System

Abstract

An object of a system according to an embodiment is to learn dietary preferences of a user and propose an appropriate restaurant or dish on the basis of the dietary preferences.SOLUTION: A system includes an input information collection unit, a characteristic analysis unit, a preference learning unit, and a proposal unit. The input information collection unit collects pictures and impressions of meals of the user. The characteristic analysis unit analyzes the information collected by the input information collection unit. The preference learning unit learns the preference of the user on the basis of the information analyzed by the characteristic analysis unit. The suggestion unit suggests a restaurant or a dish on the basis of the preference of the user learned by the preference learning unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to accurately grasp a user's food preferences and suggest appropriate restaurants and dishes based on those preferences.

[0005] The system according to the embodiment aims to learn the user's food preferences and, based on the learned preferences, suggest appropriate restaurants and dishes. [Means for solving the problem]

[0006] The system according to the embodiment includes an input information collection unit, a characteristic analysis unit, a preference learning unit, and a suggestion unit. The input information collection unit collects photos and impressions of meals from users. The characteristic analysis unit analyzes the information collected by the input information collection unit. The preference learning unit learns the user's preferences based on the information analyzed by the characteristic analysis unit. The suggestion unit suggests restaurants and dishes based on the user's preferences learned by the preference learning unit. [Effects of the Invention]

[0007] The system according to the embodiment can learn the user's food preferences and suggest appropriate restaurants and dishes based on the learned preferences. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The restaurant recommendation system according to an embodiment of the present invention is a system in which, when a user inputs photos of meals and their impressions, a generation AI uses the photos and impressions to suggest restaurants and dishes that the user likes. This allows the restaurant recommendation system to suggest appropriate restaurants and dishes based on the user's preferences.

[0029] A restaurant suggestion system according to an embodiment includes an input information collection unit, a characteristic analysis unit, a preference learning unit, and a suggestion unit. The input information collection unit collects photos of meals and impressions of users. For example, users can upload photos of meals and input impressions. The input information collection unit can also set a photo resolution and a character limit for impressions. The characteristic analysis unit analyzes the information collected by the input information collection unit. For example, the characteristic analysis unit can analyze characteristics of dishes from photos using an image analysis algorithm. The characteristic analysis unit can also analyze characteristics of dishes from impressions using text analysis technology. The preference learning unit learns user preferences based on the information analyzed by the characteristic analysis unit. For example, the preference learning unit learns user preferences using a machine learning algorithm. The preference learning unit can also learn user preferences by taking into account the type of dataset. The suggestion unit suggests restaurants and dishes based on the user preferences learned by the preference learning unit. For example, the suggestion unit can suggest appropriate restaurants to the user by taking into account restaurant selection criteria. The suggestion unit can also suggest appropriate dishes to the user by taking into account the type of dish. As a result, the restaurant recommendation system according to the embodiment can recommend appropriate restaurants and dishes based on the user's preferences. For example, if a user prefers healthy dishes, restaurants that offer healthy menus will be recommended. If a user prefers beautifully decorated dishes, restaurants that offer such dishes will be recommended. The recommendations are made taking into consideration the user's current location and past visit history.

[0030] The input information collection unit automatically adds nutritional value and calorie information for meals to user input information, thereby collecting information from the perspective of health management. For example, the input information collection unit uses AI to automatically add nutritional value and calorie information to photos of meals uploaded by users. For example, it analyzes the ingredients in the photos and displays their nutritional value. The input information collection unit also collects health management information for meals based on user input information. For example, if a user inputs, "This salad was healthy," the information is saved in a database. The input information collection unit also supports the user's health management by automatically adding nutritional value and calorie information. For example, if a user inputs, "This dish was good because it was low in calories," the information is collected. This allows the automatic addition of nutritional value and calorie information to support health management.

[0031] The input information collecting unit also collects environmental and background sounds when the user makes input, and can analyze the atmosphere and environment of the meal. For example, when the user uploads a photo of the meal, the input information collecting unit simultaneously collects environmental and background sounds. For example, it analyzes the music in the restaurant and the sounds of surrounding conversations. The input information collecting unit also analyzes the environmental and background sounds to understand the atmosphere and environment of the meal. For example, it analyzes the difference between eating in a quiet environment and eating in a lively environment. The input information collecting unit also adds the environmental and background sounds to the user's input information to understand the atmosphere of the meal in more detail. For example, if the user inputs, "The atmosphere of this restaurant was good," that information is collected. In this way, it is possible to collect environmental and background sounds and analyze the atmosphere and environment of the meal.

[0032] The input information collection unit also supports voice input or gesture input for user input information, thereby providing a wider variety of input methods. The input information collection unit, for example, allows the user to input their impressions of a meal by voice. For example, if the user inputs "This dish was delicious" by voice, the information is collected. The input information collection unit also allows the user to input their impressions of a meal using gesture input. For example, if the user makes a thumbs-up gesture, the information is collected as a positive impression. The input information collection unit also makes it easier for the user to input their impressions by using voice input or gesture input. For example, if the user inputs "This dish was excellent" by voice, the information is stored in a database. This allows for support of voice input and gesture input, providing a wider variety of input methods.

[0033] The input information collection unit can collect input information from users in different cultural spheres or regions and analyze preferences from a global perspective. The input information collection unit, for example, collects input information from users in different cultural spheres or regions and analyzes preferences from a global perspective. For example, it analyzes the differences in preferences between Japanese users and American users. The input information collection unit also analyzes the characteristics of cuisine based on input information from users in different cultural spheres or regions. For example, it analyzes the differences between Asian cuisine and European cuisine. The input information collection unit also analyzes preferences from a global perspective to make appropriate suggestions to users. For example, if a user inputs, "This dish had a Japanese taste and was delicious," that information is collected. This makes it possible to collect input information from users in different cultural spheres or regions and analyze preferences from a global perspective.

[0034] The characteristic analysis unit can also use generative AI to analyze the history and cultural background of a dish, thereby enriching the information provided to the user. For example, in analyzing the characteristics of a dish, the characteristic analysis unit uses generative AI to analyze the history and cultural background of the dish. For example, it analyzes the origin of the dish and traditional cooking methods and provides that information to the user. In addition, in analyzing the characteristics of a dish, the characteristic analysis unit refers to related literature and materials to understand the cultural background of the dish. For example, it generates text that explains the history and cultural background of the dish. In analyzing the characteristics of a dish, the characteristic analysis unit uses generative AI to analyze the history and cultural background of the dish, and provides that information to the user. For example, it generates text that explains the origin of the dish and traditional cooking methods. This allows the history and cultural background of the dish to be analyzed, thereby enriching the information provided to the user.

