System

Abstract

An object of a system according to an embodiment is to propose an appropriate recipe by utilizing visual information and visually support a cooking procedure.SOLUTION: A system according to an embodiment includes a visual input unit, an analysis unit, a recipe suggestion unit, a cooking procedure support unit, and a voice question and answer unit. The visual input unit acquires visual information. The analysis unit analyzes the visual information acquired by the visual input unit. The recipe proposal unit proposes an appropriate recipe based on the information analyzed by the analysis unit. The cooking procedure support unit visually supports a cooking procedure based on the recipe proposed by the recipe proposal unit. The voice question-and-answer unit provides an answer to the voice question.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide systems that utilize visual information to suggest appropriate recipes and support cooking procedures, and there is room for improvement.

[0005] The system according to the embodiment aims to utilize visual information to suggest suitable recipes and provide visual support for cooking procedures. [Means for solving the problem]

[0006] The system according to the embodiment includes a visual input unit, an analysis unit, a recipe suggestion unit, a cooking procedure support unit, and an audio question and answer unit. The visual input unit acquires visual information. The analysis unit analyzes the visual information acquired by the visual input unit. The recipe suggestion unit suggests an appropriate recipe based on the information analyzed by the analysis unit. The cooking procedure support unit provides visual support for cooking procedures based on the recipe suggested by the recipe suggestion unit. The audio question and answer unit provides answers to audio questions. [Effects of the Invention]

[0007] The system according to the embodiment can utilize visual information to suggest suitable recipes and provide visual support for cooking procedures. [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 cooking assistance system according to an embodiment of the present invention utilizes the generative AI and recipe data built into AR glasses to suggest recipes and support cooking procedures based on visually input information. It also provides audio support for questions and answers. This allows the cooking assistance system to improve the user's cooking experience.

[0029] A cooking assistance system according to an embodiment includes a visual input unit, an analysis unit, a recipe suggestion unit, a cooking procedure support unit, and an audio Q&A unit. The visual input unit acquires visual information. For example, it acquires visual information viewed by a user using a camera in AR glasses. The visual input unit can also use image recognition technology to recognize ingredients in a refrigerator. For example, it can analyze camera footage and identify the type of ingredient. The analysis unit analyzes the visual information acquired by the visual input unit. For example, a generation AI analyzes the type and condition of ingredients based on the visual information. The analysis unit can also evaluate the freshness and quality of ingredients using an image recognition algorithm. For example, it can analyze the color and shape of ingredients to determine whether they have lost their freshness. The recipe suggestion unit proposes an appropriate recipe based on the information analyzed by the analysis unit. For example, the generation AI selects an appropriate recipe from built-in recipe data and proposes it to the user. The recipe suggestion unit can also propose recipes based on the user's health condition and preferences. For example, it can propose nutritious recipes or allergy-friendly recipes. The cooking procedure support unit provides visual support for cooking procedures based on the recipes proposed by the recipe suggestion unit. For example, cooking steps can be displayed on the AR glasses' display, allowing the user to follow the steps to cook. The cooking step support unit can also monitor the progress of the cooking steps in real time and automatically suggest the next step. For example, it monitors how ingredients are cut and how cooking utensils are used, and provides instructions on the next step. The voice question and answer unit provides answers to voice questions. For example, if a user asks, "What should I do next?", the generation AI analyzes the question and replies, "Next, please cut the vegetables." The voice question and answer unit can also refer to past question history to provide more personalized answers. For example, if the same question has been asked in the past, the voice question and answer unit provides an answer based on that history. This allows the cooking assistance system according to the embodiment to improve the user's cooking experience. For example, by suggesting recipes and supporting cooking steps based on visual information and providing answers to voice questions, the user can cook efficiently and enjoyably.

[0030] The visual input unit can evaluate the freshness or quality of ingredients based on visual information and suggest the optimal timing for using them. The visual input unit, for example, uses the camera in the AR glasses to evaluate the freshness of ingredients in the refrigerator in real time. For example, it can analyze the color and shape of vegetables and suggest using them sooner if they are losing their freshness. The visual input unit can also use image recognition technology to evaluate the quality of ingredients. For example, it can analyze the appearance and condition of ingredients and warn users if their quality is declining. Furthermore, the visual input unit can take into account expiration dates and storage periods to suggest the optimal timing for using ingredients. For example, it can suggest using ingredients with an approaching expiration date first. This allows the system to reduce food waste and support efficient cooking by evaluating the freshness and quality of ingredients and suggesting the optimal timing for using them.

[0031] The visual input unit can analyze the nutritional value of ingredients based on visual information and suggest recipes that suit the user's health status. For example, the visual input unit uses the camera in the AR glasses to recognize the type of ingredient and analyze its nutritional value. For example, it can identify the type of vegetable or fruit and suggest healthy recipes based on the nutritional value of each. The visual input unit can also refer to a database to analyze the nutritional value of ingredients. For example, it can analyze the calorie, vitamin, and mineral content of ingredients and suggest nutritionally balanced recipes. Furthermore, the visual input unit can take into account the user's health data to suggest recipes that suit the user's health status. For example, it can suggest appropriate recipes taking allergies, medical history, and nutritional balance into consideration. This allows the system to support the user's health by analyzing the nutritional value of ingredients and suggesting recipes that suit the user's health status.

[0032] The visual input unit can suggest food storage methods or storage periods based on visual information. For example, the visual input unit can use the camera in the AR glasses to recognize the types of food in the refrigerator and suggest the optimal storage method. For example, it can suggest storing vegetables in a humid location. The visual input unit can also refer to a database to suggest food storage periods. For example, it can suggest an appropriate storage period based on the food's expiration date or shelf life. Furthermore, the visual input unit can analyze the condition of the food to suggest food storage methods and storage periods. For example, it can analyze the appearance and condition of the food and suggest an appropriate storage method. In this way, by suggesting food storage methods and storage periods, it is possible to reduce food waste and support efficient storage.

