System

Abstract

An object of a system according to an embodiment is to propose a recipe with well-balanced nutrition in a short time by effectively utilizing ingredients in a refrigerator.SOLUTION: A system includes a food ingredients management part, a recipe proposal part, a nutrition balance evaluation part, and a virtual refrigerator creation part. The food ingredient management unit manages food ingredients. The recipe suggesting unit suggests a recipe by inputting an image of what the user wants to eat. The nutrition balance evaluation unit evaluates the nutrition balance of the proposed recipe. The virtual refrigerator creation unit creates a virtual refrigerator.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 technology has the problem that it is difficult to effectively use ingredients in the refrigerator and quickly suggest nutritionally balanced recipes.

[0005] The system according to the embodiment aims to effectively utilize ingredients in the refrigerator and quickly propose nutritionally balanced recipes. [Means for solving the problem]

[0006] The system according to the embodiment includes an ingredient management unit, a recipe suggestion unit, a nutritional balance evaluation unit, and a virtual refrigerator creation unit. The ingredient management unit manages ingredients. The recipe suggestion unit suggests recipes based on the user's input of an image of what they would like to eat. The nutritional balance evaluation unit evaluates the nutritional balance of the suggested recipes. The virtual refrigerator creation unit creates a virtual refrigerator. [Effects of the Invention]

[0007] The system according to the embodiment can effectively utilize ingredients in the refrigerator and quickly suggest nutritionally balanced recipes. [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 recipe suggestion system according to an embodiment of the present invention uses a generative AI that can quickly create nutritionally balanced recipes using ingredients available in the refrigerator. The system allows users to simply input an image of what they want to eat at the time, and the generative AI records their daily meals and preferences, allowing it to suggest nutritionally balanced recipes. Furthermore, by creating a virtual refrigerator and making effective use of ingredients available at home, the system also contributes to solving the food waste problem.

[0029] A recipe suggestion system according to an embodiment includes an ingredient management unit, a recipe suggestion unit, a nutritional balance evaluation unit, and a virtual refrigerator creation unit. The ingredient management unit manages ingredients. For example, when a user registers ingredients in the refrigerator, the ingredient management unit records the information and updates the virtual refrigerator. The ingredient management unit can also manage expiration dates of ingredients and suggest recipes that prioritize ingredients that should be consumed sooner. The recipe suggestion unit suggests recipes based on a user's input of a desired meal. For example, if a user inputs, "I want to eat curry today," the recipe suggestion unit suggests a curry recipe using ingredients in the virtual refrigerator based on that information. The nutritional balance evaluation unit evaluates the nutritional balance of the suggested recipe. For example, the nutritional balance evaluation unit evaluates recipes that contain a balanced amount of nutrients, such as protein, carbohydrates, vitamins, and minerals. The virtual refrigerator creation unit creates a virtual refrigerator. For example, the virtual refrigerator creation unit supports a user in registering ingredients in their home in the virtual refrigerator using Yahoo! Mart. This allows the recipe suggestion system according to an embodiment to quickly suggest nutritionally balanced recipes using ingredients in the refrigerator.

[0030] When a user registers an ingredient, the ingredient management unit automatically registers it in the virtual refrigerator by scanning its barcode and can obtain detailed information about the ingredient. For example, when a user scans an ingredient's barcode using a smartphone camera, the ingredient management unit automatically registers the information in the virtual refrigerator. For example, detailed information such as the ingredient's name, expiration date, and nutritional information is obtained from the barcode and reflected in the virtual refrigerator. When the ingredient management unit scans an ingredient's barcode, the generation AI obtains detailed information about the ingredient from an online database and registers it in the virtual refrigerator. For example, detailed information including the ingredient's place of origin and producer is recorded. Furthermore, when a user simply scans a barcode, the ingredient management unit automatically manages ingredient inventory and updates the virtual refrigerator. For example, the unit tracks ingredient usage and expiration dates in real time and notifies the user. This simplifies ingredient registration and enables automatic acquisition of detailed information.

