System

Abstract

An object of a system according to an embodiment is to evaluate an ingredient in a virtual environment and efficiently accumulate data.SOLUTION: A system according to an embodiment includes a virtual environment generation unit, an evaluation unit, and a data accumulation unit. The virtual environment generation unit generates a virtual environment. The evaluator evaluates the foodstuff in the virtual environment generated by the virtual environment generator. The data-gathering unit gathers the evaluation results obtained by the evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies require testing and evaluation using real food ingredients, which can be costly and time-consuming.

[0005] The system according to the embodiment aims to evaluate ingredients in a virtual environment and efficiently collect data. [Means for solving the problem]

[0006] A system according to an embodiment includes a virtual environment generation unit, an evaluation unit, and a data collection unit. The virtual environment generation unit generates a virtual environment. The evaluation unit evaluates ingredients in the virtual environment generated by the virtual environment generation unit. The data collection unit collects the evaluation results obtained by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate ingredients in a virtual environment and efficiently collect data. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The virtual ingredient testing system according to an embodiment of the present invention is a system for testing and evaluating ingredients in a virtual environment without using real ingredients, thereby improving the efficiency of food quality evaluation and new product development processes.

[0029] A virtual ingredient testing system according to an embodiment includes a virtual environment generation unit, an evaluation unit, and a data collection unit. The virtual environment generation unit generates a virtual environment. For example, the virtual environment is constructed using 3D modeling technology. The virtual environment generation unit can also generate the virtual environment using simulation technology. For example, the virtual environment generation unit creates a detailed 3D model to realistically reproduce the appearance and texture of food. The evaluation unit evaluates ingredients in the virtual environment generated by the virtual environment generation unit. For example, the evaluation unit evaluates the appearance of the ingredients. The evaluation unit can also evaluate the texture of the ingredients. The evaluation unit can also evaluate the taste of the ingredients. For example, the evaluation unit evaluates the color and shape of the ingredients. The evaluation unit evaluates the hardness and softness of the ingredients. The evaluation unit evaluates the sweetness and bitterness of the ingredients. The data collection unit collects the evaluation results obtained by the evaluation unit. For example, the data collection unit collects the evaluation results as numerical data. The data collection unit can also collect the evaluation results as text data. The data collection unit can also collect the evaluation results as image data. For example, the data aggregator may store the evaluation results in a database, cloud storage, or local storage. This allows the virtual ingredient testing system according to the embodiment to evaluate ingredients in a virtual environment, enabling efficient testing.

[0030] The evaluation unit can use VR goggles to enter the virtual environment and evaluate the appearance, texture, and taste of ingredients. The evaluation unit, for example, combines VR goggles with a haptic feedback device to build a system that allows a user to realistically feel the hardness and elasticity of ingredients in the virtual environment. For example, when a user virtually grasps an apple, the haptic device reproduces its hardness and elasticity. The evaluation unit also uses force feedback technology to simulate the physical properties of ingredients. For example, when cutting a steak in the virtual environment, the resistance of the knife is realistically reproduced. The evaluation unit also collects physical property data of ingredients to provide haptic feedback and performs haptic simulation in the virtual environment based on that data. For example, the hardness and elasticity of different types of bread are compiled into a database and reproduced in the virtual environment. This allows realistic food evaluation in the virtual environment using VR goggles.

[0031] The evaluation unit can evaluate the nutritional value, taste, and texture of insect food ingredients in a virtual environment. For example, the evaluation unit builds a system that performs a nutritional simulation in a virtual environment based on nutritional data of insect food ingredients. For example, the protein and vitamin content of a specific insect is displayed in real time. The evaluation unit also evaluates nutritional enhancements or deficiencies in the virtual environment and uses the results to improve the insect food ingredients. For example, if a specific nutrient is lacking, the nutritional balance is adjusted by combining it with other ingredients. The evaluation unit also develops a system that simulates the nutritional components of insect food ingredients in detail and develops new insect food ingredients based on the results. For example, a recipe for a nutritious insect bar is prototyped in a virtual environment. This allows for efficient evaluation of insect food ingredients in a virtual environment.

