system

Abstract

An object of the system according to the embodiment is to enable an elderly person to easily learn a recipe using a local special product or a seasonal food material and to understand a cooking procedure or knack.SOLUTION: A system according to an embodiment includes a recipe proposing unit, a procedure teaching unit, an interaction interface unit, and a display unit. The recipe proposal unit proposes a recipe using local specialties or seasonal ingredients to the elderly person. The procedure giver gives a cooking procedure or a knack based on the recipe proposed by the recipe proposer. The interaction interface section provides an appropriate answer to the elderly person's question or question about the procedure or the knack given by the procedure giver section. The display unit displays the answer provided by the interaction interface unit using a TV monitor or a screen of a dedicated terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult for elderly people to easily learn recipes that use local specialties and seasonal ingredients, and to understand the cooking steps and tips.

[0005] The system of the embodiment aims to enable elderly people to easily learn recipes using local specialties and seasonal ingredients and understand cooking procedures and tips. [Means for solving the problem]

[0006] The system according to the embodiment includes a recipe suggestion unit, a procedure teaching unit, a dialogue interface unit, and a display unit. The recipe suggestion unit suggests recipes to the elderly using local specialties or seasonal ingredients. The procedure teaching unit teaches cooking procedures or tips based on the recipes suggested by the recipe suggestion unit. The dialogue interface unit provides appropriate answers to questions or doubts the elderly may have about the procedures or tips taught by the procedure teaching unit. The display unit displays the answers provided by the dialogue interface unit on a TV monitor or the screen of a dedicated terminal. [Effects of the Invention]

[0007] The system according to the embodiment allows elderly people to easily learn recipes using local specialties and seasonal ingredients, and understand cooking procedures and tips. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The service according to the embodiment of the present invention proposes recipes using local specialties and seasonal ingredients to elderly people through an AI avatar, and teaches cooking procedures and tips. This allows elderly people to experience local food culture and learn new ways to enjoy cooking.

[0029] The service according to the embodiment includes a recipe suggestion unit, a step-by-step instruction unit, a dialogue interface unit, and a display unit. The recipe suggestion unit suggests recipes to seniors using local specialties and seasonal ingredients. For example, the generation AI generates recipes based on prompts containing information about ingredients appropriate to the user's preferences, health condition, and the season. The generation AI suggests, for example, "Today, let's make a salad using fresh local tomatoes." The step-by-step instruction unit provides cooking steps and tips based on the suggested recipe. For example, the generation AI explains specific steps such as, "First, wash the tomatoes and cut them into bite-sized pieces. Then, add olive oil and salt and mix." The generation AI also provides advice such as, "Tomatoes taste even better if you refrigerate them before slicing them." The dialogue interface unit provides appropriate answers to questions and concerns from seniors. For example, the generation AI responds to the question, "Do I need to peel the tomatoes?" with, "No, you can use them as they are." The display unit provides services using a TV monitor or a dedicated device screen. For example, the service can be easily operated using a remote control or touch panel. In addition, speech-to-text technology is used to enable voice communication. This allows elderly people to enjoy content while interacting with an AI avatar in a natural way. As a result, the service according to this embodiment allows elderly people to experience local food culture and learn new ways to enjoy cooking. For example, by trying recipes suggested by the AI ​​avatar, they can discover new ways to enjoy cooking and lead a healthier diet. Furthermore, using local ingredients is expected to contribute to revitalizing the local economy.

[0030] The recipe suggestion unit can save the user's past cooking history in a database and suggest recipes based on that information. For example, the recipe suggestion unit can analyze trends in dishes made in the past and suggest new recipes using similar ingredients and cooking methods. This makes it possible to suggest more personalized recipes based on the user's past cooking history.

[0031] The recipe suggestion unit can display the nutritional value or allergy information of ingredients in real time when proposing a recipe. The recipe suggestion unit, for example, builds a system that displays the nutritional value of ingredients used in a recipe suggestion in real time. For example, nutritional information such as calories, protein, and vitamins can be displayed to enable the user to make healthy choices. This supports the user in making healthy choices.

