Recipe Processing Software for Automated Food Preparation
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Solution Overview
Problem
Current automated food preparation systems require specific machine instructions and cannot accommodate recipes from standard cookbooks or websites without human intervention, and instructions vary between systems from different manufacturers, limiting their versatility and compatibility.
Innovation Solution
A computer system that converts natural language recipes into machine-readable instructions for automated food preparation systems, allowing for the use of pre-existing recipes with minimal human intervention, and includes features for scheduling, ingredient prediction, and remote control, using a Recipe Processing Software, Controller, and Internet Recipe Server to interact with and manage automated equipment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If automated food preparation systems use specific machine instructions for each system, then the system can execute recipes reliably, but the system cannot accommodate recipes from standard cookbooks or websites without human intervention
Solution Approach 1:
The patent introduces an intermediary system comprising a natural language processor and instruction generator that converts standard cookbook recipes into machine-specific instructions. This intermediary layer enables compatibility between diverse recipe formats and various automated food preparation systems without requiring manual intervention, thus resolving the contradiction between recipe adaptability and system complexity.
Solution Approach 2:
The system implements a universal recipe processing framework that can handle multiple recipe formats and translate them into system-specific instructions. The natural language processor and instruction generator serve multiple functions including parsing, interpretation, and translation, enabling a single system to accommodate recipes from any source while maintaining reliable execution across different automated food preparation equipment.
2Reliability
If machine instructions are customized for each automated food preparation system, then execution reliability is improved, but versatility across different manufacturers' systems deteriorates
Solution Approach 1:
The instruction generator acts as an intermediary that receives natural language recipes and translates them into system-specific machine instructions. This mediation process ensures that the semantic meaning and execution requirements of the original recipe are preserved while adapting the instructions to the specific capabilities and syntax of different automated food preparation systems, thereby maintaining both reliability and cross-system compatibility.
Solution Approach 2:
The system dynamically adjusts instruction parameters based on the target system's capabilities. The instruction generator modifies timing, temperature, mixing speeds, and other process parameters to match the specific characteristics of different manufacturers' equipment while maintaining the essential cooking logic, thus ensuring reliable execution across diverse systems without sacrificing adaptability.
3Extent of automation
If human intervention is required to convert recipes to machine instructions, then instruction accuracy is maintained, but automation level and productivity deteriorate
Solution Approach 1:
The system implements self-service automation where the natural language processor and instruction generator automatically convert recipes into machine instructions without human intervention. The system independently parses the recipe text, identifies cooking operations and parameters, and generates appropriate machine commands, thereby achieving full automation while maintaining information accuracy through sophisticated natural language processing algorithms.
Solution Approach 2:
The system incorporates feedback mechanisms where the generated machine instructions are validated against the original recipe requirements. The instruction generator monitors the conversion process and adjusts its output to ensure that all essential cooking information is preserved and accurately translated into executable machine commands, maintaining interpretation accuracy while achieving complete automation.
Data Source
AI summary
A method and apparatus are presented for automatically generating machine control instructions for controlling automated food preparation systems and equipment from ordinary recipes in natural language. The invention allows the sharing, remote execution, scheduling, and automatic ingredient ordering for such recipes to allow professional food preparation with little or no human intervention.