[0031] The ingredient management unit can detect the freshness of ingredients using sensors installed inside the refrigerator and update that information to the virtual refrigerator in real time. For example, the ingredient management unit uses sensors installed inside the refrigerator to detect the freshness of ingredients and send that information to the generation AI. For example, it measures temperature, humidity, gas components, etc. to evaluate the freshness of ingredients. The ingredient management unit also uses the generation AI to update the virtual refrigerator in real time based on the freshness information detected by the sensors. For example, it can suggest recipes that prioritize the use of ingredients that have lost their freshness. The ingredient management unit also uses sensors to detect ingredient freshness information, and the generation AI can build a system that notifies the user. For example, it can send an alert to encourage the user to consume ingredients that have lost their freshness as soon as possible. This allows ingredient freshness information to be managed in real time.

[0032] The recipe suggestion unit analyzes the user's voice input to more specifically grasp the image of what they want to eat. For example, when a user vocally inputs, "I want curry today," the generation AI analyzes the voice and grasps the specific type of curry and their ingredient preferences. For example, it obtains a detailed image such as "spicy chicken curry." In addition, when analyzing the voice input, the generation AI considers the user's past eating history and preferences to concretely grasp the image of what they want to eat. For example, it reflects a request such as "I want the curry to be as spicy as the last time." In addition, the recipe suggestion unit analyzes the user's voice input and builds a system to specifically grasp the image of what they want to eat. For example, it uses voice recognition technology to analyze the user's utterances in detail and reflects them in specific recipe suggestions. This makes it easier to grasp the user's specific image of what they want to eat.

[0033] The nutritional balance evaluation unit can suggest individually optimized recipes based on the user's health condition and allergy information. For example, the nutritional balance evaluation unit registers the user's health condition and allergy information in the generation AI and suggests individually optimized nutritionally balanced recipes based on that information. For example, it can provide recipes that avoid ingredients that cause allergies. The nutritional balance evaluation unit also builds a system in which the generation AI suggests nutritionally balanced recipes based on the user's health condition and allergy information. For example, it can provide recipes that are fortified with specific nutrients. The nutritional balance evaluation unit also suggests individually optimized nutritionally balanced recipes based on the user's health condition and allergy information. For example, it can provide low-carbohydrate recipes for diabetics and low-salt recipes for high blood pressure patients. This makes it possible to suggest recipes that suit the user's health condition and allergy information.

[0034] The virtual refrigerator creation unit can suggest exchanging or sharing ingredients with other users based on the ingredients registered in the virtual refrigerator. The virtual refrigerator creation unit, for example, builds a system that suggests exchanging or sharing ingredients with nearby users based on ingredient information registered in the virtual refrigerator. For example, a function is added to provide surplus ingredients to other users. The virtual refrigerator creation unit also develops an ingredient sharing platform and shares the ingredients registered in the virtual refrigerator with other users. For example, a function is provided to promote ingredient exchanges and joint purchases. The virtual refrigerator creation unit also suggests exchanging or sharing ingredients with other users based on the data in the virtual refrigerator. For example, a matching function is provided to exchange ingredients with nearby users. This promotes effective use of ingredients and contributes to reducing food waste.

[0035] The virtual refrigerator creation unit can provide an option to directly purchase fresh ingredients from local farms and markets based on the data of the virtual refrigerator. The virtual refrigerator creation unit, for example, builds a system that provides an option to directly purchase fresh ingredients from local farms and markets based on the data of the virtual refrigerator. For example, a function to automatically order ingredients that are in short supply in the virtual refrigerator is added. The virtual refrigerator creation unit also links with local farms and markets to provide an option to directly purchase fresh ingredients based on the data of the virtual refrigerator. For example, a function to preferentially suggest local agricultural products is added. The virtual refrigerator creation unit also provides an option to directly purchase fresh ingredients from local farms and markets based on the data of the virtual refrigerator. For example, an interface is provided that allows the user to easily order fresh local ingredients. This allows fresh ingredients to be directly purchased from local farms and markets.

[0036] The recipe suggestion unit can predict and suggest what the user wants to eat based on their past eating history. For example, the recipe suggestion unit analyzes the user's past eating history and the generation AI predicts what the user wants to eat. For example, it suggests what the user wants to eat based on dishes they have eaten frequently in the past and their favorite ingredients. The recipe suggestion unit also builds a system in which the generation AI learns the user's preferences based on the past eating history and predicts what the user wants to eat. For example, it suggests dishes that are popular on specific days of the week or in specific seasons. The recipe suggestion unit also predicts and suggests what the generation AI wants to eat based on the user's past eating history. For example, it suggests recipes that reflect the user's preferred seasonings and cooking methods based on past data. This makes it easier to predict what the user wants to eat based on the user's past eating history.

