired alimentary combination if the first provider cannot fulfill the request
Solution Approach 1:
The system pre-classifies multiple alimentary providers into different categories (first alimentary providers matching initial criteria, and second alimentary providers as alternatives) before a request is made. This preliminary classification ensures that when the first provider cannot fulfill the request, pre-identified alternative providers are ready to step in, maintaining service reliability without adding complex real-time decision-making.
Solution Approach 2:
The patent introduces a classification system that acts as an intermediary layer between the user's request and the alimentary providers. This classification mechanism categorizes providers into first and second alimentary providers based on their ability to meet specific criteria, enabling a structured approach to provider selection that balances simplicity with reliability.
2Adaptability or versatility
If the system classifies and evaluates multiple alternative alimentary providers, then the user receives better service quality and alternatives, but the data processing time and computational resources increase
Solution Approach 1:
The system performs classification of alimentary providers into different categories in advance, before actual service requests are processed. This preliminary action creates a ready-to-use structured framework of first and second alimentary providers, allowing the system to quickly retrieve and present alternatives without performing complex real-time analysis when needed.
Solution Approach 2:
The patent segments the alimentary provider pool into distinct categories (first alimentary providers and second alimentary providers) based on classification criteria. This segmentation allows the system to efficiently manage and query different provider groups separately, reducing the computational burden of evaluating all providers simultaneously while maintaining the ability to provide versatile alternatives.
3Measurement precision
If the system uses machine-learning processes to compute alimentary combination scores, then the selection accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The machine-learning-based scoring system is trained and established in advance to create classification criteria for identifying first and second alimentary providers. Once trained, the model provides a ready-to-use scoring framework that can be applied efficiently to new providers and requests, balancing measurement precision with operational simplicity.
Solution Approach 2:
The patent employs machine-learning processes that analyze multiple parameters and features of alimentary providers to compute comprehensive scores. By transforming multiple input parameters into a unified scoring system, the patent achieves high measurement precision in provider evaluation while managing computational complexity through parameter integration and synthesis.
Data Source
AI summary
A method of determining a second alimentary provider is disclosed. The method inputs an order for an alimentary combination from a user. The alimentary combination is prepared by a first alimentary provider. The method classifies a plurality of alimentary providers. The method computes an alimentary provider score for a plurality of second alimentary combinations prepared by the plurality of alimentary providers as a function of a first machine-learning process, the machine learning process trained by training data correlating alimentary provider scores to alimentary combinations. The method selects a second alimentary provider from the plurality of alimentary providers as a function of the alimentary provider score. The method outputs the second alimentary provider to the user. A system of determining a second alimentary provider is also disclosed.