[0035] The characteristic analysis unit can analyze the origin and production method of ingredients in the characteristic analysis of a dish and provide eco-friendly options. The characteristic analysis unit, for example, analyzes the origin and production method of ingredients in the characteristic analysis of a dish. For example, it analyzes whether organic ingredients or locally produced ingredients are used. The characteristic analysis unit also analyzes the origin and production method of ingredients to provide eco-friendly options. For example, if a user inputs, "I'm glad this dish uses organic ingredients," the characteristic analysis unit collects that information. The characteristic analysis unit can analyze the origin and production method of ingredients in the characteristic analysis of a dish and provide eco-friendly options. For example, if a user inputs, "I'm glad this dish uses locally produced ingredients," the characteristic analysis unit collects that information. This allows the origin and production method of ingredients to be analyzed and eco-friendly options to be provided.

[0036] The characteristic analysis unit can generate a 3D model in the characteristic analysis of a dish, allowing the user to visually confirm the dish. For example, the characteristic analysis unit generates a 3D model in the characteristic analysis of a dish, allowing the user to visually confirm the dish. For example, the shape and presentation of the dish are displayed in a 3D model. Furthermore, by generating a 3D model, the characteristic analysis unit allows the user to visually understand the characteristics of the dish. For example, the presentation and color balance of the dish are displayed in a 3D model. Furthermore, the characteristic analysis unit generates a 3D model in the characteristic analysis of a dish, allowing the user to visually confirm the dish. For example, the shape and presentation of the dish are displayed in a 3D model. In this way, the 3D model is generated, allowing the user to visually confirm the dish.

[0037] The characteristic analysis unit can also analyze different cooking methods and recipe variations in the analysis of the characteristics of a dish and suggest them to the user. For example, the characteristic analysis unit can analyze different cooking methods and recipe variations in the analysis of the characteristics of a dish and suggest them to the user. For example, different cooking methods and recipes can be suggested for the same dish. The characteristic analysis unit can also analyze different cooking methods and recipe variations and suggest them to the user. For example, if a user inputs, "This dish was good when grilled," the characteristic analysis unit collects that information. The characteristic analysis unit can also analyze different cooking methods and recipe variations in the analysis of the characteristics of a dish and suggest them to the user. For example, if a user inputs, "This dish was good when stewed," the characteristic analysis unit collects that information. This allows different cooking methods and recipe variations to be analyzed and suggested to the user.

[0038] The preference learning unit can also incorporate the user's past dietary history and health data into its learning using the generation AI. For example, the preference learning unit collects the user's past dietary history and incorporates it into learning using the generation AI. For example, it learns preferences based on data on dishes the user has eaten in the past. The preference learning unit also collects the user's health data and incorporates it into learning using the generation AI. For example, it learns preferences taking into account the user's health condition and nutritional balance. The preference learning unit also learns more accurate preferences by incorporating the user's past dietary history and health data into its learning. For example, if the user inputs, "This dish was healthy and good," that information is collected. This makes it possible to incorporate the user's past dietary history and health data into its learning.

[0039] The preference learning unit can dynamically update the user's preferences by taking into account changes in seasons or weather when learning the user's preferences. The preference learning unit, for example, takes into account changes in seasons and weather when learning the user's preferences. For example, it learns that the user tends to prefer cold dishes in summer and hot dishes in winter. The preference learning unit also collects data on seasons and weather and dynamically updates the user's preferences. For example, it makes suggestions according to the weather, such as suggesting hot soup on a rainy day. The preference learning unit also builds a system that dynamically updates the user's preferences by taking into account changes in seasons and weather. For example, it learns seasonal trends in ingredients and dishes and reflects them in suggestions. This allows the user's preferences to be dynamically updated by taking into account changes in seasons and weather.

[0040] The preference learning unit can take into account the preferences of family and friends when learning the user's preferences and make group suggestions. For example, the preference learning unit can take into account the preferences of family and friends when learning the user's preferences. For example, it can suggest dishes that the whole family can enjoy. The preference learning unit can also collect the preferences of family and friends and make group suggestions. For example, if a friend inputs, "This dish was delicious," the unit learns that information. The preference learning unit can also build a system that takes into account the preferences of family and friends when learning the user's preferences. For example, it can suggest dishes that the whole family can enjoy. This allows it to take into account the preferences of family and friends when making group suggestions.

[0041] The preference learning unit synchronizes data between different devices when learning the user's preferences, thereby providing the seamless experience. The preference learning unit, for example, synchronizes data between different devices when learning the user's preferences. For example, it synchronizes data between a smartphone and a tablet, thereby providing a seamless experience. The preference learning unit also synchronizes data between different devices to consistently learn the user's preferences. For example, it makes it possible for information entered by a user on a smartphone to also be used on a tablet. The preference learning unit also builds a system that synchronizes data between different devices when learning the user's preferences. For example, it synchronizes data between a smartphone and a tablet, thereby providing a seamless experience. This allows data synchronization between different devices to provide a seamless experience.

[0042] The suggestion unit can use the generation AI to take into account the user's past reviews and ratings to improve the accuracy of the suggestions. For example, when suggesting restaurants or dishes, the suggestion unit uses the generation AI to take into account the user's past reviews and ratings. For example, it prioritizes suggesting restaurants that the user has given high ratings to in the past. The suggestion unit also collects the user's past reviews and ratings to improve the accuracy of the suggestions. For example, if a user inputs "This restaurant was delicious," the suggestion unit learns that information. The suggestion unit also uses the generation AI to build a system that takes into account the user's past reviews and ratings to improve the accuracy of the suggestions. For example, it prioritizes suggesting restaurants that the user has given high ratings to in the past. This allows the generation AI to take into account the user's past reviews and ratings and improve the accuracy of the suggestions.