[0032] The data collection unit can accumulate test results from the virtual environment in a database and analyze the data. For example, the data collection unit builds a system that performs detailed analysis of the molecular structure of allergens based on the test results from the virtual environment. For example, the data collection unit analyzes the molecular structure of allergens contained in specific insect food ingredients and evaluates their effects. The data collection unit also analyzes the molecular structure of allergens in detail and collects data to elucidate the mechanism of allergic reactions. For example, the data collection unit analyzes the relationship between the molecular structure of allergens and allergic reactions based on the test results from the virtual environment. The data collection unit also analyzes the molecular structure of allergens based on the test results from the virtual environment and develops a system that takes measures against allergies based on the data. For example, the data collection unit proposes measures to suppress allergic reactions based on the molecular structure of allergens. In this way, efficient data management is possible by accumulating and analyzing test results from the virtual environment in a database.

[0033] The data collection unit can identify allergens based on test results in a virtual environment for allergy prevention measures. The data collection unit, for example, develops a system that builds a risk assessment model for setting food safety standards based on the results of simulations in a virtual environment. For example, it performs a risk assessment of specific insect food ingredients and sets safety standards based on the results. The data collection unit also creates a database of simulation results in a virtual environment and analyzes the data to build the risk assessment model. For example, it performs risk assessment for different storage conditions and cooking methods. The data collection unit also builds a risk assessment model for setting food safety standards based on the results of simulations in a virtual environment and develops a system that takes appropriate measures based on the results. For example, it proposes improvements to allergy prevention measures and storage methods based on the risk assessment model. This makes it possible to identify allergens based on test results in a virtual environment, allowing allergy prevention measures to be implemented efficiently.

[0034] The evaluation department can identify areas for improvement in a product based on the test results in a virtual environment in order to streamline the new product development process. For example, the evaluation department builds a system that develops an algorithm that automatically suggests areas for improvement in a product based on the test results in a virtual environment. For example, the system automatically suggests areas for improvement in the taste or texture of a specific ingredient. The evaluation department also analyzes the test results and develops an algorithm that automatically suggests areas for improvement in a product. For example, the system identifies areas for improvement in a product based on user evaluation data. The evaluation department also develops a system that automatically suggests areas for improvement in a product based on the test results in a virtual environment. For example, the system suggests areas for improvement in a product in real time based on the test results. In this way, the new product development process is streamlined by identifying areas for improvement in a product based on the test results in a virtual environment.

[0035] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0036] The virtual environment generation unit can simulate food preservation methods within the virtual environment. For example, it can reproduce the effects of refrigerated or frozen storage in the virtual environment and evaluate changes in the quality of food. It can also find the optimal preservation method by changing the storage period and storage conditions. It can also provide advice on food preservation based on the results of the storage method simulation. This makes it possible to evaluate storage methods in the virtual environment and improve the efficiency of food quality control.

[0037] The evaluation unit can simulate cooking methods in a virtual environment and evaluate ingredients based on the results. For example, cooking methods such as baking, boiling, and steaming can be reproduced in the virtual environment to evaluate the changes in the taste and texture of ingredients. The unit can also find the optimal cooking method by changing the cooking time and temperature. Furthermore, it can provide advice on cooking ingredients based on the results of the cooking method simulation. This makes it possible to evaluate cooking methods in a virtual environment and streamline the cooking process of ingredients.

[0038] The data collection unit can perform a detailed analysis of the nutritional value of ingredients based on the test results in the virtual environment. For example, it can analyze the vitamin and mineral content of a specific ingredient and evaluate its nutritional balance based on the results. It can also compare the nutritional value of different ingredients to find the optimal combination. It can also provide advice on the nutritional value of ingredients based on the results of the nutritional value analysis. This makes it possible to evaluate nutritional value in a virtual environment, improving the efficiency of nutritional management of ingredients.

[0039] The evaluation unit can simulate the allergen characteristics of ingredients in a virtual environment and evaluate ingredients based on the results. For example, the effects of allergens contained in a specific ingredient can be reproduced in the virtual environment to evaluate the risk of an allergic reaction. It can also change the allergen content to select the most suitable ingredient. Furthermore, it can provide advice on food allergy prevention measures based on the results of the allergen characteristics simulation. This makes it possible to evaluate allergen characteristics in a virtual environment, improving the safety of ingredients.

[0040] The virtual environment generation unit can simulate the production process of ingredients in a virtual environment. For example, it can reproduce the cultivation and harvesting processes of agricultural crops in a virtual environment and evaluate production efficiency. It can also simulate different cultivation methods and harvesting times to find the optimal production process. It can also provide advice on food production based on the results of the production process simulation. This makes it possible to evaluate the production process in a virtual environment and improve food production efficiency.

