System

Abstract

An object of a system according to an embodiment is to propose an optimal meal based on a preference or a health condition of a user and to deliver the meal by home delivery.SOLUTION: A system includes a user information collection part, an analysis part, a proposal part, and a home delivery part. The user information collection unit collects information related to a user's preference and health condition. The analysis unit analyzes the information collected by the user information collection unit. The suggestion unit suggests an optimal meal based on the information analyzed by the analysis unit. The home delivery unit delivers the meal proposed by the proposal unit to the user.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 do not adequately suggest and deliver optimal meals based on a user's preferences and health status, and there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal meals based on the user's preferences and health condition and deliver them to their home. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information collection unit, an analysis unit, a suggestion unit, and a delivery unit. The user information collection unit collects information related to a user's preferences and health condition. The analysis unit analyzes the information collected by the user information collection unit. The suggestion unit suggests an optimal meal based on the information analyzed by the analysis unit. The delivery unit delivers the meal suggested by the suggestion unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can suggest optimal meals based on the user's preferences and health condition and deliver them to their home. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 food experience providing system according to the embodiment of the present invention is a system that proposes and delivers optimal meals based on the user's preferences and health condition. As a result, the food experience providing system can provide optimal meals to the user and support health management and the enjoyment of meals.

[0029] A food experience providing system according to an embodiment includes a user information collection unit, an analysis unit, a proposal unit, and a delivery unit. The user information collection unit collects information about a user's preferences and health status. For example, the user inputs their favorite ingredients and dishes, allergy information, and health goals (e.g., dieting, muscle building). The user information collection unit can also collect data from a health device (e.g., a smartwatch). For example, the user's exercise volume and sleep data can be used to provide more accurate meal proposals. The analysis unit analyzes the collected information. For example, the analysis unit analyzes the user's dietary needs using statistical analysis or machine learning algorithms. The analysis unit also generates a meal plan based on the user's preferences and health status using a generation AI (e.g., a text generation AI or a multimodal generation AI). The proposal unit proposes optimal meals based on the information analyzed by the analysis unit. For example, if a user is dieting, the proposal unit proposes low-calorie, nutritionally balanced meals. If a user prefers a specific ingredient, the proposal unit proposes recipes using that ingredient. The delivery unit delivers the meals proposed by the proposal unit to the user. For example, a user orders a meal through an app, and the meal is delivered at a specified time. This allows users to easily enjoy the best meal at home. As a result, the food experience providing system according to the embodiment can suggest the best meal based on the user's preferences and health condition and deliver it to their home.

[0030] The user information collection unit analyzes the user's dietary history and can predict future dietary needs from past dietary patterns. The user information collection unit, for example, collects data on meals the user has ordered in the past and analyzes those patterns. For example, if the user tends to prefer certain dishes on weekends, the unit can make suggestions for the next meal based on that information. The user information collection unit can also predict future dietary needs based on the user's dietary history, taking into account seasonal changes and changes in health status. For example, hot dishes are suggested in winter and cold dishes are suggested in summer. This makes it possible to predict future dietary needs from past dietary patterns.

[0031] The user information collection unit can analyze a user's food preferences from their SNS posts or photos and collect more detailed preference data. For example, the user information collection unit analyzes the user's SNS posts and extracts food-related keywords and hashtags. For example, if a user frequently posts hashtags such as "#sushi" or "#pizza," meal suggestions can be made based on that information. The user information collection unit can also analyze photos posted by the user using image recognition technology to identify food preferences. For example, it can analyze photos of dishes frequently posted by the user and suggest those dishes. Furthermore, the user information collection unit can make more detailed meal suggestions based on the preference data obtained from the user's SNS posts and photos. This allows for the collection of detailed preference data from SNS posts and photos.

[0032] The user information collection unit can also collect the food preferences of the user's family and friends and make group meal suggestions. The user information collection unit, for example, collects the food preferences of the user's family and friends and makes group meal suggestions. For example, it can suggest dishes that the whole family likes and provide a meal that everyone can enjoy. The user information collection unit can also make meal suggestions for specific events or parties based on the food preferences of family and friends. For example, it can suggest a special menu for a birthday party. Furthermore, the user information collection unit can accumulate the food preferences of family and friends and use them for future suggestions. This makes it possible to make group meal suggestions that take into account the food preferences of family and friends.