[0032] The recipe suggestion unit can link to producer information or purchasing locations of local ingredients, thereby promoting the revitalization of local economies. For example, the recipe suggestion unit displays producer information of ingredients used when proposing recipes, allowing users to support local producers. For example, it links to information on local farmers and fishermen and guides users to purchasing locations. This promotes the revitalization of local economies.

[0033] The recipe suggestion unit can suggest recipes based on the user's cultural background or religious restrictions. The recipe suggestion unit, for example, builds a system that suggests recipes that take the user's cultural background into consideration. For example, it suggests traditional dishes from a specific culture or region. This makes it possible to suggest recipes that meet the diverse needs of users.

[0034] The cooking procedure teaching unit can customize cooking procedures based on the user's cooking skill level. For example, the cooking procedure teaching unit registers the user's cooking skill level in a database and customizes cooking procedures based on that information. For example, it may suggest simple procedures for beginners and more advanced procedures for advanced cooks. This makes it possible to provide cooking procedures that are suitable for everyone from beginners to advanced cooks.

[0035] The recipe instruction unit can include suggestions for food preservation methods or substitute ingredients in the cooking recipe. For example, the recipe instruction unit can include food preservation methods in the cooking recipe to reduce waste. For example, it can suggest ways to preserve leftover ingredients. This reduces food waste and enables efficient cooking.

[0036] The step-by-step instruction unit can display cooking steps as 3D models or animations to make them visually easier to understand. For example, the step-by-step instruction unit displays cooking steps as 3D models, building a system that makes it easier for users to understand visually. For example, the method of cutting ingredients and cooking methods are shown using 3D models. This makes it easier for users to visually understand cooking steps.

[0037] The recipe instruction unit can introduce local traditional cooking methods or historical background to the cooking instructions. For example, the recipe instruction unit can include local traditional cooking methods in the cooking instructions, allowing the user to deepen their cultural understanding. For example, the recipe instruction unit can introduce cooking methods for local traditional dishes. This allows the user to deepen their understanding of the local food culture.

[0038] The dialogue interface unit can analyze the tone or speed of the user's voice to realize a more natural dialogue. For example, the dialogue interface unit can analyze the tone or speed of the user's voice to build a system that improves the naturalness of the dialogue. For example, it can adjust the voice of an AI avatar to match the tone of the user's voice. This makes the dialogue with the user more natural.

[0039] The dialogue interface unit can recognize the user's facial expressions or gestures and provide appropriate feedback. The dialogue interface unit, for example, builds a system that recognizes the user's facial expressions and gestures and adjusts the content of the dialogue. For example, if the user is confused, a detailed explanation can be provided. This makes it possible to provide feedback according to the user's facial expressions and gestures.

[0040] The dialogue interface unit can support multiple languages. For example, the dialogue interface unit can add support for multiple languages ​​to build a system that can also accommodate international users. For example, the dialogue interface unit can support languages ​​such as English, French, and Chinese. This makes it possible to accommodate international users.

[0041] The display unit can add a customization function based on the user's eyesight or hearing ability. The display unit adds a customization function based on the user's eyesight to, for example, a TV monitor or dedicated terminal. For example, it provides a function to adjust the character size and contrast. This makes it possible to customize the display based on the user's eyesight or hearing ability.

[0042] The display unit can add a timer or reminder function during cooking. For example, the display unit can add a timer function during cooking to a TV monitor or dedicated terminal, allowing the user to manage the cooking time. For example, a timer can be set for each cooking step. This makes it easier for the user to manage the progress of cooking.

[0043] The display unit can add a function for linking with other smart home appliances. For example, the display unit can add a function for linking with other smart home appliances to a TV monitor or dedicated terminal, creating a system that optimizes the cooking environment. For example, it can link with a smart oven or smart refrigerator. This can optimize the cooking environment.