[0043] The suggestion unit can collect user feedback on the suggestion content in real time and reflect it in the next suggestion. For example, when suggesting restaurants or dishes, the suggestion unit collects user feedback on the suggestion content in real time. For example, if a user inputs "This restaurant was delicious," the information is learned. The suggestion unit also collects user feedback in real time and reflects it in the next suggestion. For example, if a user inputs "This dish was delicious," the information is learned. The suggestion unit also builds a system that collects user feedback on the suggestion content in real time and reflects it in the next suggestion. For example, if a user inputs "This restaurant was delicious," the information is learned. In this way, user feedback on the suggestion content can be collected in real time and reflected in the next suggestion.

[0044] The suggestion unit can make the optimal suggestion by taking into account the user's current location or travel route. The suggestion unit, for example, takes into account the user's current location and travel route when suggesting restaurants and dishes. For example, the user is suggested restaurants that are close to the user's current location. The suggestion unit also takes into account the user's current location and travel route to make the optimal suggestion. For example, the suggestion unit suggests restaurants that the user can stop by while traveling. The suggestion unit also builds a system that takes into account the user's current location and travel route when suggesting restaurants and dishes and makes the optimal suggestion. For example, the user is suggested restaurants that are close to the user's current location. This makes it possible to make the optimal suggestion by taking into account the user's current location and travel route.

[0045] The suggestion unit can link the suggested content with an SNS or a messaging app to make the sharing easier. For example, when suggesting restaurants or dishes, the suggestion unit links the suggested content with an SNS or a messaging app. For example, the user can share the suggested restaurant with friends. The suggestion unit also links the suggested content with an SNS or a messaging app to make the sharing easier. For example, the user can post the suggested dish on an SNS. The suggestion unit also builds a system when suggesting restaurants or dishes that links the suggested content with an SNS or a messaging app to make the sharing easier. For example, the user can share the suggested restaurant with friends. This makes it easier to link the suggested content with an SNS or a messaging app and share it.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The input information collection unit can also support voice input or gesture input for user input information, thereby providing a wider variety of input methods. For example, the input information collection unit allows the user to input their impressions of the meal by voice. For example, if the user voice-inputs, "This dish was delicious," that information is collected. The input information collection unit can also allow the user to input their impressions of the meal using gesture input. For example, if the user makes a thumbs-up gesture, that information is collected as a positive impression. The input information collection unit can also use voice input or gesture input to make it easier for the user to input their impressions. For example, if the user voice-inputs, "This dish was excellent," that information is saved in a database. This allows for voice input and gesture input, thereby providing a wider variety of input methods.

[0048] The input information collection unit also collects environmental and background sounds when the user makes input, and can analyze the atmosphere and environment of the meal. For example, when a user uploads a photo of a meal, the environmental and background sounds are also collected at the same time. For example, the music in the restaurant and the sounds of surrounding conversations are analyzed. The input information collection unit also analyzes the environmental and background sounds to understand the atmosphere and environment of the meal. For example, it analyzes the difference between eating in a quiet environment and eating in a lively environment. The input information collection unit also adds the environmental and background sounds to the user's input information to understand the atmosphere of the meal in more detail. For example, if a user inputs, "The atmosphere of this restaurant was good," that information is collected. In this way, the environmental and background sounds can be collected and the atmosphere and environment of the meal can be analyzed.

[0049] The input information collection unit can collect input information from users of different cultural spheres or regions and analyze preferences from a global perspective. For example, input information from users of different cultural spheres or regions is collected and preferences from a global perspective is analyzed. For example, the input information collection unit analyzes the differences in preferences between Japanese users and American users. The input information collection unit also analyzes the characteristics of cuisine based on input information from users of different cultural spheres or regions. For example, the input information collection unit analyzes the differences between Asian cuisine and European cuisine. The input information collection unit also analyzes preferences from a global perspective and makes appropriate suggestions to users. For example, if a user inputs, "This dish had a Japanese taste and was delicious," that information is collected. This makes it possible to collect input information from users of different cultural spheres or regions and analyze preferences from a global perspective.

[0050] The input information collection unit automatically adds nutritional value and calorie information for meals to the information entered by the user, thereby collecting information from the perspective of health management. For example, AI automatically adds nutritional value and calorie information to photos of meals uploaded by the user. For example, it analyzes the ingredients in the photo and displays their nutritional value. The input information collection unit also collects health management information for meals based on the information entered by the user. For example, if a user enters "This salad was healthy," the information is saved in a database. The input information collection unit also supports the user's health management by automatically adding nutritional value and calorie information. For example, if a user enters "This dish was good because it was low in calories," the information is collected. This allows the automatic addition of nutritional value and calorie information to support health management.

[0051] The characteristic analysis unit can also use generative AI to analyze the history and cultural background of a dish, thereby enriching the information provided to the user. For example, in analyzing the characteristics of a dish, generative AI can be used to analyze the history and cultural background of the dish. For example, the origin of the dish and traditional cooking methods can be analyzed, and that information can be provided to the user. In addition, in analyzing the characteristics of a dish, the characteristic analysis unit can refer to related literature and materials to understand the cultural background of the dish. For example, it can generate text that explains the history and cultural background of the dish. In analyzing the characteristics of a dish, the characteristic analysis unit can use generative AI to analyze the history and cultural background of the dish, and provide that information to the user. For example, it can generate text that explains the origin of the dish and traditional cooking methods. This allows the history and cultural background of the dish to be analyzed, thereby enriching the information provided to the user.

[0052] In analyzing the characteristics of a dish, the characteristic analysis unit may also analyze the origin and production method of ingredients to provide eco-friendly options. For example, in analyzing the characteristics of a dish, the characteristic analysis unit may analyze the origin and production method of ingredients. For example, it may analyze whether organic or locally produced ingredients are used. The characteristic analysis unit may also analyze the origin and production method of ingredients to provide eco-friendly options. For example, if a user inputs, "I'm glad this dish uses organic ingredients," the characteristic analysis unit may collect that information. In analyzing the characteristics of a dish, the characteristic analysis unit may also analyze the origin and production method of ingredients to provide eco-friendly options. For example, if a user inputs, "I'm glad this dish uses locally produced ingredients," the characteristic analysis unit may collect that information. This may analyze the origin and production method of ingredients to provide eco-friendly options.