[0033] The user information collection unit can customize meal suggestions taking into account the user's lifestyle habits. For example, the user information collection unit can take into account the user's work schedule and suggest meals that can be easily eaten on busy days. For example, it can suggest dishes that can be prepared in a short time or meals that are easy to carry. The user information collection unit can also make meal suggestions taking into account the user's hobbies and exercise habits. For example, it can suggest high-protein meals that are suitable for after exercise or special menus related to hobbies. Furthermore, the user information collection unit can accumulate the user's lifestyle habit data and use it for future suggestions. This makes it possible to make customized meal suggestions based on the user's lifestyle habits.

[0034] The suggestion unit can make meal suggestions according to the season and weather, and provide meals that allow you to enjoy the season. The suggestion unit, for example, can suggest dishes that use seasonal ingredients, and provide meals that allow you to enjoy the season. For example, it can suggest a salad made with fresh vegetables in spring, and a hot soup in winter. The suggestion unit can also make meal suggestions according to the weather. For example, it can suggest cold dishes on hot days, and hot dishes on cold days. Furthermore, the suggestion unit can accumulate data related to the season and weather, and use it for future suggestions. This makes it possible to make meal suggestions according to the season and weather.

[0035] The suggestion unit can build a feedback loop that learns the user's reactions to meal suggestions and reflects them in the next suggestion. For example, the suggestion unit collects the ratings and feedback provided by the user on the meal suggestions and improves the next suggestion based on that data. For example, if the user gives a high rating to a particular dish, the suggestion unit will suggest that dish again. The suggestion unit can also learn the user's reactions and continuously improve the suggestion content. For example, it can exclude dishes that the user has given a low rating from the suggestions. Furthermore, the suggestion unit can accumulate user feedback data and use it to improve suggestions over the long term. This allows the suggestion unit to learn the user's reactions and reflect them in the next suggestion.

[0036] The suggestion unit can improve the content of the suggestions by referring to ratings and reviews from other users regarding the user's meal suggestions. The suggestion unit, for example, collects ratings and reviews from other users and improves the content of the suggestions based on that data. For example, if a particular dish is highly rated by many users, the suggestion unit can reflect that dish in the suggestions. The suggestion unit can also continuously improve the content of the suggestions based on ratings and reviews from other users. For example, it can exclude low-rated dishes from the suggestions. Furthermore, the suggestion unit can accumulate rating data from other users and use it to improve the suggestions over the long term. This allows the content of the suggestions to be improved by referring to ratings and reviews from other users.

[0037] The suggestion unit can link the user's meal suggestions with cooking classes and recipe videos to provide an opportunity to actually learn cooking. The suggestion unit, for example, provides the user with recipe videos for the suggested meals to provide an opportunity to actually learn cooking. For example, a video explains how to make the suggested dish. The suggestion unit can also link with cooking classes to provide the user with an opportunity to actually learn cooking. For example, a cooking class can be held to actually make the suggested dish. Furthermore, the suggestion unit can suggest appropriate recipe videos and cooking classes according to the user's cooking skills and interests. This makes it possible to provide an opportunity to actually learn cooking in collaboration with cooking classes and recipe videos.

[0038] The delivery unit monitors the temperature and freshness of the meal in real time during delivery, allowing it to deliver the meal in the optimal condition. For example, the delivery unit builds a system that monitors the temperature of the meal in real time during delivery and delivers it to the user at the optimal temperature. For example, a temperature sensor is used to keep the meal from getting cold. The delivery unit can also monitor the freshness of the meal in real time and deliver it in the optimal condition. For example, freshness preservation technology is used to keep the ingredients fresh. Furthermore, the delivery unit can accumulate temperature and freshness data and use it to improve the delivery service in the future. This allows the temperature and freshness of the meal to be monitored in real time and delivered in the optimal condition.

[0039] The delivery unit can provide customization options (e.g., adding sauce or changing toppings) according to the user's preferences when delivering food. For example, when a user orders a meal, the delivery unit provides customization options such as adding sauce or changing toppings. For example, the delivery unit allows the user to select their favorite sauce. In addition, the delivery unit can accumulate user preference data to provide customization options according to the user's preferences and reflect the data in the next proposal. For example, if a user likes a particular topping, the delivery unit can include the topping in the next proposal. Furthermore, the delivery unit can accumulate data on customization options and use the data to improve the service in the future. This makes it possible to provide customization options according to the user's preferences.

