System

Abstract

An object of the system according to the embodiment is to provide a recipe customized based on individual information of a user and respond to a question in a cooking process in real time.SOLUTION: A system according to an embodiment includes a user information collection unit, a recipe generation unit, and a question answering unit. The user information collection unit collects food preferences, allergy information, and available cookware of the user. The recipe generation unit generates a customized recipe based on the information collected by the user information collection unit. The question answering unit answers questions in real time based on the recipe generated by the recipe generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to provide customized recipes based on a user's individual food preferences, allergy information, and available cooking equipment.

[0005] The system according to the embodiment aims to provide customized recipes based on the user's individual information and to respond to questions during the cooking process in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a user information collection unit, a recipe generation unit, and a question and answer unit. The user information collection unit collects information about a user's ingredient preferences, allergy information, and available cooking utensils. The recipe generation unit generates a customized recipe based on the information collected by the user information collection unit. The question and answer unit responds to questions in real time based on the recipe generated by the recipe generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide customized recipes based on the user's individual information and respond to questions during the cooking process in real time. [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 provides customized recipes based on a user's ingredient preferences, allergy information, and available cooking equipment, and resolves questions that arise during the cooking process through a real-time question and answer function. This allows users to effectively improve their cooking skills at their own pace while having fun at home.

[0029] A cooking assistance system according to an embodiment includes a user information collection unit, a recipe generation unit, and a question and answer unit. The user information collection unit collects a user's ingredient preferences, allergy information, and available cooking utensils. For example, a user inputs information such as "I like chicken, I'm allergic to peanuts, and I own an oven." The user information collection unit stores this information in a database and provides it to the recipe generation unit. The recipe generation unit generates a customized recipe based on the information collected by the user information collection unit. For example, the generation AI generates an optimal recipe taking into account the user's preferences and allergy information. The generation AI generates a recipe using a text generation AI (e.g., LLM). The generation AI can also generate recipe content using a multimodal generation AI. The question and answer unit responds to questions in real time based on the recipe generated by the recipe generation unit. For example, when a user inputs a question such as "How long should I reduce this sauce?", the generation AI provides an appropriate answer to the question. The generation AI generates an answer to the user's question using a text generation AI. The generation AI can also generate an answer to the question using a multimodal generation AI. As a result, the cooking assistance system according to the embodiment can provide customized recipes based on the user's individual information and resolve any questions that arise during the cooking process through a real-time question and answer function. For example, the system can quickly resolve any questions that arise while cooking, allowing the user to proceed with the cooking process smoothly.

[0030] The user information collection unit can customize recipes based on the user's food preferences and allergy information, as well as mealtimes and nutritional balance goals. For example, the user specifies breakfast, lunch, and dinner times, and the user information collection unit provides recipes suitable for each time period. For example, a light menu is suggested for breakfast, and a nutritionally balanced menu for dinner. The user information collection unit also customizes recipes taking into account the user's nutritional balance goals. For example, a recipe is provided that takes into account calorie restrictions and protein intake. This allows the user to customize recipes taking into account the user's mealtimes and nutritional balance goals, making it possible to provide more personalized recipes.

[0031] The user information collection unit can analyze the user's past cooking history and suggest changes in preferences or new ingredients. The user information collection unit, for example, saves the history of dishes the user has made in the past and suggests new recipes based on that data. For example, it can suggest different dishes using the same ingredients. The user information collection unit can also analyze changes in the user's preferences and suggest new ingredients based on that. For example, it can suggest recipes using ingredients that the user has recently become fond of. In this way, by analyzing the user's past cooking history and suggesting changes in preferences or new ingredients, it is possible to provide more personalized recipes.

[0032] The user information collection unit also collects information on the food preferences and allergies of the user's family or housemates, making it possible to provide recipes that will satisfy everyone. For example, the user inputs the food preferences and allergy information of family members and housemates, and the user information collection unit provides recipes that everyone can eat based on that information. For example, it suggests recipes that use ingredients that everyone in the family likes. The user information collection unit also customizes recipes taking into account the allergy information of family members and housemates. For example, if there is a family member with a nut allergy, it suggests recipes that do not use nuts. In this way, more personalized recipes can be provided by collecting the food preferences and allergy information of the user's family members and housemates and providing recipes that will satisfy everyone.

[0033] The user information collection unit can suggest recipes based on the season or local specialties and utilize local ingredients. The user information collection unit, for example, takes into account seasonal specialties and provides recipes that are optimal for that time of year. For example, in spring, it suggests recipes using fresh vegetables. The user information collection unit also customizes recipes taking into account local specialties. For example, it suggests recipes using local famous products. This makes it possible to suggest recipes that take into account the season and local specialties and utilize local ingredients, thereby providing more personalized recipes.