[0033] The visual input unit can analyze allergen information for ingredients based on visual information and suggest allergy-friendly recipes. The visual input unit, for example, uses the camera in the AR glasses to recognize the type of ingredient and analyze the allergen information. For example, it can identify ingredients that contain nuts or dairy products and suggest allergy-friendly recipes. The visual input unit can also refer to a database to analyze the allergen information. For example, it can analyze specific ingredients and the risk of allergic reactions and suggest appropriate recipes. Furthermore, the visual input unit can take into account the user's health data to suggest allergy-friendly recipes. For example, it can suggest appropriate recipes based on the user's allergy information. In this way, by analyzing the allergen information for ingredients and suggesting allergy-friendly recipes, it is possible to support safe cooking for users with allergies.

[0034] The recipe suggestion unit can analyze a user's past cooking history and suggest recipes based on their preferences and habits. For example, the recipe suggestion unit stores the user's past cooking history in a database and analyzes their preferences and habits based on that data. For example, it analyzes the trends of frequently cooked dishes and suggests similar recipes. The recipe suggestion unit can also take into account user feedback to suggest recipes based on the user's preferences and habits. For example, it can prioritize suggesting recipes that have received high ratings in the past. Furthermore, the recipe suggestion unit can use a generative AI to analyze the user's cooking history. For example, the generative AI analyzes past data and learns the user's preferences and habits. This allows the user's past cooking history to be analyzed and recipes based on their preferences and habits to be suggested, thereby improving user satisfaction.

[0035] The recipe suggestion unit can suggest recipes according to the season or weather and utilize seasonal ingredients. The recipe suggestion unit, for example, builds a system that suggests recipes utilizing seasonal ingredients based on season and weather data. For example, it suggests cold dishes in summer and hot dishes in winter. The recipe suggestion unit can also refer to weather data to suggest recipes according to the season and weather. For example, it suggests appropriate recipes based on temperature and precipitation. Furthermore, the recipe suggestion unit can refer to regional specialty product data to utilize seasonal ingredients. For example, it suggests recipes based on fresh ingredients for each season. This makes it possible to enrich the user's dining experience by suggesting recipes according to the season and weather and utilizing seasonal ingredients.

[0036] The recipe suggestion unit can suggest substitutes for ingredients when suggesting a recipe, and provide recipes that can be cooked even when an ingredient is in short supply. For example, the recipe suggestion unit builds a system that automatically suggests substitutes for ingredients when suggesting a recipe. For example, if beef is not available, chicken is suggested as a substitute. The recipe suggestion unit can also take into account the nutritional value and cooking method of the ingredients when suggesting ingredient substitutes. For example, ingredients with the same nutritional value or ingredients that can be used in the same cooking method are suggested. Furthermore, the recipe suggestion unit can take into account the user's preferences and allergy information when suggesting ingredient substitutes. For example, a substitute is suggested to avoid ingredients to which the user has an allergy. This allows the system to suggest substitutes even when an ingredient is in short supply, thereby providing recipes that can be cooked, and improving user convenience.

[0037] The recipe suggestion unit can provide a link to purchase ingredients when suggesting a recipe, allowing the user to purchase the necessary ingredients online. For example, the recipe suggestion unit can build a system that automatically generates a link to purchase the necessary ingredients when suggesting a recipe. For example, the recipe suggestion unit can provide a link to an online store for the ingredients needed for the recipe. The recipe suggestion unit can also link with an online shop to provide the link to purchase ingredients. For example, the user can click on the link to purchase ingredients directly from the online shop. Furthermore, the recipe suggestion unit can take the user's preferences and budget into consideration when providing the link to purchase ingredients. For example, the recipe suggestion unit can suggest ingredients that suit the user's preferences or that can be purchased within the user's budget. This can improve user convenience by allowing the user to purchase the necessary ingredients online.

[0038] The cooking procedure support unit can monitor the progress of the cooking procedure in real time and automatically suggest the next step. For example, the cooking procedure support unit analyzes the progress of cooking based on visual information to monitor the progress of the cooking procedure in real time. For example, it monitors how ingredients are cut and how cooking utensils are used. The cooking procedure support unit can also use a generation AI to automatically suggest the next step. For example, the generation AI analyzes the cooking procedure and indicates the next step. Furthermore, the cooking procedure support unit can use sensors to monitor the progress of the cooking procedure. For example, it uses a temperature sensor or a timer to monitor the cooking progress. This allows the progress of the cooking procedure to be monitored in real time and automatically suggest the next step, thereby making the user's cooking more efficient.

[0039] The cooking procedure support unit can provide detailed video guides for each step of the cooking procedure, making it easier to understand visually. The cooking procedure support unit, for example, builds a system that provides detailed video guides for each step of the cooking procedure. For example, it can show how to cut ingredients and how to cook them through video. The cooking procedure support unit can also use a generation AI to provide the video guide. For example, the generation AI can analyze the cooking procedure and generate the video guide. Furthermore, the cooking procedure support unit can take user feedback into consideration to provide the video guide. For example, it can provide a video guide that is visually easy for the user to understand. As a result, by providing detailed video guides for each step of the cooking procedure, it can make it easier for the user to understand visually.

[0040] The cooking procedure support unit can cooperate with other home appliances to promote automation. For example, the cooking procedure support unit builds a system that cooperates with other home appliances to support cooking procedures. For example, it automatically operates an oven or a mixer. The cooking procedure support unit can also use IoT technology to cooperate with other home appliances. For example, it can cooperate with smart home devices to automate cooking procedures. Furthermore, the cooking procedure support unit can use generative AI to cooperate with other home appliances. For example, the generative AI can analyze the operation of a home appliance and promote automation. This can improve cooking efficiency by coordinating with other home appliances and promoting automation.

[0041] The cooking procedure support unit can track the user's hand movements and provide accurate instructions for cooking procedures. For example, the cooking procedure support unit builds a system that tracks the user's hand movements to support cooking procedures. For example, the cooking procedure support unit analyzes the hand movements and provides accurate instructions for cooking procedures. The cooking procedure support unit can also use motion recognition technology to track the user's hand movements. For example, the hand movements are captured with a camera and analyzed. Furthermore, the cooking procedure support unit can use a sensor to track the user's hand movements. For example, the sensor detects the hand movements and provides accurate instructions for cooking procedures. In this way, the accuracy of cooking can be improved by tracking the user's hand movements and providing accurate instructions for cooking procedures.