[0040] The delivery unit can cooperate with the delivery service to provide information about the meal (e.g., nutritional value and cooking method) when the user receives the meal. For example, the delivery unit provides nutritional value information about the meal when the user receives the meal. For example, the calorie, protein, and vitamin content may be displayed. The delivery unit can also provide information about cooking methods for the meal. For example, the delivery unit may explain cooking procedures and storage methods for ingredients. Furthermore, the delivery unit can accumulate information about the meal and use it to improve the service in the future. This makes it possible to provide information about the meal.

[0041] The delivery unit can provide quizzes and games related to meals when a user receives a meal through the delivery service, thereby making the dining experience more enjoyable. For example, the delivery unit can provide a quiz related to meals when a user receives a meal. For example, the delivery unit can ask questions about the nutritional value of ingredients or cooking methods. The delivery unit can also provide games related to meals. For example, it can provide puzzle games using ingredients or cooking simulation games. Furthermore, the delivery unit can accumulate data on quizzes and games and use it to improve the service in the future. This can provide quizzes and games related to meals, making the dining experience more enjoyable.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The user information collection unit can make appropriate meal suggestions by taking into account the user's cultural background and religious restrictions regarding food. For example, it can make suggestions to avoid ingredients prohibited by a particular religion. It can also suggest traditional dishes or meals suitable for special occasions based on the user's cultural background. Furthermore, the user information collection unit can accumulate data on the user's cultural background and religious restrictions and use it for future suggestions. This makes it possible to make meal suggestions that take into account the user's cultural background and religious restrictions.

[0044] The analysis unit can take into account the environmental impact of the user's meals and make sustainable meal suggestions. For example, it can suggest dishes using locally produced ingredients to reduce the environmental impact of transportation. The analysis unit can also suggest using seasonal ingredients to reduce the environmental impact while maintaining the freshness of the ingredients. Furthermore, the analysis unit can accumulate environmental impact data related to the user's meals and use it for future suggestions. This makes it possible to make environmentally friendly and sustainable meal suggestions.

[0045] The suggestion unit can improve the content of the suggestions by referring to other users' ratings and reviews of the user's meal suggestions. For example, if a particular dish is highly rated by many users, the suggestion unit can reflect that dish in the suggestions. The suggestion unit can also continuously improve the content of the suggestions based on other users' ratings and reviews. For example, it can exclude low-rated dishes from the suggestions. Furthermore, the suggestion unit can accumulate other users' rating data and use it to improve the suggestions over the long term. This allows the suggestion unit to improve the content of the suggestions by referring to other users' ratings and reviews.

[0046] The suggestion unit can link the user's meal suggestions with cooking classes and recipe videos to provide an opportunity to actually learn cooking. For example, it can provide the user with a recipe video for the suggested meal, providing an opportunity to actually learn cooking. For example, it can explain how to make the suggested dish through a video. The suggestion unit can also link with cooking classes to provide the user with an opportunity to actually learn cooking. For example, it can hold a cooking class to actually make the suggested dish. Furthermore, the suggestion unit can suggest appropriate recipe videos and cooking classes according to the user's cooking skills and interests. This can provide an opportunity to actually learn cooking by linking with cooking classes and recipe videos.

[0047] The delivery unit can monitor the temperature and freshness of the meal in real time during delivery, and deliver it in the optimal condition. For example, we will build a system that monitors the temperature of the meal in real time during delivery and delivers it to the user at the optimal temperature. For example, a temperature sensor can be used to keep the meal from getting cold. The delivery unit can also monitor the freshness of the meal in real time and deliver it in the optimal condition. For example, freshness preservation technology can be used to keep the ingredients fresh. Furthermore, the delivery unit can accumulate temperature and freshness data and use it to improve the delivery service in the future. This allows the temperature and freshness of the meal to be monitored in real time and delivered in the optimal condition.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The user information collection unit collects information about the user's preferences and health status. For example, the user inputs their favorite ingredients and dishes, allergy information, and health goals (diet, muscle building, etc.). The user information collection unit can also collect data from health devices (such as smartwatches). For example, the unit can make more accurate meal suggestions based on the user's exercise and sleep data. Step 2: The analysis unit analyzes the collected information. For example, it uses statistical analysis or machine learning algorithms to analyze the user's dietary needs. The analysis unit also uses generative AI (e.g., text generation AI or multimodal generation AI) to generate a meal plan based on the user's preferences and health status. Step 3: The suggestion unit suggests optimal meals based on the information analyzed by the analysis unit. For example, if the user is trying to lose weight, it will suggest low-calorie, nutritionally balanced meals. Also, if the user has a preference for a particular ingredient, it will suggest recipes using that ingredient. Step 4: The delivery unit delivers the meal suggested by the suggestion unit to the user. For example, the user can order a meal through the app, and the meal will be delivered at the specified time. This allows the user to easily enjoy the optimal meal at home.