[0053] The characteristic analysis unit can generate a 3D model in the characteristic analysis of a dish to allow the user to visually check the dish. For example, in the characteristic analysis of a dish, a 3D model is generated to allow the user to visually check the dish. For example, the shape and presentation of the dish are displayed in a 3D model. Furthermore, by generating a 3D model, the characteristic analysis unit allows the user to visually understand the characteristics of the dish. For example, the presentation and color balance of the dish are displayed in a 3D model. Furthermore, in the characteristic analysis of a dish, a 3D model is generated to allow the user to visually check the dish. For example, the shape and presentation of the dish are displayed in a 3D model. In this way, a 3D model is generated to allow the user to visually check the dish.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The input information collection unit collects photos of meals and impressions of users. For example, users can upload photos of meals and input impressions. The input information collection unit can also set limits on the resolution of photos and the number of characters in impressions. Step 2: The characteristic analysis unit analyzes the information collected by the input information collection unit. For example, the characteristic analysis unit analyzes the characteristics of the food from the photos using an image analysis algorithm. The characteristic analysis unit can also analyze the characteristics of the food from the impressions using text analysis technology. Step 3: The preference learning unit learns the user's preferences based on the information analyzed by the feature analysis unit. For example, the preference learning unit uses a machine learning algorithm to learn the user's preferences. The preference learning unit can also learn the user's preferences by taking into account the type of data set. Step 4: The suggestion unit suggests restaurants and dishes based on the user's preferences learned by the preference learning unit. For example, the suggestion unit considers restaurant selection criteria to suggest appropriate restaurants to the user. The suggestion unit can also consider the type of cuisine to suggest appropriate dishes to the user. This allows the restaurant suggestion system according to the embodiment to suggest appropriate restaurants and dishes based on the user's preferences. For example, if the user prefers healthy food, restaurants that offer healthy menus are suggested. Also, if the user prefers beautifully decorated dishes, restaurants that offer such dishes are suggested. Suggestions are made taking into account the user's current location, past visit history, etc.

[0056] (Example 2) The restaurant recommendation system according to an embodiment of the present invention is a system in which, when a user inputs photos of meals and their impressions, a generation AI uses the photos and impressions to suggest restaurants and dishes that the user likes. This allows the restaurant recommendation system to suggest appropriate restaurants and dishes based on the user's preferences.

[0057] A restaurant suggestion system according to an embodiment includes an input information collection unit, a characteristic analysis unit, a preference learning unit, and a suggestion unit. The input information collection unit collects photos of meals and impressions of users. For example, users can upload photos of meals and input impressions. The input information collection unit can also set a photo resolution and a character limit for impressions. The characteristic analysis unit analyzes the information collected by the input information collection unit. For example, the characteristic analysis unit can analyze characteristics of dishes from photos using an image analysis algorithm. The characteristic analysis unit can also analyze characteristics of dishes from impressions using text analysis technology. The preference learning unit learns user preferences based on the information analyzed by the characteristic analysis unit. For example, the preference learning unit learns user preferences using a machine learning algorithm. The preference learning unit can also learn user preferences by taking into account the type of dataset. The suggestion unit suggests restaurants and dishes based on the user preferences learned by the preference learning unit. For example, the suggestion unit can suggest appropriate restaurants to the user by taking into account restaurant selection criteria. The suggestion unit can also suggest appropriate dishes to the user by taking into account the type of dish. As a result, the restaurant recommendation system according to the embodiment can recommend appropriate restaurants and dishes based on the user's preferences. For example, if a user prefers healthy dishes, restaurants that offer healthy menus will be recommended. If a user prefers beautifully decorated dishes, restaurants that offer such dishes will be recommended. The recommendations are made taking into consideration the user's current location and past visit history.

[0058] The input information collection unit performs sentiment analysis on photos or impressions entered by users and can filter them based on the intensity or type of emotion. For example, the input information collection unit uses generative AI to perform sentiment analysis on photos and impressions of meals uploaded by users. For example, if the impressions indicate a strong positive emotion, that information is preferentially collected. The input information collection unit also performs sentiment analysis and quantifies the intensity and type of emotion. For example, if a user enters "This food was amazing," the emotion score is set high and the information is saved in a database. The input information collection unit also filters out input information with strong negative emotions based on the results of the sentiment analysis. For example, it will not collect impressions such as "This food was terrible." This allows information to be filtered based on the intensity and type of emotion.

[0059] The input information collection unit automatically adds nutritional value and calorie information for meals to user input information, thereby collecting information from the perspective of health management. For example, the input information collection unit uses AI to automatically add nutritional value and calorie information to photos of meals uploaded by users. For example, it analyzes the ingredients in the photos and displays their nutritional value. The input information collection unit also collects health management information for meals based on user input information. For example, if a user inputs, "This salad was healthy," the information is saved in a database. The input information collection unit also supports the user's health management by automatically adding nutritional value and calorie information. For example, if a user inputs, "This dish was good because it was low in calories," the information is collected. This allows the automatic addition of nutritional value and calorie information to support health management.

[0060] The input information collecting unit also collects environmental and background sounds when the user makes input, and can analyze the atmosphere and environment of the meal. For example, when the user uploads a photo of the meal, the input information collecting unit simultaneously collects environmental and background sounds. For example, it analyzes the music in the restaurant and the sounds of surrounding conversations. The input information collecting unit also analyzes the environmental and background sounds to understand the atmosphere and environment of the meal. For example, it analyzes the difference between eating in a quiet environment and eating in a lively environment. The input information collecting unit also adds the environmental and background sounds to the user's input information to understand the atmosphere of the meal in more detail. For example, if the user inputs, "The atmosphere of this restaurant was good," that information is collected. In this way, it is possible to collect environmental and background sounds and analyze the atmosphere and environment of the meal.

[0061] The input information collection unit also supports voice input or gesture input for user input information, thereby providing a wider variety of input methods. The input information collection unit, for example, allows the user to input their impressions of a meal by voice. For example, if the user inputs "This dish was delicious" by voice, the information is collected. The input information collection unit also allows the user to input their impressions of a meal using gesture input. For example, if the user makes a thumbs-up gesture, the information is collected as a positive impression. The input information collection unit also makes it easier for the user to input their impressions by using voice input or gesture input. For example, if the user inputs "This dish was excellent" by voice, the information is stored in a database. This allows for support of voice input and gesture input, providing a wider variety of input methods.

