Information processing device, information processing method, program and distribution system
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Solution Overview
Problem
Existing cooking technologies face challenges in adapting to variations in ingredient types, weights, and initial temperatures, leading to difficulties in timing and work strength, and the use of multiple sensors increases device size and cost, particularly for measuring taste, aroma, and texture.
Innovation Solution
An information processing apparatus and method that generates process data using a cooking process generation model trained on cooking data, action data, and sensor data, allowing for appropriate reproduction of cooking conditions with reduced sensor requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor values during cooking by a chef are prerecorded to reproduce cooking, then cooking quality can be maintained, but it becomes difficult to adapt to variations in ingredient types, weights, initial temperatures, and serving sizes
Solution Approach 1:
The patent transforms static prerecorded sensor values into dynamic adaptive guidance by using machine learning models that process real-time sensor data from the reproducing side. The system dynamically adjusts cooking instructions based on actual ingredient states, enabling adaptation to variations in ingredient types, weights, and initial temperatures while maintaining cooking quality.
Solution Approach 2:
The patent changes the parameter representation from fixed prerecorded sensor values to variable real-time sensor measurements. By monitoring actual temperature, time, and weight parameters during reproduction cooking and comparing them against learned patterns, the system adapts instructions to match the specific conditions of the reproducing side ingredients.
2Measurement precision
If many sensors are equipped in the reproducing device to enhance reproduction quality, then measurement precision improves, but device size and price increase
Solution Approach 1:
The patent uses machine learning models to copy and reconstruct the chef's cooking knowledge and decision-making processes. Instead of physically replicating multiple expensive sensors (taste, aroma, texture sensors) at the reproducing side, the system creates a virtual model of the chef's sensory evaluation based on data collected during the recording phase, thereby achieving high measurement precision without increasing device complexity.
Solution Approach 2:
The patent replaces physical sensor systems with computational models. The complex mechanical sensor systems (taste sensors, aroma sensors, texture sensors) are substituted with software-based machine learning models that process data from simple, inexpensive sensors (temperature, time, weight sensors), thereby maintaining measurement precision while dramatically reducing device complexity and cost.
Data Source
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AI summary
The present technology relates to an information processing apparatus, an information processing method, a program, and a distribution system that make it possible to appropriately reproduce cooking of a certain person. An information processing apparatus according to one aspect of the present technology generates process data configured to reproduce cooking according to a cooking condition, using a cooking process generation model trained on the basis of the process data of each process of the cooking, action data of a person who is cooking, and sensor data regarding a state of an ingredient. The present technology can be applied to an apparatus that assists in reproducing cooking, using a prediction model generated by learning based on data recording cooking by a professional cook.