[0037] The nutritional balance evaluation unit can generate and suggest nutritionally balanced recipes according to the season and weather. The nutritional balance evaluation unit builds a system that generates and suggests nutritionally balanced recipes according to the season and weather. For example, in the summer, it would provide recipes that emphasize cold dishes and hydration. The nutritional balance evaluation unit also analyzes seasonal and weather data using a generation AI to suggest nutritionally balanced recipes according to the season and weather. For example, in the winter, it would provide recipes that include hot soups and high levels of vitamin C. The nutritional balance evaluation unit also generates and suggests nutritionally balanced recipes according to the season and weather. For example, on rainy days, it would provide recipes that use ingredients that boost the immune system. This makes it possible to suggest nutritionally balanced recipes according to the season and weather.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The recipe suggestion system can predict and suggest what a user wants to eat based on the user's past eating history. For example, it can suggest what the user wants to eat based on dishes they have frequently eaten in the past or their favorite ingredients. It can also suggest dishes that are popular on specific days of the week or in specific seasons based on the past eating history. It can also suggest recipes that reflect the user's preferred seasonings and cooking methods based on past data. This makes it easier to predict what a user wants to eat based on the user's past eating history.

[0040] The recipe recommendation system can generate and suggest nutritionally balanced recipes according to the season and weather. For example, in summer, it can suggest recipes that emphasize cold dishes and hydration. In winter, it can suggest hot soups and recipes that are rich in vitamin C. Furthermore, it can suggest recipes that use ingredients that boost the immune system on rainy days. This allows it to suggest nutritionally balanced recipes according to the season and weather.

[0041] The recipe suggestion system can suggest individually optimized recipes based on the user's health condition and allergy information. For example, it can provide recipes that avoid ingredients that cause allergies. It can also provide recipes that are fortified with specific nutrients. It can also provide low-sugar recipes for diabetics and low-salt recipes for high blood pressure patients. This makes it possible to suggest recipes that suit the user's health condition and allergy information.

[0042] The recipe suggestion system can suggest exchanging or sharing ingredients with other users based on the ingredients registered in the virtual refrigerator. For example, we will build a system that suggests exchanging or sharing ingredients with nearby users based on the ingredient information registered in the virtual refrigerator. Furthermore, we will develop an ingredient sharing platform that allows ingredients registered in the virtual refrigerator to be shared with other users. This will promote the effective use of ingredients and contribute to reducing food waste.

[0043] The recipe suggestion system can provide the option to purchase fresh ingredients directly from local farmers and markets based on the data from the virtual refrigerator. For example, we will build a system that provides the option to purchase fresh ingredients directly from local farmers and markets based on the data from the virtual refrigerator. Furthermore, it is possible to link with local farms and markets and provide the option to purchase fresh ingredients directly based on the data from the virtual refrigerator. This allows fresh ingredients to be purchased directly from local farms and markets.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The ingredient management unit manages ingredients. For example, when a user registers ingredients in their refrigerator, the information is recorded and the virtual refrigerator is updated. The ingredient management unit also manages the expiration dates of ingredients and can suggest recipes that prioritize ingredients that should be consumed early. Step 2: The recipe suggestion unit suggests recipes based on the user's input of what they want to eat. For example, if the user inputs "I want to eat curry today," the recipe suggestion unit will use that information to suggest a curry recipe using ingredients in the virtual refrigerator. Step 3: The nutritional balance evaluation unit evaluates the nutritional balance of the proposed recipe. For example, the nutritional balance evaluation unit evaluates recipes that contain a balanced amount of nutrients such as proteins, carbohydrates, vitamins, and minerals. Step 4: The virtual refrigerator creation unit creates a virtual refrigerator. For example, the virtual refrigerator creation unit supports the user in registering ingredients at home in the virtual refrigerator using Yahoo! Mart.