[0042] The voice question and answer unit can provide detailed information on related recipes and cooking procedures in response to a voice question. The voice question and answer unit, for example, builds a system that provides detailed information on related recipes and cooking procedures in response to a voice question. For example, in response to the question, "What should I do next?", the voice question and answer unit can provide detailed explanations of the next steps. The voice question and answer unit can also use a generation AI to provide detailed information in response to a voice question. For example, the generation AI analyzes the question and provides appropriate information. Furthermore, the voice question and answer unit can refer to a database to provide detailed information in response to a voice question. For example, detailed information is provided based on recipe data and cooking procedure data. This allows the user's questions to be resolved quickly by providing detailed information on related recipes and cooking procedures in response to a voice question.

[0043] The voice question and answer unit can refer to past question history in response to a voice question and provide a more personalized answer. The voice question and answer unit, for example, builds a system that refers to past question history in response to a voice question and provides a more personalized answer. For example, if the same question has been asked in the past, an answer is provided based on that history. The voice question and answer unit can also use a database to refer to the past question history. For example, past question contents and answers are stored in a database and an answer is provided based on that. Furthermore, the voice question and answer unit can use a generation AI to refer to the past question history. For example, the generation AI analyzes past data and provides a more personalized answer. In this way, by referring to the past question history and providing a more personalized answer, user satisfaction can be improved.

[0044] The audio question and answer unit can provide answers to audio questions based on the feedback and ratings of other users. The audio question and answer unit, for example, builds a system that provides answers to audio questions based on the feedback and ratings of other users. For example, answers that have been highly rated by other users are preferentially provided. The audio question and answer unit can also use a database to provide answers based on the feedback and ratings of other users. For example, user ratings and comments can be stored in a database and answers can be provided based on the stored ratings. Furthermore, the audio question and answer unit can use a generation AI to provide answers based on the feedback and ratings of other users. For example, the generation AI analyzes the feedback and ratings and provides an appropriate answer. This makes it possible to improve user satisfaction by providing answers based on the feedback and ratings of other users.

[0045] The audio question and answer unit can provide a video tutorial related to a voice question, making it easier to understand visually. For example, the audio question and answer unit builds a system that provides a video tutorial related to a voice question. For example, in response to a question such as, "Please show me a video of this procedure," a video tutorial is played. The audio question and answer unit can also use a generation AI to provide the video tutorial. For example, the generation AI analyzes the question and provides an appropriate video tutorial. Furthermore, the audio question and answer unit can refer to a database to provide the video tutorial. For example, videos and visual explanations of cooking procedures can be stored in a database, and a video tutorial can be provided based on that. In this way, providing a video tutorial related to a voice question makes it easier for the user to understand visually.

[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 visual input unit can analyze allergen information for ingredients based on visual information and suggest allergy-friendly recipes. The visual input unit, for example, uses the camera in the AR glasses to recognize the type of ingredient and analyze the allergen information. For example, it can identify ingredients that contain nuts or dairy products and suggest allergy-friendly recipes. The visual input unit can also refer to a database to analyze the allergen information. For example, it can analyze specific ingredients and the risk of allergic reactions and suggest appropriate recipes. Furthermore, the visual input unit can take into account the user's health data to suggest allergy-friendly recipes. For example, it can suggest appropriate recipes based on the user's allergy information. In this way, by analyzing the allergen information for ingredients and suggesting allergy-friendly recipes, it is possible to support safe cooking for users with allergies.

[0048] The recipe suggestion unit can analyze a user's past cooking history and suggest recipes based on their preferences and habits. For example, the recipe suggestion unit stores the user's past cooking history in a database and analyzes their preferences and habits based on that data. For example, it analyzes the trends of frequently cooked dishes and suggests similar recipes. The recipe suggestion unit can also take into account user feedback to suggest recipes based on the user's preferences and habits. For example, it can prioritize suggesting recipes that have received high ratings in the past. Furthermore, the recipe suggestion unit can use a generative AI to analyze the user's cooking history. For example, the generative AI analyzes past data and learns the user's preferences and habits. This allows the user's past cooking history to be analyzed and recipes based on their preferences and habits to be suggested, thereby improving user satisfaction.

[0049] The cooking procedure support unit can provide detailed video guides for each step of the cooking procedure, making it easier to understand visually. The cooking procedure support unit, for example, builds a system that provides detailed video guides for each step of the cooking procedure. For example, it can show how to cut ingredients and how to cook them through video. The cooking procedure support unit can also use a generation AI to provide the video guide. For example, the generation AI can analyze the cooking procedure and generate the video guide. Furthermore, the cooking procedure support unit can take user feedback into consideration to provide the video guide. For example, it can provide a video guide that is visually easy for the user to understand. As a result, by providing detailed video guides for each step of the cooking procedure, it can make it easier for the user to understand visually.

[0050] The cooking procedure support unit can cooperate with other home appliances to promote automation. For example, the cooking procedure support unit builds a system that cooperates with other home appliances to support cooking procedures. For example, it automatically operates an oven or a mixer. The cooking procedure support unit can also use IoT technology to cooperate with other home appliances. For example, it can cooperate with smart home devices to automate cooking procedures. Furthermore, the cooking procedure support unit can use generative AI to cooperate with other home appliances. For example, the generative AI can analyze the operation of a home appliance and promote automation. This can improve cooking efficiency by coordinating with other home appliances and promoting automation.

[0051] The audio question and answer unit can provide answers to audio questions based on the feedback and ratings of other users. The audio question and answer unit, for example, builds a system that provides answers to audio questions based on the feedback and ratings of other users. For example, answers that have been highly rated by other users are preferentially provided. The audio question and answer unit can also use a database to provide answers based on the feedback and ratings of other users. For example, user ratings and comments can be stored in a database and answers can be provided based on the stored ratings. Furthermore, the audio question and answer unit can use a generation AI to provide answers based on the feedback and ratings of other users. For example, the generation AI analyzes the feedback and ratings and provides an appropriate answer. This makes it possible to improve user satisfaction by providing answers based on the feedback and ratings of other users.