[0050] (Example 2) The food experience providing system according to the embodiment of the present invention is a system that proposes and delivers optimal meals based on the user's preferences and health condition. As a result, the food experience providing system can provide optimal meals to the user and support health management and the enjoyment of meals.

[0051] A food experience providing system according to an embodiment includes a user information collection unit, an analysis unit, a proposal unit, and a delivery unit. The user information collection unit collects information about a user's preferences and health status. For example, the user inputs their favorite ingredients and dishes, allergy information, and health goals (e.g., dieting, muscle building). The user information collection unit can also collect data from a health device (e.g., a smartwatch). For example, the user's exercise volume and sleep data can be used to provide more accurate meal proposals. The analysis unit analyzes the collected information. For example, the analysis unit analyzes the user's dietary needs using statistical analysis or machine learning algorithms. The analysis unit also generates a meal plan based on the user's preferences and health status using a generation AI (e.g., a text generation AI or a multimodal generation AI). The proposal unit proposes optimal meals based on the information analyzed by the analysis unit. For example, if a user is dieting, the proposal unit proposes low-calorie, nutritionally balanced meals. If a user prefers a specific ingredient, the proposal unit proposes recipes using that ingredient. The delivery unit delivers the meals proposed by the proposal unit to the user. For example, a user orders a meal through an app, and the meal is delivered at a specified time. This allows users to easily enjoy the best meal at home. As a result, the food experience providing system according to the embodiment can suggest the best meal based on the user's preferences and health condition and deliver it to their home.

[0052] The user information collection unit analyzes the user's dietary history and can predict future dietary needs from past dietary patterns. The user information collection unit, for example, collects data on meals the user has ordered in the past and analyzes those patterns. For example, if the user tends to prefer certain dishes on weekends, the unit can make suggestions for the next meal based on that information. The user information collection unit can also predict future dietary needs based on the user's dietary history, taking into account seasonal changes and changes in health status. For example, hot dishes are suggested in winter and cold dishes are suggested in summer. This makes it possible to predict future dietary needs from past dietary patterns.

[0053] The user information collection unit can analyze a user's food preferences from their SNS posts or photos and collect more detailed preference data. For example, the user information collection unit analyzes the user's SNS posts and extracts food-related keywords and hashtags. For example, if a user frequently posts hashtags such as "#sushi" or "#pizza," meal suggestions can be made based on that information. The user information collection unit can also analyze photos posted by the user using image recognition technology to identify food preferences. For example, it can analyze photos of dishes frequently posted by the user and suggest those dishes. Furthermore, the user information collection unit can make more detailed meal suggestions based on the preference data obtained from the user's SNS posts and photos. This allows for the collection of detailed preference data from SNS posts and photos.

[0054] The user information collection unit can use the emotion estimation function to analyze the user's emotions toward food and make meal suggestions based on those emotions. For example, the user information collection unit analyzes the emotions the user has when rating a meal and makes the next suggestion based on that data. For example, if the user expresses positive emotions toward a particular dish, the unit can suggest that dish again. The user information collection unit can also use the emotion estimation function to analyze the user's emotions toward food in real time. For example, it can analyze whether the user is enjoying the meal using facial expression recognition technology and make meal suggestions based on the results. Furthermore, the user information collection unit can accumulate the user's emotion data and use it as long-term preference data. This allows for emotion-based meal suggestions.