[0046] (Example 2) The recipe suggestion system according to an embodiment of the present invention uses a generative AI that can quickly create nutritionally balanced recipes using ingredients available in the refrigerator. The system allows users to simply input an image of what they want to eat at the time, and the generative AI records their daily meals and preferences, allowing it to suggest nutritionally balanced recipes. Furthermore, by creating a virtual refrigerator and making effective use of ingredients available at home, the system also contributes to solving the food waste problem.

[0047] A recipe suggestion system according to an embodiment includes an ingredient management unit, a recipe suggestion unit, a nutritional balance evaluation unit, and a virtual refrigerator creation unit. The ingredient management unit manages ingredients. For example, when a user registers ingredients in the refrigerator, the ingredient management unit records the information and updates the virtual refrigerator. The ingredient management unit can also manage expiration dates of ingredients and suggest recipes that prioritize ingredients that should be consumed sooner. The recipe suggestion unit suggests recipes based on a user's input of a desired meal. For example, if a user inputs, "I want to eat curry today," the recipe suggestion unit suggests a curry recipe using ingredients in the virtual refrigerator based on that information. The nutritional balance evaluation unit evaluates the nutritional balance of the suggested recipe. For example, the nutritional balance evaluation unit evaluates recipes that contain a balanced amount of nutrients, such as protein, carbohydrates, vitamins, and minerals. The virtual refrigerator creation unit creates a virtual refrigerator. For example, the virtual refrigerator creation unit supports a user in registering ingredients in their home in the virtual refrigerator using Yahoo! Mart. This allows the recipe suggestion system according to an embodiment to quickly suggest nutritionally balanced recipes using ingredients in the refrigerator.

[0048] When a user registers an ingredient, the ingredient management unit automatically registers it in the virtual refrigerator by scanning its barcode and can obtain detailed information about the ingredient. For example, when a user scans an ingredient's barcode using a smartphone camera, the ingredient management unit automatically registers the information in the virtual refrigerator. For example, detailed information such as the ingredient's name, expiration date, and nutritional information is obtained from the barcode and reflected in the virtual refrigerator. When the ingredient management unit scans an ingredient's barcode, the generation AI obtains detailed information about the ingredient from an online database and registers it in the virtual refrigerator. For example, detailed information including the ingredient's place of origin and producer is recorded. Furthermore, when a user simply scans a barcode, the ingredient management unit automatically manages ingredient inventory and updates the virtual refrigerator. For example, the unit tracks ingredient usage and expiration dates in real time and notifies the user. This simplifies ingredient registration and enables automatic acquisition of detailed information.

[0049] The ingredient management unit can detect the freshness of ingredients using sensors installed inside the refrigerator and update that information to the virtual refrigerator in real time. For example, the ingredient management unit uses sensors installed inside the refrigerator to detect the freshness of ingredients and send that information to the generation AI. For example, it measures temperature, humidity, gas components, etc. to evaluate the freshness of ingredients. The ingredient management unit also uses the generation AI to update the virtual refrigerator in real time based on the freshness information detected by the sensors. For example, it can suggest recipes that prioritize the use of ingredients that have lost their freshness. The ingredient management unit also uses sensors to detect ingredient freshness information, and the generation AI can build a system that notifies the user. For example, it can send an alert to encourage the user to consume ingredients that have lost their freshness as soon as possible. This allows ingredient freshness information to be managed in real time.

[0050] The recipe suggestion unit analyzes the user's voice input to more specifically grasp the image of what they want to eat. For example, when a user vocally inputs, "I want curry today," the generation AI analyzes the voice and grasps the specific type of curry and their ingredient preferences. For example, it obtains a detailed image such as "spicy chicken curry." In addition, when analyzing the voice input, the generation AI considers the user's past eating history and preferences to concretely grasp the image of what they want to eat. For example, it reflects a request such as "I want the curry to be as spicy as the last time." In addition, the recipe suggestion unit analyzes the user's voice input and builds a system to specifically grasp the image of what they want to eat. For example, it uses voice recognition technology to analyze the user's utterances in detail and reflects them in specific recipe suggestions. This makes it easier to grasp the user's specific image of what they want to eat.