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

[0053] Step 1: The visual input unit acquires visual information. For example, it acquires visual information of what the user is seeing using the camera of the AR glasses. The visual input unit can also use image recognition technology to recognize ingredients in the refrigerator. For example, it analyzes the camera image and identifies the type of ingredient. Step 2: The analysis unit analyzes the visual information acquired by the visual input unit. For example, the generation AI analyzes the type and condition of ingredients based on the visual information. The analysis unit can also use image recognition algorithms to evaluate the freshness and quality of ingredients. For example, it can analyze the color and shape of ingredients to determine whether they have lost their freshness. Step 3: The recipe suggestion unit suggests appropriate recipes based on the information analyzed by the analysis unit. For example, the generation AI selects appropriate recipes from the built-in recipe data and suggests them to the user. The recipe suggestion unit can also suggest recipes based on the user's health condition and preferences. For example, it can suggest nutritious recipes or allergy-friendly recipes. Step 4: The cooking procedure support unit provides visual support for the cooking procedure based on the recipe proposed by the recipe suggestion unit. For example, the cooking procedure can be displayed on the AR glasses' display, allowing the user to follow the cooking procedure. The cooking procedure support unit can also monitor the progress of the cooking procedure in real time and automatically suggest the next step. For example, it monitors how ingredients are cut and how cooking utensils are used, and provides instructions on the next step. Step 5: The voice Q&A unit provides an answer to the voice question. For example, if the user asks, "What should I do next?", the generation AI analyzes the question and answers, "Next, please cut the vegetables." The voice Q&A unit can also refer to past question history to provide more personalized answers. For example, if the same question has been asked in the past, the answer will be based on that history.

[0054] (Example 2) The cooking assistance system according to an embodiment of the present invention utilizes the generative AI and recipe data built into AR glasses to suggest recipes and support cooking procedures based on visually input information. It also provides audio support for questions and answers. This allows the cooking assistance system to improve the user's cooking experience.

[0055] A cooking assistance system according to an embodiment includes a visual input unit, an analysis unit, a recipe suggestion unit, a cooking procedure support unit, and an audio Q&A unit. The visual input unit acquires visual information. For example, it acquires visual information viewed by a user using a camera in AR glasses. The visual input unit can also use image recognition technology to recognize ingredients in a refrigerator. For example, it can analyze camera footage and identify the type of ingredient. The analysis unit analyzes the visual information acquired by the visual input unit. For example, a generation AI analyzes the type and condition of ingredients based on the visual information. The analysis unit can also evaluate the freshness and quality of ingredients using an image recognition algorithm. For example, it can analyze the color and shape of ingredients to determine whether they have lost their freshness. The recipe suggestion unit proposes an appropriate recipe based on the information analyzed by the analysis unit. For example, the generation AI selects an appropriate recipe from built-in recipe data and proposes it to the user. The recipe suggestion unit can also propose recipes based on the user's health condition and preferences. For example, it can propose nutritious recipes or allergy-friendly recipes. The cooking procedure support unit provides visual support for cooking procedures based on the recipes proposed by the recipe suggestion unit. For example, cooking steps can be displayed on the AR glasses' display, allowing the user to follow the steps to cook. The cooking step support unit can also monitor the progress of the cooking steps in real time and automatically suggest the next step. For example, it monitors how ingredients are cut and how cooking utensils are used, and provides instructions on the next step. The voice question and answer unit provides answers to voice questions. For example, if a user asks, "What should I do next?", the generation AI analyzes the question and replies, "Next, please cut the vegetables." The voice question and answer unit can also refer to past question history to provide more personalized answers. For example, if the same question has been asked in the past, the voice question and answer unit provides an answer based on that history. This allows the cooking assistance system according to the embodiment to improve the user's cooking experience. For example, by suggesting recipes and supporting cooking steps based on visual information and providing answers to voice questions, the user can cook efficiently and enjoyably.

[0056] The visual input unit can evaluate the freshness or quality of ingredients based on visual information and suggest the optimal timing for using them. The visual input unit, for example, uses the camera in the AR glasses to evaluate the freshness of ingredients in the refrigerator in real time. For example, it can analyze the color and shape of vegetables and suggest using them sooner if they are losing their freshness. The visual input unit can also use image recognition technology to evaluate the quality of ingredients. For example, it can analyze the appearance and condition of ingredients and warn users if their quality is declining. Furthermore, the visual input unit can take into account expiration dates and storage periods to suggest the optimal timing for using ingredients. For example, it can suggest using ingredients with an approaching expiration date first. This allows the system to reduce food waste and support efficient cooking by evaluating the freshness and quality of ingredients and suggesting the optimal timing for using them.

[0057] The visual input unit can analyze the nutritional value of ingredients based on visual information and suggest recipes that suit the user's health status. For example, the visual input unit uses the camera in the AR glasses to recognize the type of ingredient and analyze its nutritional value. For example, it can identify the type of vegetable or fruit and suggest healthy recipes based on the nutritional value of each. The visual input unit can also refer to a database to analyze the nutritional value of ingredients. For example, it can analyze the calorie, vitamin, and mineral content of ingredients and suggest nutritionally balanced recipes. Furthermore, the visual input unit can take into account the user's health data to suggest recipes that suit the user's health status. For example, it can suggest appropriate recipes taking allergies, medical history, and nutritional balance into consideration. This allows the system to support the user's health by analyzing the nutritional value of ingredients and suggesting recipes that suit the user's health status.

[0058] The visual input unit can use an emotion estimation function to estimate a user's stress level from their facial expressions and movements and suggest relaxing recipes and cooking procedures. The visual input unit can, for example, use the camera in the AR glasses to analyze the user's facial expressions and estimate their stress level. For example, if the user's brow is furrowed, the visual input unit can suggest simple, relaxing recipes. The visual input unit can also use motion recognition technology to analyze the user's movements. For example, it can analyze hand movements and posture to estimate the stress level. Furthermore, the visual input unit can use an emotion estimation algorithm to suggest relaxing recipes and cooking procedures based on the user's stress level. For example, if the user's stress level is high, the visual input unit can suggest simple, effortless recipes. This allows the system to estimate the user's stress level and suggest relaxing recipes and cooking procedures, thereby reducing stress during cooking.

[0059] The visual input unit can suggest food storage methods or storage periods based on visual information. For example, the visual input unit can use the camera in the AR glasses to recognize the types of food in the refrigerator and suggest the optimal storage method. For example, it can suggest storing vegetables in a humid location. The visual input unit can also refer to a database to suggest food storage periods. For example, it can suggest an appropriate storage period based on the food's expiration date or shelf life. Furthermore, the visual input unit can analyze the condition of the food to suggest food storage methods and storage periods. For example, it can analyze the appearance and condition of the food and suggest an appropriate storage method. In this way, by suggesting food storage methods and storage periods, it is possible to reduce food waste and support efficient storage.