[0062] The input information collection unit can collect input information from users in different cultural spheres or regions and analyze preferences from a global perspective. The input information collection unit, for example, collects input information from users in different cultural spheres or regions and analyzes preferences from a global perspective. For example, it analyzes the differences in preferences between Japanese users and American users. The input information collection unit also analyzes the characteristics of cuisine based on input information from users in different cultural spheres or regions. For example, it analyzes the differences between Asian cuisine and European cuisine. The input information collection unit also analyzes preferences from a global perspective to make appropriate suggestions to users. For example, if a user inputs, "This dish had a Japanese taste and was delicious," that information is collected. This makes it possible to collect input information from users in different cultural spheres or regions and analyze preferences from a global perspective.

[0063] The input information collecting unit is equipped with an emotion estimation function, and can estimate the emotion of the user when inputting information in real time and make suggestions that elicit positive emotions. For example, when a user inputs their impressions of a meal, the input information collecting unit uses the emotion estimation function to estimate the emotion in real time. For example, the input information collecting unit analyzes the user's facial expressions and voice and calculates an emotion score. The input information collecting unit also uses the emotion estimation function to make suggestions that will cause the user to feel positive emotions. For example, if a user inputs "This dish was delicious," the input information collecting unit makes suggestions that reinforce that emotion. The input information collecting unit also provides an interface for estimating the user's emotions in real time and eliciting positive emotions. For example, if a user inputs "This dish was amazing," the input information collecting unit displays a message that reinforces that emotion. In this way, the emotion estimation function can be used to make suggestions that elicit positive emotions from the user.

[0064] The characteristic analysis unit can also use generative AI to analyze the history and cultural background of a dish, thereby enriching the information provided to the user. For example, in analyzing the characteristics of a dish, the characteristic analysis unit uses generative AI to analyze the history and cultural background of the dish. For example, it analyzes the origin of the dish and traditional cooking methods and provides that information to the user. In addition, in analyzing the characteristics of a dish, the characteristic analysis unit refers to related literature and materials to understand the cultural background of the dish. For example, it generates text that explains the history and cultural background of the dish. In analyzing the characteristics of a dish, the characteristic analysis unit uses generative AI to analyze the history and cultural background of the dish, and provides that information to the user. For example, it generates text that explains the origin of the dish and traditional cooking methods. This allows the history and cultural background of the dish to be analyzed, thereby enriching the information provided to the user.

[0065] The characteristic analysis unit can analyze the origin and production method of ingredients in the characteristic analysis of a dish and provide eco-friendly options. The characteristic analysis unit, for example, analyzes the origin and production method of ingredients in the characteristic analysis of a dish. For example, it analyzes whether organic ingredients or locally produced ingredients are used. The characteristic analysis unit also analyzes the origin and production method of ingredients to provide eco-friendly options. For example, if a user inputs, "I'm glad this dish uses organic ingredients," the characteristic analysis unit collects that information. The characteristic analysis unit can analyze the origin and production method of ingredients in the characteristic analysis of a dish and provide eco-friendly options. For example, if a user inputs, "I'm glad this dish uses locally produced ingredients," the characteristic analysis unit collects that information. This allows the origin and production method of ingredients to be analyzed and eco-friendly options to be provided.

[0066] The characteristic analysis unit can also use the emotion estimation function to analyze the emotional impact that a dish has on a user. For example, in analyzing the characteristics of a dish, the characteristic analysis unit uses the emotion estimation function to analyze the emotional impact that a dish has on a user. For example, if a user inputs, "This dish was delicious and made me feel happy," the emotion is analyzed. The characteristic analysis unit also uses the emotion estimation function to analyze the emotional impact that a dish has on a user. For example, if a user inputs, "This dish looked beautiful and moved me," the emotion is analyzed. The characteristic analysis unit also uses the emotion estimation function to analyze the emotional impact that a dish has on a user in analyzing the characteristics of a dish. For example, if a user inputs, "This dish had a nice aroma and was relaxing," the emotion is analyzed. In this way, the emotion estimation function can be used to analyze the emotional impact that a dish has on a user.

[0067] The characteristic analysis unit can generate a 3D model in the characteristic analysis of a dish, allowing the user to visually confirm the dish. For example, the characteristic analysis unit generates a 3D model in the characteristic analysis of a dish, allowing the user to visually confirm the dish. For example, the shape and presentation of the dish are displayed in a 3D model. Furthermore, by generating a 3D model, the characteristic analysis unit allows the user to visually understand the characteristics of the dish. For example, the presentation and color balance of the dish are displayed in a 3D model. Furthermore, the characteristic analysis unit generates a 3D model in the characteristic analysis of a dish, allowing the user to visually confirm the dish. For example, the shape and presentation of the dish are displayed in a 3D model. In this way, the 3D model is generated, allowing the user to visually confirm the dish.

[0068] The characteristic analysis unit can also analyze different cooking methods and recipe variations in the analysis of the characteristics of a dish and suggest them to the user. For example, the characteristic analysis unit can analyze different cooking methods and recipe variations in the analysis of the characteristics of a dish and suggest them to the user. For example, different cooking methods and recipes can be suggested for the same dish. The characteristic analysis unit can also analyze different cooking methods and recipe variations and suggest them to the user. For example, if a user inputs, "This dish was good when grilled," the characteristic analysis unit collects that information. The characteristic analysis unit can also analyze different cooking methods and recipe variations in the analysis of the characteristics of a dish and suggest them to the user. For example, if a user inputs, "This dish was good when stewed," the characteristic analysis unit collects that information. This allows different cooking methods and recipe variations to be analyzed and suggested to the user.

[0069] The characteristic analysis unit can use the emotion estimation function to collect the user's emotional reactions in the analysis of the characteristics of the dish and reflect them in the analysis results. The characteristic analysis unit, for example, uses the emotion estimation function to collect the user's emotional reactions in the analysis of the characteristics of the dish. For example, if a user inputs, "This dish was delicious and made me feel happy," the emotion is analyzed. The characteristic analysis unit also collects the user's emotional reactions and reflects them in the analysis results. For example, if a user inputs, "This dish looked beautiful and moved me," the emotion is analyzed. The characteristic analysis unit also uses the emotion estimation function to collect the user's emotional reactions in the analysis of the characteristics of the dish and reflects them in the analysis results. For example, if a user inputs, "This dish had a nice aroma and was relaxing," the emotion is analyzed. In this way, the emotion estimation function can be used to collect the user's emotional reactions and reflect them in the analysis results.

