Digital Menu Preparation-Time Prediction for Dynamic Kitchen Conditions
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for predicting food order preparation times in restaurants are inaccurate and fail to account for real-time kitchen conditions, leading to frustrated diners and delivery service providers due to unreliable pickup and delivery time estimates.
Innovation Solution
A deep learning system that utilizes two neural networks to generate item-level preparation time predictions based on historical data and real-time kitchen metadata, updating digital menus in real-time to provide accurate preparation time estimates to guests and delivery services.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional estimation techniques (fixed intervals, order counting) are used to predict preparation times, then the system is simple to operate, but the prediction accuracy deteriorates significantly
Solution Approach 1:
The patent replaces manual estimation methods (mechanical counting and fixed intervals) with an automated deep learning neural network system that processes kitchen metadata and historical data to generate accurate preparation time predictions dynamically
Solution Approach 2:
The system enables the kitchen operations to self-monitor and self-adjust by automatically collecting metadata from kitchen displays, processing it through the neural network, and generating predictions without requiring manual intervention from kitchen staff
2Adaptability or versatility
If fixed interval estimation (e.g., adding 30 minutes) is used, then the prediction method is easy to implement, but it fails to adapt to changing kitchen conditions
Solution Approach 1:
The patent transforms the static fixed-interval estimation into a dynamic prediction system that continuously adapts to changing kitchen conditions by processing real-time metadata (number of orders, kitchen staff, equipment status) through a neural network model
Solution Approach 2:
The system implements feedback by continuously monitoring kitchen metadata and historical preparation times, comparing predicted versus actual times, and using this information to refine future predictions through the trained neural network model
3Reliability
If subjective snooze button adjustments are made by kitchen management, then the system can respond to kitchen condition changes, but the prediction accuracy becomes inconsistent and unreliable
Solution Approach 1:
The system replaces subjective human judgment (snooze button decisions) with an automated neural network that objectively analyzes kitchen metadata and historical data to generate consistent, reliable predictions without requiring manual intervention
Solution Approach 2:
The patent replaces the manual snooze button mechanism with an automated deep learning system that processes multiple data sources and generates predictions based on learned patterns rather than subjective human judgment
Data Source
AI summary
A system and method are provided for dynamically updating a digital menu using deep-learning-based preparation-time prediction. A first deep learning neural network generates item-level embeddings for menu items based on historical preparation time records. Actual and estimated item-level preparation time vectors are generated using cosine-similarity weighting across subsets of the historical data. A second deep learning neural network is trained using the item-level vectors, ground-truth preparation times, and normalized non-categorical metadata processed through dense vector layers and concatenation. The trained network is executed to generate predicted item-level preparation times for menu items currently available for ordering. An updated digital menu including the predicted item-level preparation times is generated and transmitted to a client device, and the digital menu is automatically updated in real time on the client device in response to changes in the predicted preparation times.