[0060] The visual input unit can analyze allergen information for ingredients based on visual information and suggest allergy-friendly recipes. The visual input unit, for example, uses the camera in the AR glasses to recognize the type of ingredient and analyze the allergen information. For example, it can identify ingredients that contain nuts or dairy products and suggest allergy-friendly recipes. The visual input unit can also refer to a database to analyze the allergen information. For example, it can analyze specific ingredients and the risk of allergic reactions and suggest appropriate recipes. Furthermore, the visual input unit can take into account the user's health data to suggest allergy-friendly recipes. For example, it can suggest appropriate recipes based on the user's allergy information. In this way, by analyzing the allergen information for ingredients and suggesting allergy-friendly recipes, it is possible to support safe cooking for users with allergies.

[0061] The visual input unit can use an emotion estimation function to adjust music or lighting according to the user's emotions and optimize the cooking environment. The visual input unit, for example, uses a camera in AR glasses to analyze the user's facial expression and suggest music according to the emotion. For example, if the user has a relaxed expression, it plays calm music. The visual input unit can also work with a lighting control system to adjust lighting according to the user's emotions. For example, if the user is feeling stressed, it adjusts the lighting to a softer color. Furthermore, the visual input unit can analyze the user's emotions using an emotion estimation algorithm and adjust music and lighting. For example, it adjusts music and lighting based on an emotion score. This allows the cooking environment to be optimized and the user's cooking experience to be improved by adjusting the music and lighting according to the user's emotions.

[0062] The recipe suggestion unit can analyze a user's past cooking history and suggest recipes based on their preferences and habits. For example, the recipe suggestion unit stores the user's past cooking history in a database and analyzes their preferences and habits based on that data. For example, it analyzes the trends of frequently cooked dishes and suggests similar recipes. The recipe suggestion unit can also take into account user feedback to suggest recipes based on the user's preferences and habits. For example, it can prioritize suggesting recipes that have received high ratings in the past. Furthermore, the recipe suggestion unit can use a generative AI to analyze the user's cooking history. For example, the generative AI analyzes past data and learns the user's preferences and habits. This allows the user's past cooking history to be analyzed and recipes based on their preferences and habits to be suggested, thereby improving user satisfaction.

[0063] The recipe suggestion unit can suggest recipes according to the season or weather and utilize seasonal ingredients. The recipe suggestion unit, for example, builds a system that suggests recipes utilizing seasonal ingredients based on season and weather data. For example, it suggests cold dishes in summer and hot dishes in winter. The recipe suggestion unit can also refer to weather data to suggest recipes according to the season and weather. For example, it suggests appropriate recipes based on temperature and precipitation. Furthermore, the recipe suggestion unit can refer to regional specialty product data to utilize seasonal ingredients. For example, it suggests recipes based on fresh ingredients for each season. This makes it possible to enrich the user's dining experience by suggesting recipes according to the season and weather and utilizing seasonal ingredients.

[0064] The recipe suggestion unit can use the emotion estimation function to suggest recipes that match the user's mood. For example, the recipe suggestion unit uses the emotion estimation function to analyze the user's mood and suggest recipes based on the results. For example, when the user is tired, it can suggest simple and time-saving recipes. The recipe suggestion unit can also use an emotion estimation algorithm to suggest recipes that match the user's mood. For example, it can suggest recipes that match the user's mood based on an emotion score. Furthermore, the recipe suggestion unit can refer to the user's past emotion data to suggest recipes that match the user's mood. For example, it can suggest recipes that were popular in the past when the user was in the same mood. This can improve user satisfaction by suggesting recipes that match the user's mood.

[0065] The recipe suggestion unit can suggest substitutes for ingredients when suggesting a recipe, and provide recipes that can be cooked even when an ingredient is in short supply. For example, the recipe suggestion unit builds a system that automatically suggests substitutes for ingredients when suggesting a recipe. For example, if beef is not available, chicken is suggested as a substitute. The recipe suggestion unit can also take into account the nutritional value and cooking method of the ingredients when suggesting ingredient substitutes. For example, ingredients with the same nutritional value or ingredients that can be used in the same cooking method are suggested. Furthermore, the recipe suggestion unit can take into account the user's preferences and allergy information when suggesting ingredient substitutes. For example, a substitute is suggested to avoid ingredients to which the user has an allergy. This allows the system to suggest substitutes even when an ingredient is in short supply, thereby providing recipes that can be cooked, and improving user convenience.

[0066] The recipe suggestion unit can provide a link to purchase ingredients when suggesting a recipe, allowing the user to purchase the necessary ingredients online. For example, the recipe suggestion unit can build a system that automatically generates a link to purchase the necessary ingredients when suggesting a recipe. For example, the recipe suggestion unit can provide a link to an online store for the ingredients needed for the recipe. The recipe suggestion unit can also link with an online shop to provide the link to purchase ingredients. For example, the user can click on the link to purchase ingredients directly from the online shop. Furthermore, the recipe suggestion unit can take the user's preferences and budget into consideration when providing the link to purchase ingredients. For example, the recipe suggestion unit can suggest ingredients that suit the user's preferences or that can be purchased within the user's budget. This can improve user convenience by allowing the user to purchase the necessary ingredients online.

[0067] The recipe suggestion unit can use the emotion estimation function to suggest recipe variations according to the user's emotions. For example, the recipe suggestion unit uses the emotion estimation function to build a system that suggests recipe variations according to the user's emotions. For example, it can suggest luxurious recipes for special occasions. The recipe suggestion unit can also use an emotion estimation algorithm to suggest recipe variations according to the user's emotions. For example, it can suggest recipe variations that match the user's emotions based on an emotion score. Furthermore, the recipe suggestion unit can refer to the user's past emotion data to suggest recipe variations according to the user's emotions. For example, it can suggest recipes that were popular in the past when the user was feeling the same emotion. This can improve user satisfaction by suggesting recipe variations according to the user's emotions.