[0044] The display unit can display the user's health data and support dietary management. For example, the display unit can be added to a TV monitor or dedicated terminal with a function to display the user's health data, creating a system that supports dietary management. For example, it can display calorie intake and nutritional balance. This can support dietary management based on the user's health data.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The recipe suggestion unit can also suggest recipes using local specialties based on the user's past travel history and information about the areas visited. For example, by suggesting recipes using local specialties from areas the user has visited in the past, the user can recreate their travel memories through cooking. Also, by suggesting recipes using local specialties from areas the user is interested in, the user can be provided with the enjoyment of new cooking. Furthermore, by suggesting recipes using local specialties from areas the user wants to visit in the future, the system can also serve as a reference when planning a trip.

[0047] The recipe suggestion unit can also suggest recipes using ingredients in the refrigerator based on the user's ingredient purchasing history. For example, based on ingredients recently purchased by the user, it can suggest recipes that use up those ingredients without waste. Also, by suggesting new recipes using ingredients that the user frequently purchases, it can increase the variety of dishes. Furthermore, by taking into account the expiration dates of ingredients purchased by the user and suggesting recipes that use up those ingredients as soon as possible, it can reduce food waste.

[0048] The recipe suggestion unit can also suggest recipes that are fortified with specific nutrients based on the user's health data. For example, it can suggest recipes that use ingredients rich in vitamins and minerals depending on the user's health condition. Also, if the user needs to consume a specific nutrient, it can support a healthy diet by suggesting recipes that use ingredients rich in that nutrient. Furthermore, it can support health management by suggesting recipes that are low in calories and carbohydrates depending on the user's health goals.

[0049] The recipe suggestion unit can also suggest safe recipes based on the user's ingredient allergy information. For example, if a user is allergic to a particular ingredient, it can suggest recipes that do not use that ingredient. It can also suggest recipes that use alternative ingredients to the ingredient the user is allergic to, allowing the user to enjoy cooking safely. Furthermore, by storing information about ingredients the user is allergic to in a database and reflecting this information in future recipe suggestions, it becomes possible to suggest even safer recipes.

[0050] The recipe suggestion unit can also suggest recipes using ingredients that can be stored for a long period of time based on the user's method for storing ingredients. For example, by suggesting recipes using ingredients that can be stored for a long period of time, it is possible to reduce food waste. Furthermore, by suggesting storage methods for ingredients that the user does not know how to store, it is possible to maintain the freshness of the ingredients. Furthermore, by suggesting recipes that utilize storage methods for ingredients that the user knows how to store, it is possible to support efficient cooking.

[0051] The recipe suggestion unit can also suggest recipes using local specialties based on the user's information on where to purchase ingredients. For example, suggesting recipes using ingredients purchased by the user from local markets or farms can help revitalize the local economy. Also, by allowing users to try recipes using local specialties, it can provide an opportunity for them to experience local food culture. Furthermore, users can rediscover the appeal of local ingredients through recipes using local specialties.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The recipe suggestion unit suggests recipes to seniors using local specialties and seasonal ingredients. For example, the generation AI generates recipes based on prompts that include information on the user's preferences, health status, and seasonal ingredients. The generation AI might suggest something like, "Today, let's make a salad using fresh local tomatoes." Step 2: The step-by-step instruction section provides cooking steps and tips based on the proposed recipe. For example, the generative AI explains specific steps such as, "First, wash the tomatoes and cut them into bite-sized pieces. Then, add olive oil and salt and mix." It also gives advice such as, "If you refrigerate the tomatoes before cutting them, they will taste even better." Step 3: The dialogue interface provides appropriate answers to the elderly person's questions. For example, the generative AI responds to the question, "Do I need to peel the tomato?" with, "No, it's fine to use it as is." Step 4: The display unit provides services using a TV monitor or dedicated device screen. For example, it can be easily operated using a remote control or touch panel. Speech-to-text technology can also be used to enable voice communication. This allows elderly people to enjoy content while interacting naturally with an AI avatar.

[0054] (Example 2) The service according to the embodiment of the present invention proposes recipes using local specialties and seasonal ingredients to elderly people through an AI avatar, and teaches cooking procedures and tips. This allows elderly people to experience local food culture and learn new ways to enjoy cooking.