[0055] The user information collection unit can also collect the food preferences of the user's family and friends and make group meal suggestions. The user information collection unit, for example, collects the food preferences of the user's family and friends and makes group meal suggestions. For example, it can suggest dishes that the whole family likes and provide a meal that everyone can enjoy. The user information collection unit can also make meal suggestions for specific events or parties based on the food preferences of family and friends. For example, it can suggest a special menu for a birthday party. Furthermore, the user information collection unit can accumulate the food preferences of family and friends and use them for future suggestions. This makes it possible to make group meal suggestions that take into account the food preferences of family and friends.

[0056] The user information collection unit can customize meal suggestions taking into account the user's lifestyle habits. For example, the user information collection unit can take into account the user's work schedule and suggest meals that can be easily eaten on busy days. For example, it can suggest dishes that can be prepared in a short time or meals that are easy to carry. The user information collection unit can also make meal suggestions taking into account the user's hobbies and exercise habits. For example, it can suggest high-protein meals that are suitable for after exercise or special menus related to hobbies. Furthermore, the user information collection unit can accumulate the user's lifestyle habit data and use it for future suggestions. This makes it possible to make customized meal suggestions based on the user's lifestyle habits.

[0057] The user information collection unit can use the emotion estimation function to analyze the emotions of the user when entering meal plans in real time and make suggestions that will elicit positive emotions. For example, the user information collection unit can analyze the emotions of the user when entering meal plans in real time and make suggestions that will elicit positive emotions. For example, if the user is feeling stressed, the unit can suggest dishes that use ingredients that have a relaxing effect. The user information collection unit can also use the emotion estimation function to analyze the emotions of the user when entering meal plans using facial expression recognition technology and make meal suggestions based on the results. Furthermore, the user information collection unit can accumulate user emotion data and use it as long-term preference data. This makes it possible to make positive meal suggestions based on the user's emotions.

[0058] The suggestion unit can make meal suggestions according to the season and weather, and provide meals that allow you to enjoy the season. The suggestion unit, for example, can suggest dishes that use seasonal ingredients, and provide meals that allow you to enjoy the season. For example, it can suggest a salad made with fresh vegetables in spring, and a hot soup in winter. The suggestion unit can also make meal suggestions according to the weather. For example, it can suggest cold dishes on hot days, and hot dishes on cold days. Furthermore, the suggestion unit can accumulate data related to the season and weather, and use it for future suggestions. This makes it possible to make meal suggestions according to the season and weather.

[0059] The suggestion unit can build a feedback loop that learns the user's reactions to meal suggestions and reflects them in the next suggestion. For example, the suggestion unit collects the ratings and feedback provided by the user on the meal suggestions and improves the next suggestion based on that data. For example, if the user gives a high rating to a particular dish, the suggestion unit will suggest that dish again. The suggestion unit can also learn the user's reactions and continuously improve the suggestion content. For example, it can exclude dishes that the user has given a low rating from the suggestions. Furthermore, the suggestion unit can accumulate user feedback data and use it to improve suggestions over the long term. This allows the suggestion unit to learn the user's reactions and reflect them in the next suggestion.

[0060] The suggestion unit uses the emotion estimation function to suggest meals according to the user's emotional state, thereby reducing stress and improving mood. The suggestion unit, for example, analyzes the user's emotional state in real time and suggests meals that suit the user's mood at that time. For example, if the user is feeling stressed, the suggestion unit suggests dishes that use ingredients that have a relaxing effect. The suggestion unit can also use the emotion estimation function to analyze the user's emotional state using facial expression recognition technology and make meal suggestions based on the results. Furthermore, the suggestion unit can accumulate user emotion data and use it to improve suggestions over the long term. This makes it possible to suggest meals according to the user's emotional state.

[0061] The suggestion unit can improve the content of the suggestions by referring to ratings and reviews from other users regarding the user's meal suggestions. The suggestion unit, for example, collects ratings and reviews from other users and improves the content of the suggestions based on that data. For example, if a particular dish is highly rated by many users, the suggestion unit can reflect that dish in the suggestions. The suggestion unit can also continuously improve the content of the suggestions based on ratings and reviews from other users. For example, it can exclude low-rated dishes from the suggestions. Furthermore, the suggestion unit can accumulate rating data from other users and use it to improve the suggestions over the long term. This allows the content of the suggestions to be improved by referring to ratings and reviews from other users.