[0070] The preference learning unit can also incorporate the user's past dietary history and health data into its learning using the generation AI. For example, the preference learning unit collects the user's past dietary history and incorporates it into learning using the generation AI. For example, it learns preferences based on data on dishes the user has eaten in the past. The preference learning unit also collects the user's health data and incorporates it into learning using the generation AI. For example, it learns preferences taking into account the user's health condition and nutritional balance. The preference learning unit also learns more accurate preferences by incorporating the user's past dietary history and health data into its learning. For example, if the user inputs, "This dish was healthy and good," that information is collected. This makes it possible to incorporate the user's past dietary history and health data into its learning.

[0071] The preference learning unit can dynamically update the user's preferences by taking into account changes in seasons or weather when learning the user's preferences. The preference learning unit, for example, takes into account changes in seasons and weather when learning the user's preferences. For example, it learns that the user tends to prefer cold dishes in summer and hot dishes in winter. The preference learning unit also collects data on seasons and weather and dynamically updates the user's preferences. For example, it makes suggestions according to the weather, such as suggesting hot soup on a rainy day. The preference learning unit also builds a system that dynamically updates the user's preferences by taking into account changes in seasons and weather. For example, it learns seasonal trends in ingredients and dishes and reflects them in suggestions. This allows the user's preferences to be dynamically updated by taking into account changes in seasons and weather.

[0072] The preference learning unit can use the emotion estimation function to adjust the preferences according to changes in the user's emotions. For example, the preference learning unit uses the emotion estimation function to adjust preferences according to changes in the user's emotions. For example, if the user is feeling stressed, the preference learning unit can suggest dishes that will help them relax. The preference learning unit also collects emotional data about the user and adjusts preferences according to changes in the emotions. For example, if the user inputs, "This dish was delicious and made me feel happy," the preference learning unit learns this information. The preference learning unit also uses the emotion estimation function to build a system that adjusts preferences according to changes in the user's emotions. For example, if the user inputs, "This dish was beautiful to look at and moved me," the preference learning unit learns this information. In this way, the emotion estimation function can be used to adjust preferences according to changes in the user's emotions.

[0073] The preference learning unit can take into account the preferences of family and friends when learning the user's preferences and make group suggestions. For example, the preference learning unit can take into account the preferences of family and friends when learning the user's preferences. For example, it can suggest dishes that the whole family can enjoy. The preference learning unit can also collect the preferences of family and friends and make group suggestions. For example, if a friend inputs, "This dish was delicious," the unit learns that information. The preference learning unit can also build a system that takes into account the preferences of family and friends when learning the user's preferences. For example, it can suggest dishes that the whole family can enjoy. This allows it to take into account the preferences of family and friends when making group suggestions.

[0074] The preference learning unit synchronizes data between different devices when learning the user's preferences, thereby providing the seamless experience. The preference learning unit, for example, synchronizes data between different devices when learning the user's preferences. For example, it synchronizes data between a smartphone and a tablet, thereby providing a seamless experience. The preference learning unit also synchronizes data between different devices to consistently learn the user's preferences. For example, it makes it possible for information entered by a user on a smartphone to also be used on a tablet. The preference learning unit also builds a system that synchronizes data between different devices when learning the user's preferences. For example, it synchronizes data between a smartphone and a tablet, thereby providing a seamless experience. This allows data synchronization between different devices to provide a seamless experience.

[0075] The preference learning unit can use the emotion estimation function to reflect changes in the user's emotions in real time when learning the user's preferences, and adjust the suggestions accordingly. For example, the preference learning unit can use the emotion estimation function to reflect changes in the user's emotions in real time and adjust the suggestions accordingly. For example, if the user is feeling stressed, the preference learning unit can suggest dishes that will help them relax. The preference learning unit can also collect user emotion data in real time and adjust the suggestions accordingly. For example, if the user inputs, "This dish was delicious and made me feel happy," the preference learning unit learns this information. The preference learning unit can also use the emotion estimation function to build a system that reflects changes in the user's emotions in real time and adjusts the suggestions accordingly. For example, if the user inputs, "This dish was beautiful to look at and I was moved," the preference learning unit learns this information. In this way, the emotion estimation function can be used to reflect changes in the user's emotions in real time and adjust the suggestions accordingly.

[0076] The suggestion unit can use the generation AI to take into account the user's past reviews and ratings to improve the accuracy of the suggestions. For example, when suggesting restaurants or dishes, the suggestion unit uses the generation AI to take into account the user's past reviews and ratings. For example, it prioritizes suggesting restaurants that the user has given high ratings to in the past. The suggestion unit also collects the user's past reviews and ratings to improve the accuracy of the suggestions. For example, if a user inputs "This restaurant was delicious," the suggestion unit learns that information. The suggestion unit also uses the generation AI to build a system that takes into account the user's past reviews and ratings to improve the accuracy of the suggestions. For example, it prioritizes suggesting restaurants that the user has given high ratings to in the past. This allows the generation AI to take into account the user's past reviews and ratings and improve the accuracy of the suggestions.

[0077] The suggestion unit can collect user feedback on the suggestion content in real time and reflect it in the next suggestion. For example, when suggesting restaurants or dishes, the suggestion unit collects user feedback on the suggestion content in real time. For example, if a user inputs "This restaurant was delicious," the information is learned. The suggestion unit also collects user feedback in real time and reflects it in the next suggestion. For example, if a user inputs "This dish was delicious," the information is learned. The suggestion unit also builds a system that collects user feedback on the suggestion content in real time and reflects it in the next suggestion. For example, if a user inputs "This restaurant was delicious," the information is learned. In this way, user feedback on the suggestion content can be collected in real time and reflected in the next suggestion.