[0051] The nutritional balance evaluation unit can suggest individually optimized recipes based on the user's health condition and allergy information. For example, the nutritional balance evaluation unit registers the user's health condition and allergy information in the generation AI and suggests individually optimized nutritionally balanced recipes based on that information. For example, it can provide recipes that avoid ingredients that cause allergies. The nutritional balance evaluation unit also builds a system in which the generation AI suggests nutritionally balanced recipes based on the user's health condition and allergy information. For example, it can provide recipes that are fortified with specific nutrients. The nutritional balance evaluation unit also suggests individually optimized nutritionally balanced recipes based on the user's health condition and allergy information. For example, it can provide low-carbohydrate recipes for diabetics and low-salt recipes for high blood pressure patients. This makes it possible to suggest recipes that suit the user's health condition and allergy information.

[0052] The virtual refrigerator creation unit can suggest exchanging or sharing ingredients with other users based on the ingredients registered in the virtual refrigerator. The virtual refrigerator creation unit, for example, builds a system that suggests exchanging or sharing ingredients with nearby users based on ingredient information registered in the virtual refrigerator. For example, a function is added to provide surplus ingredients to other users. The virtual refrigerator creation unit also develops an ingredient sharing platform and shares the ingredients registered in the virtual refrigerator with other users. For example, a function is provided to promote ingredient exchanges and joint purchases. The virtual refrigerator creation unit also suggests exchanging or sharing ingredients with other users based on the data in the virtual refrigerator. For example, a matching function is provided to exchange ingredients with nearby users. This promotes effective use of ingredients and contributes to reducing food waste.

[0053] The virtual refrigerator creation unit can provide an option to directly purchase fresh ingredients from local farms and markets based on the data of the virtual refrigerator. The virtual refrigerator creation unit, for example, builds a system that provides an option to directly purchase fresh ingredients from local farms and markets based on the data of the virtual refrigerator. For example, a function to automatically order ingredients that are in short supply in the virtual refrigerator is added. The virtual refrigerator creation unit also links with local farms and markets to provide an option to directly purchase fresh ingredients based on the data of the virtual refrigerator. For example, a function to preferentially suggest local agricultural products is added. The virtual refrigerator creation unit also provides an option to directly purchase fresh ingredients from local farms and markets based on the data of the virtual refrigerator. For example, an interface is provided that allows the user to easily order fresh local ingredients. This allows fresh ingredients to be directly purchased from local farms and markets.

[0054] The recipe suggestion unit can predict and suggest what the user wants to eat based on their past eating history. For example, the recipe suggestion unit analyzes the user's past eating history and the generation AI predicts what the user wants to eat. For example, it suggests what the user wants to eat based on dishes they have eaten frequently in the past and their favorite ingredients. The recipe suggestion unit also builds a system in which the generation AI learns the user's preferences based on the past eating history and predicts what the user wants to eat. For example, it suggests dishes that are popular on specific days of the week or in specific seasons. The recipe suggestion unit also predicts and suggests what the generation AI wants to eat based on the user's past eating history. For example, it suggests recipes that reflect the user's preferred seasonings and cooking methods based on past data. This makes it easier to predict what the user wants to eat based on the user's past eating history.

[0055] The nutritional balance evaluation unit can generate and suggest nutritionally balanced recipes according to the season and weather. The nutritional balance evaluation unit builds a system that generates and suggests nutritionally balanced recipes according to the season and weather. For example, in the summer, it would provide recipes that emphasize cold dishes and hydration. The nutritional balance evaluation unit also analyzes seasonal and weather data using a generation AI to suggest nutritionally balanced recipes according to the season and weather. For example, in the winter, it would provide recipes that include hot soups and high levels of vitamin C. The nutritional balance evaluation unit also generates and suggests nutritionally balanced recipes according to the season and weather. For example, on rainy days, it would provide recipes that use ingredients that boost the immune system. This makes it possible to suggest nutritionally balanced recipes according to the season and weather.

[0056] The virtual refrigerator creation unit can use the emotion estimation function to analyze the emotions expressed by the user when registering ingredients and provide an interface for eliciting positive emotions. For example, when a user registers ingredients, the virtual refrigerator creation unit uses a generation AI to analyze facial expressions and voice to estimate emotions. For example, if the positive emotions are strong, an encouraging message is displayed. The virtual refrigerator creation unit can also use the emotion estimation function to analyze the emotions expressed by the user when registering ingredients and provide an interface for eliciting positive emotions. For example, it can incorporate game elements to allow users to enjoy registering ingredients. The virtual refrigerator creation unit can also provide real-time feedback based on emotion estimation data when the user registers ingredients, providing advice to strengthen positive emotions. For example, it can display appropriate encouragement or praise based on the input content. This makes it possible to elicit positive emotions when the user registers ingredients.