[0034] The question answering unit can refer to the user's past question history and provide more personalized answers. For example, the question answering unit stores a history of questions the user has asked in the past and provides personalized answers based on that data. For example, it provides information related to the content of the past questions. The question answering unit also analyzes the user's past question history to understand the user's preferences and trends. For example, it provides information on topics that the user frequently asks about. In this way, by referring to the user's past question history and providing more personalized answers, the user's questions can be more accurately resolved.

[0035] The question answering unit can provide not only answers to questions but also related additional information or tips. For example, the question answering unit provides not only answers but also related additional information in response to questions asked by the user. For example, in response to a question about how to reduce a sauce, it provides tips and precautions for reducing it. The question answering unit also provides information related to the content asked by the user. For example, in response to a question about a specific cooking method, it provides related recipes and cooking tips. In this way, by providing not only answers to questions but also related additional information and tips, the user's cooking skills can be improved more effectively.

[0036] The question answering unit links the question answering function with a voice assistant to enable hands-free operation. For example, the question answering unit links the question answering function with a voice assistant to enable a user to input questions by voice and receive answers by voice. For example, a smart speaker is used. The question answering unit also uses a voice assistant to enable a user to operate the device hands-free. For example, it supports operation by voice commands and gesture operation. This links the question answering function with the voice assistant to enable hands-free operation, improving user convenience.

[0037] The question answering unit can provide answers to questions in video format, making them visually easy to understand. For example, the question answering unit provides answers to user questions in video format, making them visually easy to understand. For example, cooking procedures are shown in video. The question answering unit also provides answers to user questions in a format that is visually easy to understand. For example, explanations are provided using animations or infographics. In this way, answers to questions are provided in video format, making them visually easy to understand, thereby deepening the user's understanding.

[0038] The step-by-step guide unit can monitor the progress of each step in real time and notify the user when to proceed to the next step. The step-by-step guide unit, for example, monitors the progress of each step in real time and notifies the user when to proceed to the next step. For example, it measures the cooking time and notifies the user when to proceed to the next step. The step-by-step guide unit also monitors the user's progress and instructs the user to proceed to the next step at the appropriate time. For example, it notifies the user when to proceed to the next step based on changes in temperature and the progress of cooking. In this way, by monitoring the progress of each step in real time and notifying the user when to proceed to the next step, the user can proceed with cooking smoothly.

[0039] The step-by-step guide section can provide not only detailed explanations of each step but also points where mistakes are likely to occur and important points to be careful of. For example, the step-by-step guide section, in addition to detailed explanations of each step, clearly indicates points where mistakes are likely to occur. For example, it warns that sauces are prone to burning when simmering. The step-by-step guide section also provides important points to be careful of for each step. For example, it provides important points to be careful of when handling ingredients and using cooking utensils. In this way, by providing not only detailed explanations of each step but also points where mistakes are likely to occur and important points to be careful of, the user can proceed with cooking without making mistakes.

[0040] The step-by-step guide unit can customize the step-by-step guide based on the user's cooking skill level. For example, the step-by-step guide unit evaluates the user's cooking skill level and provides a step-by-step guide accordingly. For example, detailed explanations are provided for beginners and concise explanations for advanced cooks. The step-by-step guide unit also customizes the guide based on the user's cooking skill level. For example, detailed explanations of basic cooking methods are provided for beginners, and advanced cooks are introduced to more advanced cooks. In this way, customizing the step-by-step guide according to the user's cooking skill level allows the user to learn cooking more effectively.

[0041] The step-by-step guide unit can provide the guide in multiple languages ​​to accommodate international users. The step-by-step guide unit, for example, provides the guide in multiple languages ​​to accommodate international users. For example, the guide is provided in English, Spanish, Chinese, etc. The step-by-step guide unit also provides the guide based on the user's language setting. For example, the guide language switches depending on the language set by the user. This allows the guide to be provided in multiple languages ​​to accommodate international users, making it usable by a wider range of users.

[0042] The video and image providing unit can highlight parts of a video or image that are of particular interest to the user. For example, the video and image providing unit highlights parts of a video or image that are likely to be of particular interest to the user. For example, it may highlight the finished dish or the presentation of the dish. The video and image providing unit may also highlight parts that attract the user's interest. For example, it may highlight cooking tips or how to handle specific ingredients. In this way, by highlighting parts of a video or image that are likely to be of particular interest to the user, the user's interest is piqued and their understanding is deepened.

[0043] The video and image providing unit can provide a function to adjust the video playback speed to suit the user's pace. The video and image providing unit provides, for example, a function to adjust the video playback speed to suit the user's pace. For example, the playback speed can be slowed down for beginners and increased for advanced learners. The video and image providing unit also adjusts the playback speed based on the user's operation history and learning speed. For example, the video and image providing unit suggests an optimal playback speed based on the playback speed of videos the user has previously watched. In this way, by providing a function to adjust the video playback speed to suit the user's pace, the user can learn at their own pace.