[0078] The suggestion unit can use the emotion estimation function to make suggestions based on the user's emotions. For example, when suggesting restaurants or dishes, the suggestion unit uses the emotion estimation function to make suggestions based on the user's emotions. For example, if the user is feeling stressed, the suggestion unit suggests a restaurant where the user can relax. The suggestion unit also uses the emotion estimation function to make suggestions based on the user's emotions. For example, if the user inputs, "This dish was delicious and made me feel happy," the suggestion unit makes a suggestion that reinforces that emotion. The suggestion unit also builds a system that uses the emotion estimation function to make suggestions based on the user's emotions when suggesting restaurants or dishes. For example, if the user inputs, "This dish looked beautiful and moved me," the suggestion unit makes a suggestion that reinforces that emotion. In this way, the emotion estimation function can be used to make suggestions based on the user's emotions.

[0079] The suggestion unit can make the optimal suggestion by taking into account the user's current location or travel route. The suggestion unit, for example, takes into account the user's current location and travel route when suggesting restaurants and dishes. For example, the user is suggested restaurants that are close to the user's current location. The suggestion unit also takes into account the user's current location and travel route to make the optimal suggestion. For example, the suggestion unit suggests restaurants that the user can stop by while traveling. The suggestion unit also builds a system that takes into account the user's current location and travel route when suggesting restaurants and dishes and makes the optimal suggestion. For example, the user is suggested restaurants that are close to the user's current location. This makes it possible to make the optimal suggestion by taking into account the user's current location and travel route.

[0080] The suggestion unit can link the suggested content with an SNS or a messaging app to make the sharing easier. For example, when suggesting restaurants or dishes, the suggestion unit links the suggested content with an SNS or a messaging app. For example, the user can share the suggested restaurant with friends. The suggestion unit also links the suggested content with an SNS or a messaging app to make the sharing easier. For example, the user can post the suggested dish on an SNS. The suggestion unit also builds a system when suggesting restaurants or dishes that links the suggested content with an SNS or a messaging app to make the sharing easier. For example, the user can share the suggested restaurant with friends. This makes it easier to link the suggested content with an SNS or a messaging app and share it.

[0081] The suggestion unit uses the emotion estimation function to collect the user's emotional reactions when suggesting restaurants and dishes, and can continuously improve the content of the suggestions. For example, the suggestion unit uses the emotion estimation function to collect the user's emotional reactions when suggesting restaurants and dishes. For example, if a user inputs "This restaurant was delicious," the emotion is analyzed. The suggestion unit also collects the user's emotional reactions and continuously improves the content of the suggestions. For example, if a user inputs "This dish was delicious," the emotion is analyzed. The suggestion unit also uses the emotion estimation function to collect the user's emotional reactions when suggesting restaurants and dishes, and builds a system that continuously improves the content of the suggestions. For example, if a user inputs "This restaurant was delicious," the emotion is analyzed. In this way, the emotion estimation function can be used to collect the user's emotional reactions and continuously improve the content of the suggestions.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The input information collection unit can also support voice input or gesture input for user input information, thereby providing a wider variety of input methods. For example, the input information collection unit allows the user to input their impressions of the meal by voice. For example, if the user voice-inputs, "This dish was delicious," that information is collected. The input information collection unit can also allow the user to input their impressions of the meal using gesture input. For example, if the user makes a thumbs-up gesture, that information is collected as a positive impression. The input information collection unit can also use voice input or gesture input to make it easier for the user to input their impressions. For example, if the user voice-inputs, "This dish was excellent," that information is saved in a database. This allows for voice input and gesture input, thereby providing a wider variety of input methods.

[0084] The input information collection unit performs sentiment analysis on photos or impressions entered by users and can filter based on the intensity or type of emotion. For example, generative AI is used to perform sentiment analysis on photos and impressions of meals uploaded by users. For example, if the impressions indicate a strong positive emotion, that information is preferentially collected. The input information collection unit also performs sentiment analysis and quantifies the intensity and type of emotion. For example, if a user enters "This food was amazing," the emotion score is set high and the information is saved in the database. The input information collection unit also filters out input information with strong negative emotions based on the results of the sentiment analysis. For example, it will not collect impressions such as "This food was terrible." This makes it possible to filter information based on the intensity and type of emotion.

[0085] The input information collection unit also collects environmental and background sounds when the user makes input, and can analyze the atmosphere and environment of the meal. For example, when a user uploads a photo of a meal, the environmental and background sounds are also collected at the same time. For example, the music in the restaurant and the sounds of surrounding conversations are analyzed. The input information collection unit also analyzes the environmental and background sounds to understand the atmosphere and environment of the meal. For example, it analyzes the difference between eating in a quiet environment and eating in a lively environment. The input information collection unit also adds the environmental and background sounds to the user's input information to understand the atmosphere of the meal in more detail. For example, if a user inputs, "The atmosphere of this restaurant was good," that information is collected. In this way, the environmental and background sounds can be collected and the atmosphere and environment of the meal can be analyzed.

[0086] The input information collection unit can collect input information from users of different cultural spheres or regions and analyze preferences from a global perspective. For example, input information from users of different cultural spheres or regions is collected and preferences from a global perspective is analyzed. For example, the input information collection unit analyzes the differences in preferences between Japanese users and American users. The input information collection unit also analyzes the characteristics of cuisine based on input information from users of different cultural spheres or regions. For example, the input information collection unit analyzes the differences between Asian cuisine and European cuisine. The input information collection unit also analyzes preferences from a global perspective and makes appropriate suggestions to users. For example, if a user inputs, "This dish had a Japanese taste and was delicious," that information is collected. This makes it possible to collect input information from users of different cultural spheres or regions and analyze preferences from a global perspective.

[0087] The input information collection unit automatically adds nutritional value and calorie information for meals to the information entered by the user, thereby collecting information from the perspective of health management. For example, AI automatically adds nutritional value and calorie information to photos of meals uploaded by the user. For example, it analyzes the ingredients in the photo and displays their nutritional value. The input information collection unit also collects health management information for meals based on the information entered by the user. For example, if a user enters "This salad was healthy," the information is saved in a database. The input information collection unit also supports the user's health management by automatically adding nutritional value and calorie information. For example, if a user enters "This dish was good because it was low in calories," the information is collected. This allows the automatic addition of nutritional value and calorie information to support health management.