[0055] The service according to the embodiment includes a recipe suggestion unit, a step-by-step instruction unit, a dialogue interface unit, and a display unit. The recipe suggestion unit suggests recipes to seniors using local specialties and seasonal ingredients. For example, the generation AI generates recipes based on prompts containing information about ingredients appropriate to the user's preferences, health condition, and the season. The generation AI suggests, for example, "Today, let's make a salad using fresh local tomatoes." The step-by-step instruction unit provides cooking steps and tips based on the suggested recipe. For example, the generation AI explains specific steps such as, "First, wash the tomatoes and cut them into bite-sized pieces. Then, add olive oil and salt and mix." The generation AI also provides advice such as, "Tomatoes taste even better if you refrigerate them before slicing them." The dialogue interface unit provides appropriate answers to questions and concerns from seniors. For example, the generation AI responds to the question, "Do I need to peel the tomatoes?" with, "No, you can use them as they are." The display unit provides services using a TV monitor or a dedicated device screen. For example, the service can be easily operated using a remote control or touch panel. In addition, speech-to-text technology is used to enable voice communication. This allows elderly people to enjoy content while interacting with an AI avatar in a natural way. As a result, the service according to this embodiment allows elderly people to experience local food culture and learn new ways to enjoy cooking. For example, by trying recipes suggested by the AI ​​avatar, they can discover new ways to enjoy cooking and lead a healthier diet. Furthermore, using local ingredients is expected to contribute to revitalizing the local economy.

[0056] The recipe suggestion unit can save the user's past cooking history in a database and suggest recipes based on that information. For example, the recipe suggestion unit can analyze trends in dishes made in the past and suggest new recipes using similar ingredients and cooking methods. This makes it possible to suggest more personalized recipes based on the user's past cooking history.

[0057] The recipe suggestion unit can display the nutritional value or allergy information of ingredients in real time when proposing a recipe. The recipe suggestion unit, for example, builds a system that displays the nutritional value of ingredients used in a recipe suggestion in real time. For example, nutritional information such as calories, protein, and vitamins can be displayed to enable the user to make healthy choices. This supports the user in making healthy choices.

[0058] The recipe suggestion unit can suggest recipes based on the user's mood using the emotion estimation function. For example, the recipe suggestion unit analyzes the user's mood using the emotion estimation function and suggests recipes that match that mood. For example, when the user wants to relax, it suggests simple and easy recipes. This makes it possible to suggest recipes that match the user's mood.

[0059] The recipe suggestion unit can link to producer information or purchasing locations of local ingredients, thereby promoting the revitalization of local economies. For example, the recipe suggestion unit displays producer information of ingredients used when proposing recipes, allowing users to support local producers. For example, it links to information on local farmers and fishermen and guides users to purchasing locations. This promotes the revitalization of local economies.

[0060] The recipe suggestion unit can suggest recipes based on the user's cultural background or religious restrictions. The recipe suggestion unit, for example, builds a system that suggests recipes that take the user's cultural background into consideration. For example, it suggests traditional dishes from a specific culture or region. This makes it possible to suggest recipes that meet the diverse needs of users.

[0061] The recipe suggestion unit can use the emotion estimation function to identify the ingredients or cooking genres that the user is most interested in and suggest recipes based on that information. For example, the recipe suggestion unit can use the emotion estimation function to identify the ingredients that the user is most interested in and suggest recipes using those ingredients. For example, ingredients that the user is interested in can be used preferentially. This makes it possible to suggest recipes that match the user's interests.

[0062] The cooking procedure teaching unit can customize cooking procedures based on the user's cooking skill level. For example, the cooking procedure teaching unit registers the user's cooking skill level in a database and customizes cooking procedures based on that information. For example, it may suggest simple procedures for beginners and more advanced procedures for advanced cooks. This makes it possible to provide cooking procedures that are suitable for everyone from beginners to advanced cooks.

[0063] The recipe instruction unit can include suggestions for food preservation methods or substitute ingredients in the cooking recipe. For example, the recipe instruction unit can include food preservation methods in the cooking recipe to reduce waste. For example, it can suggest ways to preserve leftover ingredients. This reduces food waste and enables efficient cooking.