[0044] The video and image providing unit can add an interactive function that allows users to post comments or questions to videos or images. The video and image providing unit adds an interactive function that allows users to post comments or questions to videos or images, for example. For example, a question about a specific cooking procedure can be posted. The video and image providing unit also provides a function that allows users to post comments and questions in real time. For example, a question that arises while cooking can be asked on the spot. In this way, adding an interactive function that allows users to post comments and questions to videos or images can resolve users' doubts and deepen their understanding.

[0045] The video and image providing unit can provide videos or images in a format optimized for the user's device. For example, the video and image providing unit provides videos and images in a format optimized for the user's device. For example, it provides them in a resolution and format suitable for a smartphone or tablet. The video and image providing unit also provides videos and images in a format optimal for the user's device. For example, it provides them in a format suitable for a desktop computer or a laptop. In this way, by providing videos and images in a format optimized for the user's device, the user can view them comfortably.

[0046] The progress management unit can record the progress of the user's cooking skills in detail and provide specific areas for improvement. The progress management unit, for example, records the progress of the user's cooking skills in detail and provides feedback on specific areas for improvement. For example, it evaluates cooking time and degree of completion and suggests areas for improvement. The progress management unit also records the progress of the user's cooking skills in detail and provides specific areas for improvement. For example, it provides advice on areas for improvement in cooking methods and how to select ingredients. In this way, by recording the progress of the user's cooking skills in detail and providing feedback on specific areas for improvement, the user's skills can be supported.

[0047] The progress management unit can provide recipes of varying difficulty levels in stages based on the improvement of the user's cooking skills. For example, the progress management unit provides recipes of varying difficulty levels in stages according to the improvement of the user's cooking skills. For example, starting with recipes for beginners and gradually increasing the difficulty level. The progress management unit also evaluates the improvement of the user's cooking skills and provides recipes based on that evaluation. For example, when the user has mastered a particular skill, it suggests a more difficult recipe as the next step. In this way, by providing recipes of varying difficulty levels in stages according to the improvement of the user's cooking skills, the user can improve their skills without straining themselves.

[0048] The progress management unit can provide the results of progress management in a format that users can share and provide a function for competing with other users. For example, the progress management unit provides the results of progress management in a format that users can share on social media or messaging apps. For example, users can share photos of the finished dish or graphs showing skill improvement. The progress management unit also provides a function for users to compete with other users. For example, users can compete through a ranking function or by earning badges. In this way, by providing the results of progress management in a format that users can share and adding a function for users to compete with other users, it is possible to increase users' motivation.

[0049] The progress management unit can customize the feedback based on the user's preferences and provide more personalized advice. For example, the progress management unit customizes the feedback according to the user's preferences and provides personalized advice. For example, it provides advice that matches the user's preferred cooking style. The progress management unit also provides feedback based on the user's past cooking history and preferences. For example, it specifically indicates areas for improvement in dishes the user has made in the past. In this way, the feedback can be customized according to the user's preferences and more personalized advice can be provided, thereby supporting the user's skill improvement.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The cooking assistance system can further include a storage advice unit that suggests methods for storing ingredients. The storage advice unit suggests, for example, how to store ingredients purchased by the user. For example, it provides methods for storing vegetables and freezing meat. The storage advice unit can also manage the expiration dates of ingredients and notify the user to use them at the appropriate time. This allows the user to avoid waste and use ingredients efficiently.

[0052] The cooking assistance system can further include a health management unit that monitors the user's health condition and suggests healthy recipes. The health management unit, for example, monitors the user's blood pressure and blood sugar levels and customizes recipes based on the results. For example, it can suggest low-salt recipes to a user with high blood pressure. The health management unit can also provide recipes that match the user's health goals. This allows the user to enjoy cooking while maintaining their health.

[0053] The cooking assistance system may further include a shopping assistance unit that analyzes the user's ingredient purchase history and generates an efficient shopping list. The shopping assistance unit, for example, lists necessary ingredients based on the user's ingredient purchase history. For example, ingredients required for a specific recipe may be automatically added to the list. The shopping assistance unit may also customize the shopping list taking into account the user's preferences and allergy information. This allows the user to shop efficiently.

[0054] The cooking assistance system may further include a skill evaluation unit that evaluates the user's cooking skills and provides a training program to improve the skills. The skill evaluation unit may, for example, evaluate the user's cooking quality and cooking time, and suggest a training program based on the evaluation. For example, the skill evaluation unit may provide a video tutorial on how to use a knife or adjust the heat. The skill evaluation unit may also monitor the user's progress and provide appropriate feedback. This allows the user to effectively improve their cooking skills.