[0088] The input information collection unit is equipped with an emotion estimation function, which can estimate the emotion of the user when inputting information in real time and make suggestions that elicit positive emotions. For example, when a user inputs their impressions of a meal, the emotion estimation function is used to estimate the emotion in real time. For example, the emotion score is calculated by analyzing the user's facial expressions and voice. The input information collection unit also uses the emotion estimation function to make suggestions that will cause the user to feel positive emotions. For example, if a user inputs "This dish was delicious," the input information collection unit makes suggestions that reinforce that emotion. The input information collection unit also provides an interface for estimating the user's emotions in real time and eliciting positive emotions. For example, if a user inputs "This dish was amazing," the input information collection unit displays a message that reinforces that emotion. In this way, the emotion estimation function can be used to make suggestions that will elicit positive emotions from the user.

[0089] The characteristic analysis unit can also use generative AI to analyze the history and cultural background of a dish, thereby enriching the information provided to the user. For example, in analyzing the characteristics of a dish, generative AI can be used to analyze the history and cultural background of the dish. For example, the origin of the dish and traditional cooking methods can be analyzed, and that information can be provided to the user. In addition, in analyzing the characteristics of a dish, the characteristic analysis unit can refer to related literature and materials to understand the cultural background of the dish. For example, it can generate text that explains the history and cultural background of the dish. In analyzing the characteristics of a dish, the characteristic analysis unit can use generative AI to analyze the history and cultural background of the dish, and provide that information to the user. For example, it can generate text that explains the origin of the dish and traditional cooking methods. This allows the history and cultural background of the dish to be analyzed, thereby enriching the information provided to the user.

[0090] In analyzing the characteristics of a dish, the characteristic analysis unit may also analyze the origin and production method of ingredients to provide eco-friendly options. For example, in analyzing the characteristics of a dish, the characteristic analysis unit may analyze the origin and production method of ingredients. For example, it may analyze whether organic or locally produced ingredients are used. The characteristic analysis unit may also analyze the origin and production method of ingredients to provide eco-friendly options. For example, if a user inputs, "I'm glad this dish uses organic ingredients," the characteristic analysis unit may collect that information. In analyzing the characteristics of a dish, the characteristic analysis unit may also analyze the origin and production method of ingredients to provide eco-friendly options. For example, if a user inputs, "I'm glad this dish uses locally produced ingredients," the characteristic analysis unit may collect that information. This may analyze the origin and production method of ingredients to provide eco-friendly options.

[0091] The characteristic analysis unit can also use the emotion estimation function to analyze the emotional impact that a dish has on a user. For example, in analyzing the characteristics of a dish, the emotion estimation function is used to analyze the emotional impact that a dish has on a user. For example, if a user inputs, "This dish was delicious and made me feel happy," the emotion is analyzed. The characteristic analysis unit also uses the emotion estimation function to analyze the emotional impact that a dish has on a user. For example, if a user inputs, "This dish looked beautiful and moved me," the emotion is analyzed. The characteristic analysis unit also uses the emotion estimation function to analyze the emotional impact that a dish has on a user in analyzing the characteristics of a dish. For example, if a user inputs, "This dish had a nice aroma and was relaxing," the emotion is analyzed. In this way, the emotion estimation function can be used to analyze the emotional impact that a dish has on a user.

[0092] The characteristic analysis unit can generate a 3D model in the characteristic analysis of a dish to allow the user to visually check the dish. For example, in the characteristic analysis of a dish, a 3D model is generated to allow the user to visually check the dish. For example, the shape and presentation of the dish are displayed in a 3D model. Furthermore, by generating a 3D model, the characteristic analysis unit allows the user to visually understand the characteristics of the dish. For example, the presentation and color balance of the dish are displayed in a 3D model. Furthermore, in the characteristic analysis of a dish, a 3D model is generated to allow the user to visually check the dish. For example, the shape and presentation of the dish are displayed in a 3D model. In this way, a 3D model is generated to allow the user to visually check the dish.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The input information collection unit collects photos of meals and impressions of users. For example, users can upload photos of meals and input impressions. The input information collection unit can also set limits on the resolution of photos and the number of characters in impressions. Step 2: The characteristic analysis unit analyzes the information collected by the input information collection unit. For example, the characteristic analysis unit analyzes the characteristics of the food from the photos using an image analysis algorithm. The characteristic analysis unit can also analyze the characteristics of the food from the impressions using text analysis technology. Step 3: The preference learning unit learns the user's preferences based on the information analyzed by the feature analysis unit. For example, the preference learning unit uses a machine learning algorithm to learn the user's preferences. The preference learning unit can also learn the user's preferences by taking into account the type of data set. Step 4: The suggestion unit suggests restaurants and dishes based on the user's preferences learned by the preference learning unit. For example, the suggestion unit considers restaurant selection criteria to suggest appropriate restaurants to the user. The suggestion unit can also consider the type of cuisine to suggest appropriate dishes to the user. This allows the restaurant suggestion system according to the embodiment to suggest appropriate restaurants and dishes based on the user's preferences. For example, if the user prefers healthy food, restaurants that offer healthy menus are suggested. Also, if the user prefers beautifully decorated dishes, restaurants that offer such dishes are suggested. Suggestions are made taking into account the user's current location, past visit history, etc.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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]

[0162] 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. an input information collection unit that collects photos and impressions of meals taken by users; a characteristic analysis unit that analyzes the information collected by the input information collection unit; a preference learning unit that learns user preferences based on the information analyzed by the characteristic analysis unit; a suggestion unit that suggests restaurants and dishes based on the user's preferences learned by the preference learning unit. A system characterized by:

2. The input information collection unit Perform sentiment analysis on user-entered photos or comments, and filter based on the intensity or type of sentiment.

2. The system of claim 1.

3. The input information collection unit Automatically add nutritional and calorie information to the user's input, and collect information from a health management perspective.

2. The system of claim 1.

4. The input information collection unit The system also collects ambient and background sounds while the user is typing to analyze the atmosphere and environment of the meal.

2. The system of claim 1.

5. The input information collection unit Supports voice input or gesture input for user input information, providing more diverse input methods 2. The system of claim 1.

6. The input information collection unit Collect input from users across different cultures or regions to analyze global preferences 2. The system of claim 1.

7. The input information collection unit Equipped with an emotion estimation function, it estimates the user's emotions in real time as they input, and makes suggestions that elicit positive emotions.

2. The system of claim 1.

8. The characteristic analysis unit Generative AI will be used to analyze the history and cultural background of the cuisine, enriching the information provided to the user.

2. The system of claim 1.

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