[0041] The processing flow of the first embodiment will be briefly explained below.

[0042] Step 1: The virtual environment generation unit generates a virtual environment. For example, it uses 3D modeling and simulation technologies to build the virtual environment and create detailed 3D models to realistically reproduce the appearance and texture of food. Step 2: The evaluation unit evaluates the food ingredients in the virtual environment generated by the virtual environment generation unit, for example, evaluating the appearance, texture, taste, color, shape, hardness, softness, sweetness, bitterness, etc. of the food ingredients. Step 3: The data collection unit collects the evaluation results obtained by the evaluation unit. For example, the evaluation results are collected as numerical data, text data, or image data, and stored in a database, cloud storage, or local storage.

[0043] (Example 2) The virtual ingredient testing system according to an embodiment of the present invention is a system for testing and evaluating ingredients in a virtual environment without using real ingredients, thereby improving the efficiency of food quality evaluation and new product development processes.

[0044] A virtual ingredient testing system according to an embodiment includes a virtual environment generation unit, an evaluation unit, and a data collection unit. The virtual environment generation unit generates a virtual environment. For example, the virtual environment is constructed using 3D modeling technology. The virtual environment generation unit can also generate the virtual environment using simulation technology. For example, the virtual environment generation unit creates a detailed 3D model to realistically reproduce the appearance and texture of food. The evaluation unit evaluates ingredients in the virtual environment generated by the virtual environment generation unit. For example, the evaluation unit evaluates the appearance of the ingredients. The evaluation unit can also evaluate the texture of the ingredients. The evaluation unit can also evaluate the taste of the ingredients. For example, the evaluation unit evaluates the color and shape of the ingredients. The evaluation unit evaluates the hardness and softness of the ingredients. The evaluation unit evaluates the sweetness and bitterness of the ingredients. The data collection unit collects the evaluation results obtained by the evaluation unit. For example, the data collection unit collects the evaluation results as numerical data. The data collection unit can also collect the evaluation results as text data. The data collection unit can also collect the evaluation results as image data. For example, the data aggregator may store the evaluation results in a database, cloud storage, or local storage. This allows the virtual ingredient testing system according to the embodiment to evaluate ingredients in a virtual environment, enabling efficient testing.

[0045] The evaluation unit can use VR goggles to enter the virtual environment and evaluate the appearance, texture, and taste of ingredients. The evaluation unit, for example, combines VR goggles with a haptic feedback device to build a system that allows a user to realistically feel the hardness and elasticity of ingredients in the virtual environment. For example, when a user virtually grasps an apple, the haptic device reproduces its hardness and elasticity. The evaluation unit also uses force feedback technology to simulate the physical properties of ingredients. For example, when cutting a steak in the virtual environment, the resistance of the knife is realistically reproduced. The evaluation unit also collects physical property data of ingredients to provide haptic feedback and performs haptic simulation in the virtual environment based on that data. For example, the hardness and elasticity of different types of bread are compiled into a database and reproduced in the virtual environment. This allows realistic food evaluation in the virtual environment using VR goggles.

[0046] The evaluation unit can evaluate the nutritional value, taste, and texture of insect food ingredients in a virtual environment. For example, the evaluation unit builds a system that performs a nutritional simulation in a virtual environment based on nutritional data of insect food ingredients. For example, the protein and vitamin content of a specific insect is displayed in real time. The evaluation unit also evaluates nutritional enhancements or deficiencies in the virtual environment and uses the results to improve the insect food ingredients. For example, if a specific nutrient is lacking, the nutritional balance is adjusted by combining it with other ingredients. The evaluation unit also develops a system that simulates the nutritional components of insect food ingredients in detail and develops new insect food ingredients based on the results. For example, a recipe for a nutritious insect bar is prototyped in a virtual environment. This allows for efficient evaluation of insect food ingredients in a virtual environment.