[0068] The cooking procedure support unit can monitor the progress of the cooking procedure in real time and automatically suggest the next step. For example, the cooking procedure support unit analyzes the progress of cooking based on visual information to monitor the progress of the cooking procedure in real time. For example, it monitors how ingredients are cut and how cooking utensils are used. The cooking procedure support unit can also use a generation AI to automatically suggest the next step. For example, the generation AI analyzes the cooking procedure and indicates the next step. Furthermore, the cooking procedure support unit can use sensors to monitor the progress of the cooking procedure. For example, it uses a temperature sensor or a timer to monitor the cooking progress. This allows the progress of the cooking procedure to be monitored in real time and automatically suggest the next step, thereby making the user's cooking more efficient.

[0069] The cooking procedure support unit can provide detailed video guides for each step of the cooking procedure, making it easier to understand visually. The cooking procedure support unit, for example, builds a system that provides detailed video guides for each step of the cooking procedure. For example, it can show how to cut ingredients and how to cook them through video. The cooking procedure support unit can also use a generation AI to provide the video guide. For example, the generation AI can analyze the cooking procedure and generate the video guide. Furthermore, the cooking procedure support unit can take user feedback into consideration to provide the video guide. For example, it can provide a video guide that is visually easy for the user to understand. As a result, by providing detailed video guides for each step of the cooking procedure, it can make it easier for the user to understand visually.

[0070] The cooking procedure support unit can use the emotion estimation function to simplify cooking procedures according to the user's stress level. For example, the cooking procedure support unit uses the emotion estimation function to analyze the user's stress level and builds a system that simplifies cooking procedures based on the results. For example, if the user is highly stressed, a procedure may be omitted. The cooking procedure support unit can also use an emotion estimation algorithm to simplify cooking procedures according to the user's stress level. For example, based on the emotion score, the cooking procedure support unit can suggest cooking procedures according to the user's stress level. Furthermore, the cooking procedure support unit can take user feedback into consideration to simplify cooking procedures according to the user's stress level. For example, if the user is feeling stressed, the cooking procedure support unit can suggest a simple alternative procedure. This allows the cooking procedure to be simplified according to the user's stress level, thereby reducing stress during cooking.

[0071] The cooking procedure support unit can cooperate with other home appliances to promote automation. For example, the cooking procedure support unit builds a system that cooperates with other home appliances to support cooking procedures. For example, it automatically operates an oven or a mixer. The cooking procedure support unit can also use IoT technology to cooperate with other home appliances. For example, it can cooperate with smart home devices to automate cooking procedures. Furthermore, the cooking procedure support unit can use generative AI to cooperate with other home appliances. For example, the generative AI can analyze the operation of a home appliance and promote automation. This can improve cooking efficiency by coordinating with other home appliances and promoting automation.

[0072] The cooking procedure support unit can track the user's hand movements and provide accurate instructions for cooking procedures. For example, the cooking procedure support unit builds a system that tracks the user's hand movements to support cooking procedures. For example, the cooking procedure support unit analyzes the hand movements and provides accurate instructions for cooking procedures. The cooking procedure support unit can also use motion recognition technology to track the user's hand movements. For example, the hand movements are captured with a camera and analyzed. Furthermore, the cooking procedure support unit can use a sensor to track the user's hand movements. For example, the sensor detects the hand movements and provides accurate instructions for cooking procedures. In this way, the accuracy of cooking can be improved by tracking the user's hand movements and providing accurate instructions for cooking procedures.

[0073] The cooking procedure support unit can use the emotion estimation function to provide cooking procedure advice based on the user's emotions. For example, the cooking procedure support unit uses the emotion estimation function to build a system that provides cooking procedure advice based on the user's emotions. For example, advice on staying calm when the user is feeling anxious is provided. The cooking procedure support unit can also use an emotion estimation algorithm to provide cooking procedure advice based on the user's emotions. For example, advice based on the user's emotions is provided based on an emotion score. Furthermore, the cooking procedure support unit can refer to the user's past emotion data to provide cooking procedure advice based on the user's emotions. For example, advice that was effective when the user felt the same emotion in the past is provided. In this way, by providing cooking procedure advice based on the user's emotions, stress during cooking can be reduced and the cooking experience can be improved.

[0074] The voice question and answer unit can provide detailed information on related recipes and cooking procedures in response to a voice question. The voice question and answer unit, for example, builds a system that provides detailed information on related recipes and cooking procedures in response to a voice question. For example, in response to the question, "What should I do next?", the voice question and answer unit can provide detailed explanations of the next steps. The voice question and answer unit can also use a generation AI to provide detailed information in response to a voice question. For example, the generation AI analyzes the question and provides appropriate information. Furthermore, the voice question and answer unit can refer to a database to provide detailed information in response to a voice question. For example, detailed information is provided based on recipe data and cooking procedure data. This allows the user's questions to be resolved quickly by providing detailed information on related recipes and cooking procedures in response to a voice question.

[0075] The voice question and answer unit can refer to past question history in response to a voice question and provide a more personalized answer. The voice question and answer unit, for example, builds a system that refers to past question history in response to a voice question and provides a more personalized answer. For example, if the same question has been asked in the past, an answer is provided based on that history. The voice question and answer unit can also use a database to refer to the past question history. For example, past question contents and answers are stored in a database and an answer is provided based on that. Furthermore, the voice question and answer unit can use a generation AI to refer to the past question history. For example, the generation AI analyzes past data and provides a more personalized answer. In this way, by referring to the past question history and providing a more personalized answer, user satisfaction can be improved.

[0076] The voice question and answer unit can use the emotion estimation function to provide answers in a tone that corresponds to the user's emotion. For example, the voice question and answer unit uses the emotion estimation function to build a system that provides answers in a tone that corresponds to the user's emotion. For example, the voice question and answer unit answers in a relaxed tone. The voice question and answer unit can also use an emotion estimation algorithm to provide answers in a tone that corresponds to the user's emotion. For example, the voice question and answer unit provides answers in a tone that corresponds to the user's emotion based on an emotion score. Furthermore, the voice question and answer unit can refer to the user's past emotion data to provide answers in a tone that corresponds to the user's emotion. For example, the voice question and answer unit provides answers in a tone that was effective in the past when the user had the same emotion. In this way, by providing answers in a tone that corresponds to the user's emotion, user satisfaction can be improved.