[0064] The procedure teaching unit can use the emotion estimation function to identify a procedure that the user finds difficult and provide supplementary explanations or videos. The procedure teaching unit can, for example, use the emotion estimation function to identify a procedure that the user finds difficult and provide supplementary explanations for that procedure. For example, it can provide detailed explanations for the procedure that the user finds difficult. This allows the user to receive support for the procedure that they find difficult with supplementary explanations or videos.

[0065] The step-by-step instruction unit can display cooking steps as 3D models or animations to make them visually easier to understand. For example, the step-by-step instruction unit displays cooking steps as 3D models, building a system that makes it easier for users to understand visually. For example, the method of cutting ingredients and cooking methods are shown using 3D models. This makes it easier for users to visually understand cooking steps.

[0066] The recipe instruction unit can introduce local traditional cooking methods or historical background to the cooking instructions. For example, the recipe instruction unit can include local traditional cooking methods in the cooking instructions, allowing the user to deepen their cultural understanding. For example, the recipe instruction unit can introduce cooking methods for local traditional dishes. This allows the user to deepen their understanding of the local food culture.

[0067] The step-teaching unit can use the emotion estimation function to identify the step that the user enjoys most and emphasize that step. The step-teaching unit, for example, uses the emotion estimation function to identify the step that the user enjoys most and builds a system that emphasizes that step. For example, the step that the user enjoys is explained in detail. In this way, the enjoyment of cooking is amplified by emphasizing the step that the user enjoys.

[0068] The dialogue interface unit can analyze the tone or speed of the user's voice to realize a more natural dialogue. For example, the dialogue interface unit can analyze the tone or speed of the user's voice to build a system that improves the naturalness of the dialogue. For example, it can adjust the voice of an AI avatar to match the tone of the user's voice. This makes the dialogue with the user more natural.

[0069] The dialogue interface unit can recognize the user's facial expressions or gestures and provide appropriate feedback. The dialogue interface unit, for example, builds a system that recognizes the user's facial expressions and gestures and adjusts the content of the dialogue. For example, if the user is confused, a detailed explanation can be provided. This makes it possible to provide feedback according to the user's facial expressions and gestures.

[0070] The dialogue interface unit can adjust the dialogue content based on the user's emotional state using the emotion estimation function. For example, the dialogue interface unit analyzes the user's emotional state using the emotion estimation function and adjusts the dialogue content based on the results. For example, if the user is feeling stressed, the dialogue interface unit provides content that helps the user to relax. This makes it possible to provide dialogue content that suits the user's emotional state.

[0071] The dialogue interface unit can support multiple languages. For example, the dialogue interface unit can add support for multiple languages ​​to build a system that can also accommodate international users. For example, the dialogue interface unit can support languages ​​such as English, French, and Chinese. This makes it possible to accommodate international users.

[0072] The dialogue interface unit can use the emotion estimation function to identify a dialogue style that makes the user most relaxed and adopt that style. The dialogue interface unit, for example, uses the emotion estimation function to identify a dialogue style that makes the user most relaxed and builds a system that adopts that style. For example, it adopts a speaking style or tone that makes the user feel relaxed. This makes it possible to provide a dialogue style that makes the user feel relaxed.

[0073] The display unit can add a customization function based on the user's eyesight or hearing ability. The display unit adds a customization function based on the user's eyesight to, for example, a TV monitor or dedicated terminal. For example, it provides a function to adjust the character size and contrast. This makes it possible to customize the display based on the user's eyesight or hearing ability.

[0074] The display unit can add a timer or reminder function during cooking. For example, the display unit can add a timer function during cooking to a TV monitor or dedicated terminal, allowing the user to manage the cooking time. For example, a timer can be set for each cooking step. This makes it easier for the user to manage the progress of cooking.

[0075] The display unit can use the emotion estimation function to automatically adjust the screen layout or audio settings that the user finds most comfortable. For example, the display unit uses the emotion estimation function to identify the screen layout that the user finds most comfortable and builds a system that automatically adjusts that layout. For example, it sets a character size or color scheme that the user finds comfortable. This makes it possible to provide a screen layout and audio settings that the user finds comfortable.