[0062] The suggestion unit can link the user's meal suggestions with cooking classes and recipe videos to provide an opportunity to actually learn cooking. The suggestion unit, for example, provides the user with recipe videos for the suggested meals to provide an opportunity to actually learn cooking. For example, a video explains how to make the suggested dish. The suggestion unit can also link with cooking classes to provide the user with an opportunity to actually learn cooking. For example, a cooking class can be held to actually make the suggested dish. Furthermore, the suggestion unit can suggest appropriate recipe videos and cooking classes according to the user's cooking skills and interests. This makes it possible to provide an opportunity to actually learn cooking in collaboration with cooking classes and recipe videos.

[0063] The suggestion unit can use the emotion estimation function to make meal suggestions based on the user's emotions and suggest meals that elicit specific emotions. For example, the suggestion unit can analyze the user's emotional state in real time and suggest meals that match the user's mood at that time. For example, if the user is feeling stressed, the suggestion unit can suggest dishes that use ingredients that have a relaxing effect. The suggestion unit can also use the emotion estimation function to analyze the user's emotional state with facial expression recognition technology and make meal suggestions based on the results. Furthermore, the suggestion unit can accumulate user emotion data and use it to improve the suggestions over the long term. This makes it possible to suggest meals that elicit specific emotions based on the user's emotions.

[0064] The delivery unit monitors the temperature and freshness of the meal in real time during delivery, allowing it to deliver the meal in the optimal condition. For example, the delivery unit builds a system that monitors the temperature of the meal in real time during delivery and delivers it to the user at the optimal temperature. For example, a temperature sensor is used to keep the meal from getting cold. The delivery unit can also monitor the freshness of the meal in real time and deliver it in the optimal condition. For example, freshness preservation technology is used to keep the ingredients fresh. Furthermore, the delivery unit can accumulate temperature and freshness data and use it to improve the delivery service in the future. This allows the temperature and freshness of the meal to be monitored in real time and delivered in the optimal condition.

[0065] The delivery unit can provide customization options (e.g., adding sauce or changing toppings) according to the user's preferences when delivering food. For example, when a user orders a meal, the delivery unit provides customization options such as adding sauce or changing toppings. For example, the delivery unit allows the user to select their favorite sauce. In addition, the delivery unit can accumulate user preference data to provide customization options according to the user's preferences and reflect the data in the next proposal. For example, if a user likes a particular topping, the delivery unit can include the topping in the next proposal. Furthermore, the delivery unit can accumulate data on customization options and use the data to improve the service in the future. This makes it possible to provide customization options according to the user's preferences.

[0066] The delivery unit can use the emotion estimation function to analyze the emotions of the user when receiving the meal and reflect them in the next delivery service. For example, the delivery unit can analyze the emotions of the user when receiving the meal in real time and improve the next delivery service based on the data. For example, if the user is satisfied, the information can be reflected in the next proposal. The delivery unit can also use the emotion estimation function to analyze the emotions of the user when receiving the meal using facial recognition technology and improve the delivery service based on the results. Furthermore, the delivery unit can accumulate user emotion data and use it for long-term service improvement. This allows the user's emotions to be analyzed and reflected in the next delivery service.

[0067] The delivery unit can cooperate with the delivery service to provide information about the meal (e.g., nutritional value and cooking method) when the user receives the meal. For example, the delivery unit provides nutritional value information about the meal when the user receives the meal. For example, the calorie, protein, and vitamin content may be displayed. The delivery unit can also provide information about cooking methods for the meal. For example, the delivery unit may explain cooking procedures and storage methods for ingredients. Furthermore, the delivery unit can accumulate information about the meal and use it to improve the service in the future. This makes it possible to provide information about the meal.

[0068] The delivery unit can provide quizzes and games related to meals when a user receives a meal through the delivery service, thereby making the dining experience more enjoyable. For example, the delivery unit can provide a quiz related to meals when a user receives a meal. For example, the delivery unit can ask questions about the nutritional value of ingredients or cooking methods. The delivery unit can also provide games related to meals. For example, it can provide puzzle games using ingredients or cooking simulation games. Furthermore, the delivery unit can accumulate data on quizzes and games and use it to improve the service in the future. This can provide quizzes and games related to meals, making the dining experience more enjoyable.