[0077] The audio question and answer unit can provide answers to audio questions based on the feedback and ratings of other users. The audio question and answer unit, for example, builds a system that provides answers to audio questions based on the feedback and ratings of other users. For example, answers that have been highly rated by other users are preferentially provided. The audio question and answer unit can also use a database to provide answers based on the feedback and ratings of other users. For example, user ratings and comments can be stored in a database and answers can be provided based on the stored ratings. Furthermore, the audio question and answer unit can use a generation AI to provide answers based on the feedback and ratings of other users. For example, the generation AI analyzes the feedback and ratings and provides an appropriate answer. This makes it possible to improve user satisfaction by providing answers based on the feedback and ratings of other users.

[0078] The audio question and answer unit can provide a video tutorial related to a voice question, making it easier to understand visually. For example, the audio question and answer unit builds a system that provides a video tutorial related to a voice question. For example, in response to a question such as, "Please show me a video of this procedure," a video tutorial is played. The audio question and answer unit can also use a generation AI to provide the video tutorial. For example, the generation AI analyzes the question and provides an appropriate video tutorial. Furthermore, the audio question and answer unit can refer to a database to provide the video tutorial. For example, videos and visual explanations of cooking procedures can be stored in a database, and a video tutorial can be provided based on that. In this way, providing a video tutorial related to a voice question makes it easier for the user to understand visually.

[0079] The voice question and answer unit can use the emotion estimation function to provide additional advice or suggestions based on the user's emotions. For example, the voice question and answer unit uses the emotion estimation function to build a system that provides additional advice or suggestions based on the user's emotions. For example, the voice question and answer unit provides words of encouragement. The voice question and answer unit can also use an emotion estimation algorithm to provide additional advice or suggestions based on the user's emotions. For example, the voice question and answer unit provides advice or suggestions based on the user's emotions based on an emotion score. Furthermore, the voice question and answer unit can refer to the user's past emotion data to provide additional advice or suggestions based on the user's emotions. For example, the voice question and answer unit provides advice or suggestions that were effective when the user had the same emotion in the past. This allows the user's satisfaction to be improved by providing additional advice or suggestions based on the user's emotions.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The visual input unit can analyze allergen information for ingredients based on visual information and suggest allergy-friendly recipes. The visual input unit, for example, uses the camera in the AR glasses to recognize the type of ingredient and analyze the allergen information. For example, it can identify ingredients that contain nuts or dairy products and suggest allergy-friendly recipes. The visual input unit can also refer to a database to analyze the allergen information. For example, it can analyze specific ingredients and the risk of allergic reactions and suggest appropriate recipes. Furthermore, the visual input unit can take into account the user's health data to suggest allergy-friendly recipes. For example, it can suggest appropriate recipes based on the user's allergy information. In this way, by analyzing the allergen information for ingredients and suggesting allergy-friendly recipes, it is possible to support safe cooking for users with allergies.

[0082] The visual input unit can use an emotion estimation function to estimate a user's stress level from their facial expressions and movements and suggest relaxing recipes and cooking procedures. The visual input unit can, for example, use the camera in the AR glasses to analyze the user's facial expressions and estimate their stress level. For example, if the user's brow is furrowed, the visual input unit can suggest simple, relaxing recipes. The visual input unit can also use motion recognition technology to analyze the user's movements. For example, it can analyze hand movements and posture to estimate the stress level. Furthermore, the visual input unit can use an emotion estimation algorithm to suggest relaxing recipes and cooking procedures based on the user's stress level. For example, if the user's stress level is high, the visual input unit can suggest simple, effortless recipes. This allows the system to estimate the user's stress level and suggest relaxing recipes and cooking procedures, thereby reducing stress during cooking.

[0083] The recipe suggestion unit can analyze a user's past cooking history and suggest recipes based on their preferences and habits. For example, the recipe suggestion unit stores the user's past cooking history in a database and analyzes their preferences and habits based on that data. For example, it analyzes the trends of frequently cooked dishes and suggests similar recipes. The recipe suggestion unit can also take into account user feedback to suggest recipes based on the user's preferences and habits. For example, it can prioritize suggesting recipes that have received high ratings in the past. Furthermore, the recipe suggestion unit can use a generative AI to analyze the user's cooking history. For example, the generative AI analyzes past data and learns the user's preferences and habits. This allows the user's past cooking history to be analyzed and recipes based on their preferences and habits to be suggested, thereby improving user satisfaction.

[0084] The recipe suggestion unit can use the emotion estimation function to suggest recipes that match the user's mood. For example, the recipe suggestion unit uses the emotion estimation function to analyze the user's mood and suggest recipes based on the results. For example, when the user is tired, it can suggest simple and time-saving recipes. The recipe suggestion unit can also use an emotion estimation algorithm to suggest recipes that match the user's mood. For example, it can suggest recipes that match the user's mood based on an emotion score. Furthermore, the recipe suggestion unit can refer to the user's past emotion data to suggest recipes that match the user's mood. For example, it can suggest recipes that were popular in the past when the user was in the same mood. This can improve user satisfaction by suggesting recipes that match the user's mood.

[0085] The cooking procedure support unit can provide detailed video guides for each step of the cooking procedure, making it easier to understand visually. The cooking procedure support unit, for example, builds a system that provides detailed video guides for each step of the cooking procedure. For example, it can show how to cut ingredients and how to cook them through video. The cooking procedure support unit can also use a generation AI to provide the video guide. For example, the generation AI can analyze the cooking procedure and generate the video guide. Furthermore, the cooking procedure support unit can take user feedback into consideration to provide the video guide. For example, it can provide a video guide that is visually easy for the user to understand. As a result, by providing detailed video guides for each step of the cooking procedure, it can make it easier for the user to understand visually.

[0086] The cooking procedure support unit can use the emotion estimation function to simplify cooking procedures according to the user's stress level. For example, the cooking procedure support unit uses the emotion estimation function to analyze the user's stress level and builds a system that simplifies cooking procedures based on the results. For example, if the user is highly stressed, a procedure may be omitted. The cooking procedure support unit can also use an emotion estimation algorithm to simplify cooking procedures according to the user's stress level. For example, based on the emotion score, the cooking procedure support unit can suggest cooking procedures according to the user's stress level. Furthermore, the cooking procedure support unit can take user feedback into consideration to simplify cooking procedures according to the user's stress level. For example, if the user is feeling stressed, the cooking procedure support unit can suggest a simple alternative procedure. This allows the cooking procedure to be simplified according to the user's stress level, thereby reducing stress during cooking.