[0076] The display unit can add a function for linking with other smart home appliances. For example, the display unit can add a function for linking with other smart home appliances to a TV monitor or dedicated terminal, creating a system that optimizes the cooking environment. For example, it can link with a smart oven or smart refrigerator. This can optimize the cooking environment.

[0077] The display unit can display the user's health data and support dietary management. For example, the display unit can be added to a TV monitor or dedicated terminal with a function to display the user's health data, creating a system that supports dietary management. For example, it can display calorie intake and nutritional balance. This can support dietary management based on the user's health data.

[0078] The display unit can use the emotion estimation function to preferentially display content that the user is most interested in. For example, the display unit uses the emotion estimation function to identify content that the user is most interested in, and builds a system that preferentially displays that content. For example, recipes or cooking videos that the user is interested in can be displayed. This allows the content that the user is most interested in to be preferentially displayed.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The recipe suggestion unit can also suggest recipes using local specialties based on the user's past travel history and information about the areas visited. For example, by suggesting recipes using local specialties from areas the user has visited in the past, the user can recreate their travel memories through cooking. Also, by suggesting recipes using local specialties from areas the user is interested in, the user can be provided with the enjoyment of new cooking. Furthermore, by suggesting recipes using local specialties from areas the user wants to visit in the future, the system can also serve as a reference when planning a trip.

[0081] The recipe suggestion unit can also suggest recipes using ingredients in the refrigerator based on the user's ingredient purchasing history. For example, based on ingredients recently purchased by the user, it can suggest recipes that use up those ingredients without waste. Also, by suggesting new recipes using ingredients that the user frequently purchases, it can increase the variety of dishes. Furthermore, by taking into account the expiration dates of ingredients purchased by the user and suggesting recipes that use up those ingredients as soon as possible, it can reduce food waste.

[0082] The recipe suggestion unit can also suggest recipes that are fortified with specific nutrients based on the user's health data. For example, it can suggest recipes that use ingredients rich in vitamins and minerals depending on the user's health condition. Also, if the user needs to consume a specific nutrient, it can support a healthy diet by suggesting recipes that use ingredients rich in that nutrient. Furthermore, it can support health management by suggesting recipes that are low in calories and carbohydrates depending on the user's health goals.

[0083] The recipe suggestion unit can also use the emotion estimation function to analyze the user's stress level and suggest recipes using ingredients with a relaxing effect. For example, if the user is feeling stressed, it can suggest recipes using herbs and spices with a relaxing effect. Also, when the user wants to relax, it can suggest simple and easy recipes, providing time for relaxation through cooking. Furthermore, if the user is seeking relaxation, it can support the relaxation of the mind and body by suggesting recipes using ingredients with aromatherapy effects.

[0084] The recipe suggestion unit can also suggest safe recipes based on the user's ingredient allergy information. For example, if a user is allergic to a particular ingredient, it can suggest recipes that do not use that ingredient. It can also suggest recipes that use alternative ingredients to the ingredient the user is allergic to, allowing the user to enjoy cooking safely. Furthermore, by storing information about ingredients the user is allergic to in a database and reflecting this information in future recipe suggestions, it becomes possible to suggest even safer recipes.

[0085] The recipe suggestion unit can also use the emotion estimation function to analyze the user's sense of happiness and suggest recipes using ingredients that enhance that sense of happiness. For example, if the user is feeling happy, the system can suggest recipes using ingredients that enhance happiness, such as chocolate and berries, to further enhance that feeling. Also, if the user is seeking happiness, the system can provide a sense of happiness through cooking by suggesting recipes using ingredients that have the effect of enhancing happiness. Furthermore, by storing information about ingredients that make the user feel happy in a database and reflecting this in future recipe suggestions, more personalized recipe suggestions become possible.

[0086] The recipe suggestion unit can also suggest recipes using ingredients that can be stored for a long period of time based on the user's method for storing ingredients. For example, by suggesting recipes using ingredients that can be stored for a long period of time, it is possible to reduce food waste. Furthermore, by suggesting storage methods for ingredients that the user does not know how to store, it is possible to maintain the freshness of the ingredients. Furthermore, by suggesting recipes that utilize storage methods for ingredients that the user knows how to store, it is possible to support efficient cooking.