[0055] The cooking assistance system can further include an inventory management unit that manages the user's inventory of ingredients and automatically orders the necessary ingredients. The inventory management unit, for example, monitors the inventory in the user's refrigerator or pantry and lists the necessary ingredients. For example, if milk is running low, it is automatically ordered. The inventory management unit can also manage inventory taking into account the user's preferences and allergy information. This allows the user to always have the necessary ingredients on hand.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The user information collection unit collects the user's food preferences, allergy information, and available cooking equipment. For example, the user enters information such as "I like chicken, I'm allergic to peanuts, and I own an oven." The user information collection unit saves this information in a database and provides it to the recipe generation unit. Step 2: The recipe generation unit generates a customized recipe based on the information collected by the user information collection unit. For example, the generation AI generates an optimal recipe taking into account the user's preferences and allergy information. The generation AI generates the recipe using a text generation AI (e.g., LLM). The generation AI can also generate the content of the recipe using a multimodal generation AI. Step 3: The question answering unit responds to questions in real time based on the recipe generated by the recipe generation unit. For example, when a user inputs a question such as "How long should I reduce this sauce?", the generation AI provides an appropriate answer to the question. The generation AI generates an answer to the user's question using text generation AI. The generation AI can also generate answers to questions using multimodal generation AI.

[0058] (Example 2) The cooking assistance system according to an embodiment of the present invention provides customized recipes based on a user's ingredient preferences, allergy information, and available cooking equipment, and resolves questions that arise during the cooking process through a real-time question and answer function. This allows users to effectively improve their cooking skills at their own pace while having fun at home.

[0059] A cooking assistance system according to an embodiment includes a user information collection unit, a recipe generation unit, and a question and answer unit. The user information collection unit collects a user's ingredient preferences, allergy information, and available cooking utensils. For example, a user inputs information such as "I like chicken, I'm allergic to peanuts, and I own an oven." The user information collection unit stores this information in a database and provides it to the recipe generation unit. The recipe generation unit generates a customized recipe based on the information collected by the user information collection unit. For example, the generation AI generates an optimal recipe taking into account the user's preferences and allergy information. The generation AI generates a recipe using a text generation AI (e.g., LLM). The generation AI can also generate recipe content using a multimodal generation AI. The question and answer unit responds to questions in real time based on the recipe generated by the recipe generation unit. For example, when a user inputs a question such as "How long should I reduce this sauce?", the generation AI provides an appropriate answer to the question. The generation AI generates an answer to the user's question using a text generation AI. The generation AI can also generate an answer to the question using a multimodal generation AI. As a result, the cooking assistance system according to the embodiment can provide customized recipes based on the user's individual information and resolve any questions that arise during the cooking process through a real-time question and answer function. For example, the system can quickly resolve any questions that arise while cooking, allowing the user to proceed with the cooking process smoothly.

[0060] The user information collection unit can customize recipes based on the user's food preferences and allergy information, as well as mealtimes and nutritional balance goals. For example, the user specifies breakfast, lunch, and dinner times, and the user information collection unit provides recipes suitable for each time period. For example, a light menu is suggested for breakfast, and a nutritionally balanced menu for dinner. The user information collection unit also customizes recipes taking into account the user's nutritional balance goals. For example, a recipe is provided that takes into account calorie restrictions and protein intake. This allows the user to customize recipes taking into account the user's mealtimes and nutritional balance goals, making it possible to provide more personalized recipes.

[0061] The user information collection unit can analyze the user's past cooking history and suggest changes in preferences or new ingredients. The user information collection unit, for example, saves the history of dishes the user has made in the past and suggests new recipes based on that data. For example, it can suggest different dishes using the same ingredients. The user information collection unit can also analyze changes in the user's preferences and suggest new ingredients based on that. For example, it can suggest recipes using ingredients that the user has recently become fond of. In this way, by analyzing the user's past cooking history and suggesting changes in preferences or new ingredients, it is possible to provide more personalized recipes.

[0062] The user information collection unit can use the emotion estimation function to suggest recipes based on the user's current mood. The user information collection unit, for example, analyzes the user's facial expressions and voice to estimate the user's current mood. For example, if the user is tired, the unit suggests easy-to-make recipes. The user information collection unit also uses the emotion estimation function to suggest recipes that match the user's current mood. For example, if the user wants to relax, the unit suggests recipes that use relaxing ingredients. In this way, by using the emotion estimation function to suggest recipes that match the user's current mood, more personalized recipes can be provided.

[0063] The user information collection unit also collects information on the food preferences and allergies of the user's family or housemates, making it possible to provide recipes that will satisfy everyone. For example, the user inputs the food preferences and allergy information of family members and housemates, and the user information collection unit provides recipes that everyone can eat based on that information. For example, it suggests recipes that use ingredients that everyone in the family likes. The user information collection unit also customizes recipes taking into account the allergy information of family members and housemates. For example, if there is a family member with a nut allergy, it suggests recipes that do not use nuts. In this way, more personalized recipes can be provided by collecting the food preferences and allergy information of the user's family members and housemates and providing recipes that will satisfy everyone.