[0047] The data collection unit can accumulate test results from the virtual environment in a database and analyze the data. For example, the data collection unit builds a system that performs detailed analysis of the molecular structure of allergens based on the test results from the virtual environment. For example, the data collection unit analyzes the molecular structure of allergens contained in specific insect food ingredients and evaluates their effects. The data collection unit also analyzes the molecular structure of allergens in detail and collects data to elucidate the mechanism of allergic reactions. For example, the data collection unit analyzes the relationship between the molecular structure of allergens and allergic reactions based on the test results from the virtual environment. The data collection unit also analyzes the molecular structure of allergens based on the test results from the virtual environment and develops a system that takes measures against allergies based on the data. For example, the data collection unit proposes measures to suppress allergic reactions based on the molecular structure of allergens. In this way, efficient data management is possible by accumulating and analyzing test results from the virtual environment in a database.

[0048] The data collection unit can identify allergens based on test results in a virtual environment for allergy prevention measures. The data collection unit, for example, develops a system that builds a risk assessment model for setting food safety standards based on the results of simulations in a virtual environment. For example, it performs a risk assessment of specific insect food ingredients and sets safety standards based on the results. The data collection unit also creates a database of simulation results in a virtual environment and analyzes the data to build the risk assessment model. For example, it performs risk assessment for different storage conditions and cooking methods. The data collection unit also builds a risk assessment model for setting food safety standards based on the results of simulations in a virtual environment and develops a system that takes appropriate measures based on the results. For example, it proposes improvements to allergy prevention measures and storage methods based on the risk assessment model. This makes it possible to identify allergens based on test results in a virtual environment, allowing allergy prevention measures to be implemented efficiently.

[0049] The evaluation department can identify areas for improvement in a product based on the test results in a virtual environment in order to streamline the new product development process. For example, the evaluation department builds a system that develops an algorithm that automatically suggests areas for improvement in a product based on the test results in a virtual environment. For example, the system automatically suggests areas for improvement in the taste or texture of a specific ingredient. The evaluation department also analyzes the test results and develops an algorithm that automatically suggests areas for improvement in a product. For example, the system identifies areas for improvement in a product based on user evaluation data. The evaluation department also develops a system that automatically suggests areas for improvement in a product based on the test results in a virtual environment. For example, the system suggests areas for improvement in a product in real time based on the test results. In this way, the new product development process is streamlined by identifying areas for improvement in a product based on the test results in a virtual environment.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The evaluation unit can estimate the user's emotions and adjust the evaluation results of ingredients based on the estimated user emotions. For example, if the user expresses positive emotions toward the appearance of an ingredient, the evaluation result can be increased. On the other hand, if the user expresses negative emotions toward the texture of an ingredient, the evaluation result can be decreased. Furthermore, the evaluation unit can accumulate user emotion data and predict evaluation results based on past emotion data. This allows for more personalized evaluations based on the user's emotions.

[0052] The virtual environment generation unit can estimate the user's emotions and adjust the virtual environment based on the estimated emotions. For example, if the user expresses a relaxed emotion, the virtual environment's color tones and music can be changed to more relaxing ones. Alternatively, if the user expresses an excited emotion, the virtual environment's color tones and music can be changed to more lively ones. Furthermore, the virtual environment generation unit can accumulate user emotion data and predictively adjust the virtual environment based on past emotion data. This allows the virtual environment to be optimally tailored to the user's emotions.

[0053] The data aggregation unit can collect user emotion data and analyze the evaluation results of ingredients based on that data. For example, if a user expresses positive emotion toward a particular ingredient, the evaluation result of that ingredient can be increased. Conversely, if a user expresses negative emotion toward a particular ingredient, the evaluation result of that ingredient can be decreased. Furthermore, by accumulating user emotion data, it is possible to predict the evaluation results based on past emotion data. This allows for more accurate evaluation based on the user's emotions.

[0054] The evaluation unit can estimate the user's emotions and suggest improvements to ingredients based on the estimated emotions. For example, if the user expresses positive emotions toward a particular ingredient, the improvement points for that ingredient can be reduced. Conversely, if the user expresses negative emotions toward a particular ingredient, the improvement points for that ingredient can be increased. Furthermore, the evaluation unit can accumulate user emotion data and predict improvements based on past emotion data. This allows for more effective improvements based on the user's emotions.

[0055] The virtual environment generation unit can estimate the user's emotions and adjust the appearance and texture of the virtual ingredients based on the estimated emotions. For example, if the user expresses positive emotions toward the appearance of a particular ingredient, the appearance can be emphasized. Alternatively, if the user expresses negative emotions toward the texture of a particular ingredient, the texture can be changed. Furthermore, the virtual environment generation unit can accumulate user emotion data and predictively adjust the appearance and texture of the virtual ingredients based on past emotion data. This allows the system to provide optimal virtual ingredients that match the user's emotions.