[0087] The recipe suggestion unit can also use the emotion estimation function to suggest recipes that bring out specific emotions based on the user's emotional state. For example, when a user wants to feel energized, it can suggest recipes that use ingredients that have an energy-boosting effect. When a user wants to relax, it can suggest recipes that use ingredients that have a relaxing effect, allowing the user to control emotions through cooking. Furthermore, when a user desires a specific emotion, it can suggest recipes that use ingredients that have the effect of bringing out that emotion, allowing the user to maintain emotional balance.

[0088] The recipe suggestion unit can also suggest recipes using local specialties based on the user's information on where to purchase ingredients. For example, suggesting recipes using ingredients purchased by the user from local markets or farms can help revitalize the local economy. Also, by allowing users to try recipes using local specialties, it can provide an opportunity for them to experience local food culture. Furthermore, users can rediscover the appeal of local ingredients through recipes using local specialties.

[0089] The recipe suggestion unit can also use the emotion estimation function to suggest recipes to alleviate specific emotions based on the user's emotional state. For example, if the user is feeling stressed, the recipe suggestion unit can suggest recipes using ingredients that have the effect of relieving stress. If the user is feeling sad, the recipe suggestion unit can suggest recipes using ingredients that have the effect of brightening the mood, thereby helping the user to alleviate their emotions through cooking. Furthermore, the recipe suggestion unit can suggest recipes using ingredients that have the effect of stabilizing emotions in order to alleviate specific emotions, helping the user maintain emotional balance.

[0090] The processing flow of the second embodiment will be briefly explained below.

[0091] Step 1: The recipe suggestion unit suggests recipes to seniors using local specialties and seasonal ingredients. For example, the generation AI generates recipes based on prompts that include information on the user's preferences, health status, and seasonal ingredients. The generation AI might suggest something like, "Today, let's make a salad using fresh local tomatoes." Step 2: The step-by-step instruction section provides cooking steps and tips based on the proposed recipe. For example, the generative AI explains specific steps such as, "First, wash the tomatoes and cut them into bite-sized pieces. Then, add olive oil and salt and mix." It also gives advice such as, "If you refrigerate the tomatoes before cutting them, they will taste even better." Step 3: The dialogue interface provides appropriate answers to the elderly person's questions. For example, the generative AI responds to the question, "Do I need to peel the tomato?" with, "No, it's fine to use it as is." Step 4: The display unit provides services using a TV monitor or dedicated device screen. For example, it can be easily operated using a remote control or touch panel. Speech-to-text technology can also be used to enable voice communication. This allows elderly people to enjoy content while interacting naturally with an AI avatar.

[0092] 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.

[0093] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0094] 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.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0136] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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."

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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]

[0159] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A recipe suggestion department that suggests recipes to seniors using local specialties or seasonal ingredients; a recipe suggestion unit that teaches cooking procedures or tips based on the recipes proposed by the recipe suggestion unit; a dialogue interface unit that provides appropriate answers to questions or doubts of the elderly person regarding the procedures or tips taught by the procedure teaching unit; a display unit that displays the answer provided by the dialogue interface unit on a TV monitor or a dedicated terminal screen; A system characterized by:

2. The recipe suggestion unit When suggesting recipes, display the nutritional value or allergy information of the ingredients in real time 2. The system of claim 1.

3. The recipe suggestion unit Linking information on producers or purchasing locations of the local ingredients will promote the revitalization of the local economy.

2. The system of claim 1.

4. The procedure teaching unit Customize cooking instructions based on the user's cooking skill level 2. The system of claim 1.

5. The dialogue interface unit Analyze the tone or rate of your voice to enable more natural conversations 2. The system of claim 1.

6. The display unit Add customization based on the user's vision or hearing ability 2. The system of claim 1.

7. The recipe suggestion unit Suggest recipes based on the user's mood 2. The system of claim 1.

8. The dialogue interface unit Adjusting dialogue based on the user's emotional state 2. The system of claim 1.

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