[0064] The user information collection unit can suggest recipes based on the season or local specialties and utilize local ingredients. The user information collection unit, for example, takes into account seasonal specialties and provides recipes that are optimal for that time of year. For example, in spring, it suggests recipes using fresh vegetables. The user information collection unit also customizes recipes taking into account local specialties. For example, it suggests recipes using local famous products. This makes it possible to suggest recipes that take into account the season and local specialties and utilize local ingredients, thereby providing more personalized recipes.

[0065] The user information collection unit can use the emotion estimation function to suggest music or entertainment that will help the user enjoy cooking. The user information collection unit, for example, analyzes the user's emotions and suggests music that can be enjoyed while cooking based on the results. For example, it suggests calm music when the user wants to relax. The user information collection unit also uses the emotion estimation function to suggest entertainment that will help the user enjoy cooking. For example, it suggests movies or games that can be enjoyed while cooking. In this way, by using the emotion estimation function to suggest music or entertainment that will help the user enjoy cooking, a more enjoyable cooking experience can be provided.

[0066] The question answering unit can refer to the user's past question history and provide more personalized answers. For example, the question answering unit stores a history of questions the user has asked in the past and provides personalized answers based on that data. For example, it provides information related to the content of the past questions. The question answering unit also analyzes the user's past question history to understand the user's preferences and trends. For example, it provides information on topics that the user frequently asks about. In this way, by referring to the user's past question history and providing more personalized answers, the user's questions can be more accurately resolved.

[0067] The question answering unit can provide not only answers to questions but also related additional information or tips. For example, the question answering unit provides not only answers but also related additional information in response to questions asked by the user. For example, in response to a question about how to reduce a sauce, it provides tips and precautions for reducing it. The question answering unit also provides information related to the content asked by the user. For example, in response to a question about a specific cooking method, it provides related recipes and cooking tips. In this way, by providing not only answers to questions but also related additional information and tips, the user's cooking skills can be improved more effectively.

[0068] The question answering unit can use the emotion estimation function to detect the user's stress level and provide advice for relaxation. The question answering unit, for example, analyzes the user's facial expressions and voice to estimate the stress level. For example, if the user is nervous, it provides advice to help them relax. The question answering unit also uses the emotion estimation function to detect the user's stress level and provide advice for helping them relax. For example, it suggests deep breathing techniques and relaxing music. In this way, the emotion estimation function is used to detect the user's stress level and provide advice to help them relax, making the user's cooking experience more comfortable.

[0069] The question answering unit links the question answering function with a voice assistant to enable hands-free operation. For example, the question answering unit links the question answering function with a voice assistant to enable a user to input questions by voice and receive answers by voice. For example, a smart speaker is used. The question answering unit also uses a voice assistant to enable a user to operate the device hands-free. For example, it supports operation by voice commands and gesture operation. This links the question answering function with the voice assistant to enable hands-free operation, improving user convenience.

[0070] The question answering unit can provide answers to questions in video format, making them visually easy to understand. For example, the question answering unit provides answers to user questions in video format, making them visually easy to understand. For example, cooking procedures are shown in video. The question answering unit also provides answers to user questions in a format that is visually easy to understand. For example, explanations are provided using animations or infographics. In this way, answers to questions are provided in video format, making them visually easy to understand, thereby deepening the user's understanding.

[0071] The question answering unit can use the emotion estimation function to analyze the emotion of the user when asking a question and provide positive feedback. For example, the question answering unit analyzes the emotion of the user when asking a question and provides positive feedback based on the results. For example, if the user is feeling anxious, it provides an encouraging message. The question answering unit also uses the emotion estimation function to analyze the emotion of the user when asking a question and provides positive feedback. For example, if the user is confident, it provides a message that reinforces that confidence. In this way, by using the emotion estimation function to analyze the emotion of the user when asking a question and providing positive feedback, the user's motivation is improved.

[0072] The step-by-step guide unit can monitor the progress of each step in real time and notify the user when to proceed to the next step. The step-by-step guide unit, for example, monitors the progress of each step in real time and notifies the user when to proceed to the next step. For example, it measures the cooking time and notifies the user when to proceed to the next step. The step-by-step guide unit also monitors the user's progress and instructs the user to proceed to the next step at the appropriate time. For example, it notifies the user when to proceed to the next step based on changes in temperature and the progress of cooking. In this way, by monitoring the progress of each step in real time and notifying the user when to proceed to the next step, the user can proceed with cooking smoothly.

[0073] The step-by-step guide section can provide not only detailed explanations of each step but also points where mistakes are likely to occur and important points to be careful of. For example, the step-by-step guide section, in addition to detailed explanations of each step, clearly indicates points where mistakes are likely to occur. For example, it warns that sauces are prone to burning when simmering. The step-by-step guide section also provides important points to be careful of for each step. For example, it provides important points to be careful of when handling ingredients and using cooking utensils. In this way, by providing not only detailed explanations of each step but also points where mistakes are likely to occur and important points to be careful of, the user can proceed with cooking without making mistakes.

