Recipe Graph Data Structure for Multi-Device Control Readability
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Solution Overview
Problem
Existing recipe data represented as natural language graphs lacks machine readability and is limited in supporting various devices, making it difficult to control multiple devices effectively.
Innovation Solution
A device control system that represents recipe data as a graph with nodes for ingredients and actions, using unique IDs for ingredients and actions, enabling machine-readable data that can control various devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If recipe data is represented as natural language graphs, then human readability is maintained, but machine readability deteriorates
Solution Approach 1:
The patent segments recipe data into distinct graph components: ingredient nodes with unique ingredient IDs, action nodes with unique action IDs, and equipment nodes with unique equipment IDs. This segmentation transforms continuous natural language into discrete, machine-parseable units while preserving the semantic relationships through graph edges, thereby improving machine readability without sacrificing human readability.
Solution Approach 2:
The patent introduces an intermediary graph data structure that sits between natural language representation and machine execution. This graph serves as a mediator that translates human-readable recipe descriptions into a structured format that machines can interpret, maintaining both human and machine readability through its dual nature.
2Ease of operation
If recipe data uses generic action descriptions, then human understanding is improved, but device control precision deteriorates
Solution Approach 1:
The patent applies local quality by assigning different levels of specificity to different parts of the recipe data. Ingredient nodes contain detailed properties (name, quantity, unit), action nodes contain specific operation parameters (type, duration, temperature), and equipment nodes contain precise identification. This localized detail provides both human understanding through descriptive labels and machine precision through structured data.
Solution Approach 2:
The patent transforms generic action descriptions into parameterized action nodes that include specific attributes such as action type, duration, temperature, and equipment ID. This parameterization enables precise device control while maintaining human understanding through the structured organization of these parameters in the graph data structure.
3Reliability
If recipe data is designed for specific devices, then control reliability is improved, but adaptability to new devices deteriorates
Solution Approach 1:
The patent creates a universal graph data structure that can represent recipes independent of specific devices. By using abstract equipment nodes with unique IDs rather than device-specific instructions, the system achieves multi-functionality where the same recipe graph can control different devices performing the same culinary function, thereby improving adaptability while maintaining control reliability through consistent graph semantics.
4Ease of operation
If recipe data includes detailed natural language descriptions, then human readability is improved, but data processing complexity increases
Solution Approach 1:
The patent extracts essential semantic information from natural language descriptions and places it into structured graph nodes. By taking out only the critical elements (ingredients, actions, equipment, their relationships and parameters) and representing them in a standardized graph format, the system reduces data processing complexity while preserving human readability through the organized structure.
Data Source
AI summary
A device control system that includes a memory, and processing circuitry that acquires recipe data of a culinary dish, the recipe data being represented by a graph including a plurality of nodes and edges between the nodes, and processes the acquired recipe data for controlling a device, and the plurality of nodes include an ingredient node that represents an ingredient of the culinary dish, and a culinary dish node that represents the culinary dish, the edge represents an action that is necessary for state transition between the nodes, an ingredient ID that uniquely identifies the ingredient is assigned to the ingredient node, an action ID that uniquely identifies the action is assigned to the edge, and the processing circuitry controls the device based on the acquired recipe data.