[0069] The delivery unit can use the emotion estimation function to analyze the emotions of the user when receiving the meal in real time and make suggestions that will elicit positive emotions. For example, the delivery unit can analyze the emotions of the user when receiving the meal in real time and make suggestions that will elicit positive emotions. For example, the delivery unit can display a message that will make the user feel happy. The delivery unit can also use the emotion estimation function to analyze the emotions of the user when receiving the meal using facial expression recognition technology and make suggestions based on the results. Furthermore, the delivery unit can accumulate user emotion data and use it for long-term service improvements. This makes it possible to analyze the user's emotions in real time and make suggestions that will elicit positive emotions.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The user information collection unit can make appropriate meal suggestions by taking into account the user's cultural background and religious restrictions regarding food. For example, it can make suggestions to avoid ingredients prohibited by a particular religion. It can also suggest traditional dishes or meals suitable for special occasions based on the user's cultural background. Furthermore, the user information collection unit can accumulate data on the user's cultural background and religious restrictions and use it for future suggestions. This makes it possible to make meal suggestions that take into account the user's cultural background and religious restrictions.

[0072] The analysis unit can take into account the environmental impact of the user's meals and make sustainable meal suggestions. For example, it can suggest dishes using locally produced ingredients to reduce the environmental impact of transportation. The analysis unit can also suggest using seasonal ingredients to reduce the environmental impact while maintaining the freshness of the ingredients. Furthermore, the analysis unit can accumulate environmental impact data related to the user's meals and use it for future suggestions. This makes it possible to make environmentally friendly and sustainable meal suggestions.

[0073] The suggestion unit can improve the content of the suggestions by referring to other users' ratings and reviews of the user's meal suggestions. For example, if a particular dish is highly rated by many users, the suggestion unit can reflect that dish in the suggestions. The suggestion unit can also continuously improve the content of the suggestions based on other users' ratings and reviews. For example, it can exclude low-rated dishes from the suggestions. Furthermore, the suggestion unit can accumulate other users' rating data and use it to improve the suggestions over the long term. This allows the suggestion unit to improve the content of the suggestions by referring to other users' ratings and reviews.

[0074] The suggestion unit can link the user's meal suggestions with cooking classes and recipe videos to provide an opportunity to actually learn cooking. For example, it can provide the user with a recipe video for the suggested meal, providing an opportunity to actually learn cooking. For example, it can explain how to make the suggested dish through a video. The suggestion unit can also link with cooking classes to provide the user with an opportunity to actually learn cooking. For example, it can hold a cooking class to actually make the suggested dish. Furthermore, the suggestion unit can suggest appropriate recipe videos and cooking classes according to the user's cooking skills and interests. This can provide an opportunity to actually learn cooking by linking with cooking classes and recipe videos.

[0075] The delivery unit can monitor the temperature and freshness of the meal in real time during delivery, and deliver it in the optimal condition. For example, we will build a system that monitors the temperature of the meal in real time during delivery and delivers it to the user at the optimal temperature. For example, a temperature sensor can be used to keep the meal from getting cold. The delivery unit can also monitor the freshness of the meal in real time and deliver it in the optimal condition. For example, freshness preservation technology can be used to keep the ingredients fresh. Furthermore, the delivery unit can accumulate temperature and freshness data and use it to improve the delivery service in the future. This allows the temperature and freshness of the meal to be monitored in real time and delivered in the optimal condition.

[0076] The user information collection unit can use the emotion estimation function to analyze the user's emotions toward food and make meal suggestions based on those emotions. For example, the emotion when the user evaluates a meal can be analyzed and the next suggestion can be made based on that data. For example, if the user expresses positive emotions toward a particular dish, that dish can be suggested again. The user information collection unit can also use the emotion estimation function to analyze the user's emotions toward food in real time. For example, it can use facial expression recognition technology to analyze whether the user is enjoying the meal and make meal suggestions based on the results. Furthermore, the user information collection unit can accumulate the user's emotion data and use it as long-term preference data. This allows for emotion-based meal suggestions.

[0077] The suggestion unit can use the emotion estimation function to suggest meals according to the user's emotional state, thereby reducing stress and improving mood. For example, it can analyze the user's emotional state in real time and suggest meals that suit the user's mood at that time. For example, if the user is feeling stressed, it can suggest dishes that use ingredients that have a relaxing effect. The suggestion unit can also use the emotion estimation function to analyze the user's emotional state using facial expression recognition technology and make meal suggestions based on the results. Furthermore, the suggestion unit can accumulate user emotion data and use it to improve suggestions over the long term. This makes it possible to suggest meals according to the user's emotional state.