[0074] The step-by-step guide unit can use the emotion estimation function to provide an encouraging message when the user experiences difficulty. The step-by-step guide unit, for example, analyzes the user's emotions and sends an encouraging message when the user experiences difficulty. For example, if the user is feeling impatient, it provides a message such as "Stay calm and keep going." The step-by-step guide unit also uses the emotion estimation function to provide an encouraging message when the user experiences difficulty. For example, if the user experiences difficulty, it provides a message such as "You can do it." In this way, the emotion estimation function can be used to send an encouraging message when the user experiences difficulty, thereby maintaining the user's motivation.

[0075] The step-by-step guide unit can customize the step-by-step guide based on the user's cooking skill level. For example, the step-by-step guide unit evaluates the user's cooking skill level and provides a step-by-step guide accordingly. For example, detailed explanations are provided for beginners and concise explanations for advanced cooks. The step-by-step guide unit also customizes the guide based on the user's cooking skill level. For example, detailed explanations of basic cooking methods are provided for beginners, and advanced cooks are introduced to more advanced cooks. In this way, customizing the step-by-step guide according to the user's cooking skill level allows the user to learn cooking more effectively.

[0076] The step-by-step guide unit can provide the guide in multiple languages ​​to accommodate international users. The step-by-step guide unit, for example, provides the guide in multiple languages ​​to accommodate international users. For example, the guide is provided in English, Spanish, Chinese, etc. The step-by-step guide unit also provides the guide based on the user's language setting. For example, the guide language switches depending on the language set by the user. This allows the guide to be provided in multiple languages ​​to accommodate international users, making it usable by a wider range of users.

[0077] The step-by-step guide unit can use the emotion estimation function to analyze whether the user is enjoying themselves and make suggestions to increase their enjoyment. The step-by-step guide unit, for example, analyzes the user's emotions and analyzes whether they are enjoying themselves. For example, if the user is enjoying themselves, it provides a message such as "Keep it up!". The step-by-step guide unit also uses the emotion estimation function to analyze whether the user is enjoying themselves and make suggestions to increase their enjoyment. For example, if the user is enjoying themselves, it suggests an activity that will make them even more enjoyable. In this way, the emotion estimation function is used to analyze whether the user is enjoying themselves and make suggestions to increase their enjoyment, making the user's cooking experience more enjoyable.

[0078] The video and image providing unit can highlight parts of a video or image that are of particular interest to the user. For example, the video and image providing unit highlights parts of a video or image that are likely to be of particular interest to the user. For example, it may highlight the finished dish or the presentation of the dish. The video and image providing unit may also highlight parts that attract the user's interest. For example, it may highlight cooking tips or how to handle specific ingredients. In this way, by highlighting parts of a video or image that are likely to be of particular interest to the user, the user's interest is piqued and their understanding is deepened.

[0079] The video and image providing unit can provide a function to adjust the video playback speed to suit the user's pace. The video and image providing unit provides, for example, a function to adjust the video playback speed to suit the user's pace. For example, the playback speed can be slowed down for beginners and increased for advanced learners. The video and image providing unit also adjusts the playback speed based on the user's operation history and learning speed. For example, the video and image providing unit suggests an optimal playback speed based on the playback speed of videos the user has previously watched. In this way, by providing a function to adjust the video playback speed to suit the user's pace, the user can learn at their own pace.

[0080] The video and image providing unit can use the emotion estimation function to provide videos or images in a format that is visually easy for the user to understand. The video and image providing unit, for example, analyzes the user's emotions and provides videos and images in a format that is visually easy for the user to understand. For example, if the user is confused, a detailed explanation is added. The video and image providing unit also uses the emotion estimation function to provide videos and images in a format that is visually easy for the user to understand. For example, explanations are provided using animations or infographics. In this way, the emotion estimation function is used to provide videos and images in a format that is visually easy for the user to understand, thereby deepening the user's understanding.

[0081] The video and image providing unit can add an interactive function that allows users to post comments or questions to videos or images. The video and image providing unit adds an interactive function that allows users to post comments or questions to videos or images, for example. For example, a question about a specific cooking procedure can be posted. The video and image providing unit also provides a function that allows users to post comments and questions in real time. For example, a question that arises while cooking can be asked on the spot. In this way, adding an interactive function that allows users to post comments and questions to videos or images can resolve users' doubts and deepen their understanding.

[0082] The video and image providing unit can provide videos or images in a format optimized for the user's device. For example, the video and image providing unit provides videos and images in a format optimized for the user's device. For example, it provides them in a resolution and format suitable for a smartphone or tablet. The video and image providing unit also provides videos and images in a format optimal for the user's device. For example, it provides them in a format suitable for a desktop computer or a laptop. In this way, by providing videos and images in a format optimized for the user's device, the user can view them comfortably.