[0057] The recipe suggestion unit uses the emotion estimation function to analyze the emotions felt when a user inputs what they want to eat and can suggest recipes that elicit positive emotions. For example, when a user inputs what they want to eat, the recipe suggestion unit uses a generation AI to analyze facial expressions and voice to estimate emotions. For example, if positive emotions are strong, the recipe suggestion unit suggests recipes that further elicit those emotions. The recipe suggestion unit also uses the emotion estimation function to analyze the emotions felt when a user inputs what they want to eat and suggests recipes that elicit positive emotions. For example, it provides recipes that users can enjoy cooking. The recipe suggestion unit also provides real-time feedback based on emotion estimation data when a user inputs what they want to eat and suggests recipes that enhance positive emotions. For example, it displays appropriate encouragement or praise based on the input content. This makes it possible to elicit positive emotions when a user inputs what they want to eat.

[0058] The nutritional balance evaluation unit can use the emotion estimation function to suggest nutritionally balanced recipes according to the user's emotional state. For example, the nutritional balance evaluation unit analyzes the user's emotional state using the emotion estimation function and suggests nutritionally balanced recipes based on the results. For example, when stress is high, it provides recipes using ingredients that have a relaxing effect. The nutritional balance evaluation unit also uses the emotion estimation function to suggest nutritionally balanced recipes according to the user's emotional state. For example, it provides recipes using ingredients that elicit positive emotions. The nutritional balance evaluation unit also analyzes the user's emotional state in real time and builds a system that suggests nutritionally balanced recipes based on the results. For example, it provides recipes that contain appropriate nutrients according to the emotional state. This makes it possible to suggest nutritionally balanced recipes according to the user's emotional state.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The recipe suggestion system can estimate a user's emotions and suggest recipes based on those emotions. For example, if a user is feeling stressed, it can suggest recipes that use ingredients with a relaxing effect. Also, if a user is feeling positive, it can suggest fun recipes that further enhance those emotions. Furthermore, it is possible to provide recipes that take nutritional balance into consideration according to the user's emotional state. For example, by providing recipes that contain appropriate nutrients according to the user's emotional state, it is possible to support the user's health.

[0061] The recipe suggestion system can predict and suggest what a user wants to eat based on the user's past eating history. For example, it can suggest what the user wants to eat based on dishes they have frequently eaten in the past or their favorite ingredients. It can also suggest dishes that are popular on specific days of the week or in specific seasons based on the past eating history. It can also suggest recipes that reflect the user's preferred seasonings and cooking methods based on past data. This makes it easier to predict what a user wants to eat based on the user's past eating history.

[0062] The recipe recommendation system can generate and suggest nutritionally balanced recipes according to the season and weather. For example, in summer, it can suggest recipes that emphasize cold dishes and hydration. In winter, it can suggest hot soups and recipes that are rich in vitamin C. Furthermore, it can suggest recipes that use ingredients that boost the immune system on rainy days. This allows it to suggest nutritionally balanced recipes according to the season and weather.

[0063] The recipe suggestion system can suggest individually optimized recipes based on the user's health condition and allergy information. For example, it can provide recipes that avoid ingredients that cause allergies. It can also provide recipes that are fortified with specific nutrients. It can also provide low-sugar recipes for diabetics and low-salt recipes for high blood pressure patients. This makes it possible to suggest recipes that suit the user's health condition and allergy information.

[0064] The recipe suggestion system can suggest exchanging or sharing ingredients with other users based on the ingredients registered in the virtual refrigerator. For example, we will build a system that suggests exchanging or sharing ingredients with nearby users based on the ingredient information registered in the virtual refrigerator. Furthermore, we will develop an ingredient sharing platform that allows ingredients registered in the virtual refrigerator to be shared with other users. This will promote the effective use of ingredients and contribute to reducing food waste.