[0087] The cooking procedure support unit can cooperate with other home appliances to promote automation. For example, the cooking procedure support unit builds a system that cooperates with other home appliances to support cooking procedures. For example, it automatically operates an oven or a mixer. The cooking procedure support unit can also use IoT technology to cooperate with other home appliances. For example, it can cooperate with smart home devices to automate cooking procedures. Furthermore, the cooking procedure support unit can use generative AI to cooperate with other home appliances. For example, the generative AI can analyze the operation of a home appliance and promote automation. This can improve cooking efficiency by coordinating with other home appliances and promoting automation.

[0088] The voice question and answer unit can use the emotion estimation function to provide answers in a tone that corresponds to the user's emotion. For example, the voice question and answer unit uses the emotion estimation function to build a system that provides answers in a tone that corresponds to the user's emotion. For example, the voice question and answer unit answers in a relaxed tone. The voice question and answer unit can also use an emotion estimation algorithm to provide answers in a tone that corresponds to the user's emotion. For example, the voice question and answer unit provides answers in a tone that corresponds to the user's emotion based on an emotion score. Furthermore, the voice question and answer unit can refer to the user's past emotion data to provide answers in a tone that corresponds to the user's emotion. For example, the voice question and answer unit provides answers in a tone that was effective in the past when the user had the same emotion. In this way, by providing answers in a tone that corresponds to the user's emotion, user satisfaction can be improved.

[0089] The audio question and answer unit can provide answers to audio questions based on the feedback and ratings of other users. The audio question and answer unit, for example, builds a system that provides answers to audio questions based on the feedback and ratings of other users. For example, answers that have been highly rated by other users are preferentially provided. The audio question and answer unit can also use a database to provide answers based on the feedback and ratings of other users. For example, user ratings and comments can be stored in a database and answers can be provided based on the stored ratings. Furthermore, the audio question and answer unit can use a generation AI to provide answers based on the feedback and ratings of other users. For example, the generation AI analyzes the feedback and ratings and provides an appropriate answer. This makes it possible to improve user satisfaction by providing answers based on the feedback and ratings of other users.

[0090] The voice question and answer unit can use the emotion estimation function to provide additional advice or suggestions based on the user's emotions. For example, the voice question and answer unit uses the emotion estimation function to build a system that provides additional advice or suggestions based on the user's emotions. For example, the voice question and answer unit provides words of encouragement. The voice question and answer unit can also use an emotion estimation algorithm to provide additional advice or suggestions based on the user's emotions. For example, the voice question and answer unit provides advice or suggestions based on the user's emotions based on an emotion score. Furthermore, the voice question and answer unit can refer to the user's past emotion data to provide additional advice or suggestions based on the user's emotions. For example, the voice question and answer unit provides advice or suggestions that were effective when the user had the same emotion in the past. This allows the user's satisfaction to be improved by providing additional advice or suggestions based on the user's emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The visual input unit acquires visual information. For example, it acquires visual information of what the user is seeing using the camera of the AR glasses. The visual input unit can also use image recognition technology to recognize ingredients in the refrigerator. For example, it analyzes the camera image and identifies the type of ingredient. Step 2: The analysis unit analyzes the visual information acquired by the visual input unit. For example, the generation AI analyzes the type and condition of ingredients based on the visual information. The analysis unit can also use image recognition algorithms to evaluate the freshness and quality of ingredients. For example, it can analyze the color and shape of ingredients to determine whether they have lost their freshness. Step 3: The recipe suggestion unit suggests appropriate recipes based on the information analyzed by the analysis unit. For example, the generation AI selects appropriate recipes from the built-in recipe data and suggests them to the user. The recipe suggestion unit can also suggest recipes based on the user's health condition and preferences. For example, it can suggest nutritious recipes or allergy-friendly recipes. Step 4: The cooking procedure support unit provides visual support for the cooking procedure based on the recipe proposed by the recipe suggestion unit. For example, the cooking procedure can be displayed on the AR glasses' display, allowing the user to follow the cooking procedure. The cooking procedure support unit can also monitor the progress of the cooking procedure in real time and automatically suggest the next step. For example, it monitors how ingredients are cut and how cooking utensils are used, and provides instructions on the next step. Step 5: The voice Q&A unit provides an answer to the voice question. For example, if the user asks, "What should I do next?", the generation AI analyzes the question and answers, "Next, please cut the vegetables." The voice Q&A unit can also refer to past question history to provide more personalized answers. For example, if the same question has been asked in the past, the answer will be based on that history.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0099] The 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.

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

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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, in order to avoid confusion and to 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.

[0159] 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]

[0160] 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 visual input unit for acquiring visual information; an analysis unit that analyzes the visual information acquired by the visual input unit; a recipe suggestion unit that suggests an appropriate recipe based on the information analyzed by the analysis unit; a cooking procedure support unit that visually supports cooking procedures based on the recipes proposed by the recipe suggestion unit; and a voice question and answer section that provides answers to voice questions. A system characterized by:

2. The visual input unit Using emotion estimation function, the stress level of the user is estimated from their facial expressions and movements, and the recipe or procedure for relaxation is suggested.

2. The system of claim 1.

3. The visual input unit Based on the visual information, the system suggests storage methods or storage periods for ingredients.

2. The system of claim 1.

4. The recipe suggestion unit Analyzes the user's cooking history and suggests recipes based on their preferences and habits 2. The system of claim 1.

5. The cooking procedure support unit includes: Monitor the progress of the cooking procedure in real time and automatically suggest next steps 2. The system of claim 1.

6. The audio question and answer section Providing detailed information about the recipe and cooking steps related to the voice question 2. The system of claim 1.

7. The visual input unit Emotion estimation function adjusts music or lighting according to the user's emotions to optimize the cooking environment 2. The system of claim 1.

8. The audio question and answer section Using emotion estimation function, answers are provided in a tone that matches the user's emotions 2. The system of claim 1.

Images