[0083] The video and image providing unit can use the emotion estimation function to analyze the emotion of the user when watching a video or image and provide additional information at the optimal timing. The video and image providing unit, for example, analyzes the user's emotion and analyzes the emotion when watching a video or image. For example, if the user is interested, it provides related additional information. The video and image providing unit also uses the emotion estimation function to analyze the emotion of the user when watching a video or image and provides additional information at the optimal timing. For example, if the user is confused, it adds a detailed explanation. In this way, the emotion estimation function is used to analyze the emotion of the user when watching a video or image and provides additional information at the optimal timing, thereby deepening the user's understanding.

[0084] The progress management unit can record the progress of the user's cooking skills in detail and provide specific areas for improvement. The progress management unit, for example, records the progress of the user's cooking skills in detail and provides feedback on specific areas for improvement. For example, it evaluates cooking time and degree of completion and suggests areas for improvement. The progress management unit also records the progress of the user's cooking skills in detail and provides specific areas for improvement. For example, it provides advice on areas for improvement in cooking methods and how to select ingredients. In this way, by recording the progress of the user's cooking skills in detail and providing feedback on specific areas for improvement, the user's skills can be supported.

[0085] The progress management unit can provide recipes of varying difficulty levels in stages based on the improvement of the user's cooking skills. For example, the progress management unit provides recipes of varying difficulty levels in stages according to the improvement of the user's cooking skills. For example, starting with recipes for beginners and gradually increasing the difficulty level. The progress management unit also evaluates the improvement of the user's cooking skills and provides recipes based on that evaluation. For example, when the user has mastered a particular skill, it suggests a more difficult recipe as the next step. In this way, by providing recipes of varying difficulty levels in stages according to the improvement of the user's cooking skills, the user can improve their skills without straining themselves.

[0086] The progress management unit can use the emotion estimation function to provide encouraging messages to maintain the user's motivation. The progress management unit, for example, analyzes the user's emotions and sends encouraging messages to maintain the user's motivation. For example, if the user is feeling down, it provides a message such as "You'll do better next time." The progress management unit also uses the emotion estimation function to provide encouraging messages to maintain the user's motivation. For example, if the user is experiencing difficulties, it provides a message such as "You can do it." In this way, by using the emotion estimation function to send encouraging messages to maintain the user's motivation, the user can maintain their motivation to continue learning cooking.

[0087] The progress management unit can provide the results of progress management in a format that users can share and provide a function for competing with other users. For example, the progress management unit provides the results of progress management in a format that users can share on social media or messaging apps. For example, users can share photos of the finished dish or graphs showing skill improvement. The progress management unit also provides a function for users to compete with other users. For example, users can compete through a ranking function or by earning badges. In this way, by providing the results of progress management in a format that users can share and adding a function for users to compete with other users, it is possible to increase users' motivation.

[0088] The progress management unit can customize the feedback based on the user's preferences and provide more personalized advice. For example, the progress management unit customizes the feedback according to the user's preferences and provides personalized advice. For example, it provides advice that matches the user's preferred cooking style. The progress management unit also provides feedback based on the user's past cooking history and preferences. For example, it specifically indicates areas for improvement in dishes the user has made in the past. In this way, the feedback can be customized according to the user's preferences and more personalized advice can be provided, thereby supporting the user's skill improvement.

[0089] The progress management unit can use the emotion estimation function to analyze the emotion the user feels when receiving feedback and provide positive feedback. The progress management unit, for example, analyzes the user's emotion and analyzes the emotion when receiving feedback. For example, if the user has positive emotions, it provides feedback that emphasizes those emotions. The progress management unit also uses the emotion estimation function to analyze the emotion the user feels when receiving feedback and provides positive feedback. For example, if the user is confident, it provides a message that reinforces that confidence. In this way, the emotion estimation function can be used to analyze the emotion the user feels when receiving feedback and emphasize positive feedback, thereby increasing the user's motivation.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The cooking assistance system can further include a storage advice unit that suggests methods for storing ingredients. The storage advice unit suggests, for example, how to store ingredients purchased by the user. For example, it provides methods for storing vegetables and freezing meat. The storage advice unit can also manage the expiration dates of ingredients and notify the user to use them at the appropriate time. This allows the user to avoid waste and use ingredients efficiently.

[0092] The cooking assistance system can further include a health management unit that monitors the user's health condition and suggests healthy recipes. The health management unit, for example, monitors the user's blood pressure and blood sugar levels and customizes recipes based on the results. For example, it can suggest low-salt recipes to a user with high blood pressure. The health management unit can also provide recipes that match the user's health goals. This allows the user to enjoy cooking while maintaining their health.

[0093] The cooking assistance system may further include a shopping assistance unit that analyzes the user's ingredient purchase history and generates an efficient shopping list. The shopping assistance unit, for example, lists necessary ingredients based on the user's ingredient purchase history. For example, ingredients required for a specific recipe may be automatically added to the list. The shopping assistance unit may also customize the shopping list taking into account the user's preferences and allergy information. This allows the user to shop efficiently.