[0056] The virtual environment generation unit can simulate food preservation methods within the virtual environment. For example, it can reproduce the effects of refrigerated or frozen storage in the virtual environment and evaluate changes in the quality of food. It can also find the optimal preservation method by changing the storage period and storage conditions. It can also provide advice on food preservation based on the results of the storage method simulation. This makes it possible to evaluate storage methods in the virtual environment and improve the efficiency of food quality control.

[0057] The evaluation unit can simulate cooking methods in a virtual environment and evaluate ingredients based on the results. For example, cooking methods such as baking, boiling, and steaming can be reproduced in the virtual environment to evaluate the changes in the taste and texture of ingredients. The unit can also find the optimal cooking method by changing the cooking time and temperature. Furthermore, it can provide advice on cooking ingredients based on the results of the cooking method simulation. This makes it possible to evaluate cooking methods in a virtual environment and streamline the cooking process of ingredients.

[0058] The data collection unit can perform a detailed analysis of the nutritional value of ingredients based on the test results in the virtual environment. For example, it can analyze the vitamin and mineral content of a specific ingredient and evaluate its nutritional balance based on the results. It can also compare the nutritional value of different ingredients to find the optimal combination. It can also provide advice on the nutritional value of ingredients based on the results of the nutritional value analysis. This makes it possible to evaluate nutritional value in a virtual environment, improving the efficiency of nutritional management of ingredients.

[0059] The evaluation unit can simulate the allergen characteristics of ingredients in a virtual environment and evaluate ingredients based on the results. For example, the effects of allergens contained in a specific ingredient can be reproduced in the virtual environment to evaluate the risk of an allergic reaction. It can also change the allergen content to select the most suitable ingredient. Furthermore, it can provide advice on food allergy prevention measures based on the results of the allergen characteristics simulation. This makes it possible to evaluate allergen characteristics in a virtual environment, improving the safety of ingredients.

[0060] The virtual environment generation unit can simulate the production process of ingredients in a virtual environment. For example, it can reproduce the cultivation and harvesting processes of agricultural crops in a virtual environment and evaluate production efficiency. It can also simulate different cultivation methods and harvesting times to find the optimal production process. It can also provide advice on food production based on the results of the production process simulation. This makes it possible to evaluate the production process in a virtual environment and improve food production efficiency.

[0061] The processing flow of the second embodiment will be briefly explained below.

[0062] Step 1: The virtual environment generation unit generates a virtual environment. For example, it uses 3D modeling and simulation technologies to build the virtual environment and create detailed 3D models to realistically reproduce the appearance and texture of food. Step 2: The evaluation unit evaluates the food ingredients in the virtual environment generated by the virtual environment generation unit, for example, evaluating the appearance, texture, taste, color, shape, hardness, softness, sweetness, bitterness, etc. of the food ingredients. Step 3: The data collection unit collects the evaluation results obtained by the evaluation unit. For example, the evaluation results are collected as numerical data, text data, or image data, and stored in a database, cloud storage, or local storage.

[0063] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0064] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0065] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0066] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0067] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0068] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0069] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0070] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0071] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0072] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0073] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0074] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0075] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0076] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0077] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0078] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0079] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0080] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0081] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0082] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0083] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0084] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0085] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0086] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0087] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0088] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0089] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0090] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0091] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0092] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0093] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0096] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0097] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0104] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0113] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0114] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0115] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0116] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0117] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0118] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0119] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0120] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0121] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0122] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0123] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0124] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0125] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0126] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0127] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0128] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0129] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0130] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a virtual environment generation unit that generates a virtual environment; an evaluation unit that evaluates ingredients in the virtual environment generated by the virtual environment generation unit; a data accumulation unit that accumulates the evaluation results obtained by the evaluation unit. A system characterized by:

2. The evaluation unit Enter the virtual environment using VR goggles and evaluate the appearance, texture, and taste of the food.

2. The system of claim 1.

3. The evaluation unit Evaluating the nutritional value, taste, and texture of insect food ingredients in the virtual environment 2. The system of claim 1.

4. The data accumulation unit The test results in the virtual environment are collected in a database and analyzed.

2. The system of claim 1.

5. The data accumulation unit To combat allergies, allergens are identified based on the test results in the virtual environment.

2. The system of claim 1.

Images