[0065] The recipe suggestion system can use the emotion estimation function to analyze the emotions expressed when a user registers ingredients and provide an interface that elicits positive emotions. For example, when a user registers ingredients, the generation AI analyzes their facial expressions and voice to estimate their emotions. If the emotion is strong, an encouraging message can be displayed. It is also possible to incorporate game elements that allow users to enjoy registering ingredients. This can elicit positive emotions when users register ingredients.

[0066] The recipe suggestion system uses emotion estimation to analyze the emotions expressed when a user inputs what they want to eat, and can suggest recipes that elicit positive emotions. For example, when a user inputs what they want to eat, the generative AI analyzes their facial expressions and voice to estimate their emotions. If the positive emotions are strong, it can suggest recipes that further elicit those emotions. It is also possible to provide recipes that users will enjoy cooking. This allows positive emotions to be elicited when users input what they want to eat.

[0067] The recipe suggestion system can use the emotion estimation function to suggest nutritionally balanced recipes according to the user's emotional state. For example, the emotion estimation function can analyze the user's emotional state and suggest nutritionally balanced recipes based on the results. If the user is under high stress, it can suggest recipes that use ingredients that have a relaxing effect. It is also possible to suggest recipes that use ingredients that elicit positive emotions. This makes it possible to suggest nutritionally balanced recipes according to the user's emotional state.

[0068] The recipe suggestion system can use the emotion estimation function to analyze the emotions expressed when a user registers ingredients and provide an interface that elicits positive emotions. For example, when a user registers ingredients, the generation AI analyzes their facial expressions and voice to estimate their emotions. If the emotion is strong, an encouraging message can be displayed. It is also possible to incorporate game elements that allow users to enjoy registering ingredients. This can elicit positive emotions when users register ingredients.

[0069] The recipe suggestion system can provide the option to purchase fresh ingredients directly from local farmers and markets based on the data from the virtual refrigerator. For example, we will build a system that provides the option to purchase fresh ingredients directly from local farmers and markets based on the data from the virtual refrigerator. Furthermore, it is possible to link with local farms and markets and provide the option to purchase fresh ingredients directly based on the data from the virtual refrigerator. This allows fresh ingredients to be purchased directly from local farms and markets.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The ingredient management unit manages ingredients. For example, when a user registers ingredients in their refrigerator, the information is recorded and the virtual refrigerator is updated. The ingredient management unit also manages the expiration dates of ingredients and can suggest recipes that prioritize ingredients that should be consumed early. Step 2: The recipe suggestion unit suggests recipes based on the user's input of what they want to eat. For example, if the user inputs "I want to eat curry today," the recipe suggestion unit will use that information to suggest a curry recipe using ingredients in the virtual refrigerator. Step 3: The nutritional balance evaluation unit evaluates the nutritional balance of the proposed recipe. For example, the nutritional balance evaluation unit evaluates recipes that contain a balanced amount of nutrients such as proteins, carbohydrates, vitamins, and minerals. Step 4: The virtual refrigerator creation unit creates a virtual refrigerator. For example, the virtual refrigerator creation unit supports the user in registering ingredients at home in the virtual refrigerator using Yahoo! Mart.

[0072] 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.

[0073] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0074] 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.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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).

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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 AI 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.

[0089] 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.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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 AI 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.

[0104] 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.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 7, a 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] 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 AI 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0126] 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."

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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]

[0139] 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 food management department that manages food ingredients; a recipe suggestion unit that suggests recipes based on the user's input of an image of what they want to eat; a nutritional balance evaluation unit that evaluates the nutritional balance of the proposed recipe; a virtual refrigerator creation unit that creates a virtual refrigerator A system characterized by:

2. The ingredient management unit When a user registers an ingredient, the barcode is scanned to automatically register it in the virtual refrigerator and obtain detailed information about the ingredient.

2. The system of claim 1.

3. The ingredient management unit A sensor installed inside the refrigerator detects the freshness of ingredients and updates the information to the virtual refrigerator in real time.

2. The system of claim 1.

4. The recipe suggestion unit Analyze the user's voice input to get a more specific idea of ​​what they want to eat.

2. The system of claim 1.

5. The nutritional balance evaluation unit Proposes individually optimized recipes based on the user's health condition and allergy information 2. The system of claim 1.

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