[0094] The cooking assistance system may further include a skill evaluation unit that evaluates the user's cooking skills and provides a training program to improve the skills. The skill evaluation unit may, for example, evaluate the user's cooking quality and cooking time, and suggest a training program based on the evaluation. For example, the skill evaluation unit may provide a video tutorial on how to use a knife or adjust the heat. The skill evaluation unit may also monitor the user's progress and provide appropriate feedback. This allows the user to effectively improve their cooking skills.

[0095] The cooking assistance system can further include an inventory management unit that manages the user's inventory of ingredients and automatically orders the necessary ingredients. The inventory management unit, for example, monitors the inventory in the user's refrigerator or pantry and lists the necessary ingredients. For example, if milk is running low, it is automatically ordered. The inventory management unit can also manage inventory taking into account the user's preferences and allergy information. This allows the user to always have the necessary ingredients on hand.

[0096] The cooking assistance system can also estimate the user's emotions and adjust the difficulty of the cooking based on the estimated emotions. For example, if the user is feeling stressed, it can suggest easy recipes, and if the user is feeling relaxed, it can suggest more challenging recipes. The emotion estimation function can also be used to analyze whether the user is enjoying cooking and make suggestions to increase the enjoyment. This allows users to enjoy cooking according to their mood.

[0097] The cooking assistance system can also estimate the user's emotions and provide advice during cooking based on the estimated emotions. For example, if the user is confused, it can provide detailed explanations, and if the user is confident, it can provide concise advice. The emotion estimation function can also be used to provide advice to reduce the stress the user feels while cooking. This allows the user to cook while receiving support tailored to their emotions.

[0098] The cooking assistance system can also estimate the user's emotions and adjust the cooking progress based on the estimated emotions. For example, if the user is in a hurry, it will extend the cooking time, and if the user is relaxed, it will suggest the normal cooking time. The emotion estimation function can also be used to analyze whether the user is enjoying cooking and make suggestions to increase the enjoyment. This allows the user to cook at a pace that suits their mood.

[0099] The cooking assistance system can also estimate the user's emotions and suggest food presentations based on the estimated emotions. For example, if the user is celebrating a special occasion, it can suggest a more colorful presentation, and if the meal is an everyday meal, it can suggest a simpler presentation. The emotion estimation function can also be used to analyze whether the user is enjoying cooking and suggest presentations to enhance the enjoyment. This allows users to enjoy food presentations that match their mood.

[0100] The cooking assistance system can also estimate the user's emotions and provide feedback on the cooking process based on the estimated emotions. For example, if the user is satisfied, it can provide positive feedback, and if the user is dissatisfied, it can suggest areas for improvement. The emotion estimation function can also be used to analyze whether the user is enjoying cooking and provide feedback to increase the enjoyment. This allows the user to receive feedback that is tailored to their emotions.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The user information collection unit collects the user's food preferences, allergy information, and available cooking equipment. For example, the user enters information such as "I like chicken, I'm allergic to peanuts, and I own an oven." The user information collection unit saves this information in a database and provides it to the recipe generation unit. Step 2: The recipe generation unit generates a customized recipe based on the information collected by the user information collection unit. For example, the generation AI generates an optimal recipe taking into account the user's preferences and allergy information. The generation AI generates the recipe using a text generation AI (e.g., LLM). The generation AI can also generate the content of the recipe using a multimodal generation AI. Step 3: The question answering unit responds to questions in real time based on the recipe generated by the recipe generation unit. For example, when a user inputs a question such as "How long should I reduce this sauce?", the generation AI provides an appropriate answer to the question. The generation AI generates an answer to the user's question using text generation AI. The generation AI can also generate answers to questions using multimodal generation AI.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] 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.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] 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]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a user information collection unit that collects information about the user's food preferences, allergy information, and available cooking utensils; a recipe generation unit that generates a customized recipe based on the information collected by the user information collection unit; a question answering unit that answers questions in real time based on the recipe generated by the recipe generating unit; A system characterized by:

2. The user information collection unit Analyzes the user's cooking history and suggests new ingredients or changes in preferences 2. The system of claim 1.

3. The user information collection unit It also collects information about the food preferences and allergies of the user's family or housemates to provide recipes that will satisfy everyone.

2. The system of claim 1.

4. The question answering unit Refer to the user's past question history to provide more personalized answers 2. The system of claim 1.

5. The step-by-step guide section Monitor the progress of each step in real time and let you know when it's time to move on to the next step 2. The system of claim 1.

6. Video and image providers: Highlighting parts of a video or image that are of particular interest to the user 2. The system of claim 1.

7. The Progress Management Department Provide encouraging messages to keep users motivated 2. The system of claim 1.

8. The user information collection unit Suggest recipes based on the user's current mood 2. The system of claim 1.

Images