Generative AI Food Manufacturing Instructions
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Solution Overview
Problem
Conventional manufacturing processes are limited in producing custom products, require specialized machinery, and are often time-consuming and error-prone.
Innovation Solution
A method utilizing generative artificial intelligence models to receive user prompts, generate 2D and 3D models of food items, and produce instructions for additive or subtractive manufacturing devices, enabling fast and automated custom food production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional manufacturing processes are used, then specialized machinery and manual design are required, but this increases device complexity and time consumption
Solution Approach 1:
The patent replaces manual design and specialized machinery with AI-generated instructions that can be executed by standard manufacturing equipment. The system uses generative AI models to create manufacturing instructions from text prompts, eliminating the need for manual design processes and reducing dependency on specialized machinery while increasing automation extent.
Solution Approach 2:
The patent introduces an intermediary layer of AI-generated instructions between the user's text prompt and the manufacturing process. This intermediary layer translates high-level descriptions into actionable manufacturing steps, allowing standard equipment to perform specialized functions without requiring direct operator intervention or complex specialized machinery.
2Productivity
If manual design and prototyping workflows are used, then flexibility in custom products is achieved, but this increases time consumption and reduces productivity
Solution Approach 1:
The patent performs preliminary actions by pre-training generative AI models on extensive datasets of food items, manufacturing processes, and material properties. This pre-processing enables the system to rapidly generate accurate manufacturing instructions for custom food products without requiring time-consuming manual design and prototyping, significantly increasing productivity.
Solution Approach 2:
The patent uses generative AI models to create virtual copies and representations of food items and manufacturing processes. By generating digital models and instructions through AI rather than physical prototyping, the system eliminates time-consuming manual design iterations while maintaining flexibility for custom products, thereby reducing time loss and increasing production speed.
3Manufacturing precision
If manual design processes are used, then design flexibility is maintained, but this increases error-proneness and reduces manufacturing precision
Solution Approach 1:
The patent implements feedback mechanisms where the AI system continuously refines manufacturing instructions based on analyzed feedback about food item requirements and manufacturing constraints. This feedback loop ensures high manufacturing precision by iteratively improving the generated instructions while maintaining ease of operation through automated processing, eliminating manual design errors.
Data Source
AI summary
Certain aspects of the disclosure pertain to promptable food manufacturing. A prompt can be received, and at least one two-dimensional image and a three-dimensional model can be produced using generative artificial intelligence based on the prompt. The two-dimensional image and three-dimensional model can be refined through iterative user feedback. The three-dimensional model can be validated against physical, technical, and logistic constraints utilizing simulation and machine learning models. After validation, instructions can be generated based on the three-dimensional model, targeting one or more manufacturing devices. The instructions can then be transmitted to one or more manufacturing devices to produce a food item. The produced food item can subsequently be scanned and compared to the three-dimensional model. Differences can be determined and utilized to update the instructions generated.