[0078] The delivery unit can use the emotion estimation function to analyze the emotions of the user when receiving the meal and reflect them in the next delivery service. For example, the emotion of the user when receiving the meal can be analyzed in real time and the data can be used to improve the next delivery service. For example, if the user is satisfied, that information can be reflected in the next proposal. The delivery unit can also use the emotion estimation function to analyze the emotions of the user when receiving the meal using facial recognition technology and improve the delivery service based on the results. Furthermore, the delivery unit can accumulate user emotion data and use it for long-term service improvement. This allows the user's emotions to be analyzed and reflected in the next delivery service.

[0079] The delivery unit can use the emotion estimation function to analyze the emotions of the user when receiving the meal in real time and make suggestions that will elicit positive emotions. For example, the delivery unit can analyze the emotions of the user when receiving the meal in real time and make suggestions that will elicit positive emotions. For example, the delivery unit can display a message that will make the user feel happy. The delivery unit can also use the emotion estimation function to analyze the emotions of the user when receiving the meal using facial expression recognition technology and make suggestions based on the results. Furthermore, the delivery unit can accumulate user emotion data and use it for long-term service improvements. This makes it possible to analyze the user's emotions in real time and make suggestions that will elicit positive emotions.

[0080] The suggestion unit can use the emotion estimation function to make meal suggestions based on the user's emotions and suggest meals that elicit specific emotions. For example, it can analyze the user's emotional state in real time and suggest meals that match the user's mood at that time. For example, if the user is feeling stressed, it can suggest dishes that use ingredients that have a relaxing effect. The suggestion unit can also use the emotion estimation function to analyze the user's emotional state using facial expression recognition technology and make meal suggestions based on the results. Furthermore, the suggestion unit can accumulate user emotion data and use it to improve suggestions over the long term. This makes it possible to suggest meals that elicit specific emotions based on the user's emotions.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The user information collection unit collects information about the user's preferences and health status. For example, the user inputs their favorite ingredients and dishes, allergy information, and health goals (diet, muscle building, etc.). The user information collection unit can also collect data from health devices (such as smartwatches). For example, the unit can make more accurate meal suggestions based on the user's exercise and sleep data. Step 2: The analysis unit analyzes the collected information. For example, it uses statistical analysis or machine learning algorithms to analyze the user's dietary needs. The analysis unit also uses generative AI (e.g., text generation AI or multimodal generation AI) to generate a meal plan based on the user's preferences and health status. Step 3: The suggestion unit suggests optimal meals based on the information analyzed by the analysis unit. For example, if the user is trying to lose weight, it will suggest low-calorie, nutritionally balanced meals. Also, if the user has a preference for a particular ingredient, it will suggest recipes using that ingredient. Step 4: The delivery unit delivers the meal suggested by the suggestion unit to the user. For example, the user can order a meal through the app, and the meal will be delivered at the specified time. This allows the user to easily enjoy the optimal meal at home.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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 user information collection unit that collects information about a user's preferences and health status; an analysis unit that analyzes the information collected by the user information collection unit; a suggestion unit that suggests an optimal meal based on the information analyzed by the analysis unit; a home delivery unit that delivers the meal suggested by the suggestion unit to the user. A system characterized by:

2. The user information collection unit Analyze the user's food preferences from their SNS posts or photos to collect more detailed preference data.

2. The system of claim 1.

3. The proposal unit We offer meals that reflect the season and weather, allowing you to enjoy seasonal meals.

2. The system of claim 1.

4. The home delivery department During delivery, the temperature and freshness of the food are monitored in real time, and the food is delivered in the optimal condition.

2. The system of claim 1.

5. The user information collection unit Analyzing the user's feelings about food and making meal suggestions based on the feelings 2. The system of claim 1.

6. The proposal unit Providing meal suggestions based on the user's emotional state to reduce stress and improve mood 2. The system of claim 1.

7. The home delivery department Analyze the user's emotions when receiving the meal and reflect them in the next delivery service.

2. The system of claim 1.

8. The home delivery department Analyze the user's emotions in real time when receiving the meal and make suggestions that will elicit positive emotions 2. The system of claim 1.

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