System

Abstract

An object of a system according to an embodiment is to enable even a user who has forgotten something to cook without anxiety.SOLUTION: A system according to an embodiment includes a reminder generation unit, a recipe display unit, and an ingredient confirmation unit. The reminder generation unit generates a reminder for prompting a user to start cooking on the basis of a schedule or a past cooking history of the user. The recipe display unit automatically displays a necessary recipe on the basis of the reminder generated by the reminder generation unit and guides the recipe by voice. The ingredient confirmation unit confirms necessary ingredients based on the recipe displayed by the recipe display unit, and prompts preparation.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 techniques have had the problem that forgetful users may forget to check the recipe when cooking.

[0005] The system according to the embodiment aims to enable even forgetful users to cook with peace of mind. [Means for solving the problem]

[0006] The system according to the embodiment includes a reminder generation unit, a recipe display unit, and an ingredient confirmation unit. The reminder generation unit generates a reminder to prompt the user to start cooking based on the user's schedule or past cooking history. The recipe display unit automatically displays the necessary recipe based on the reminder generated by the reminder generation unit and provides audio guidance. The ingredient confirmation unit confirms the necessary ingredients based on the recipe displayed by the recipe display unit and prompts the user to prepare them. [Effects of the Invention]

[0007] The system according to the embodiment can enable even forgetful users to cook with peace of mind. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The support system according to an embodiment of the present invention is a system that provides support to people who forget recipes due to illness or other reasons. This system provides support from the pre-cooking stage, allowing people to cook with peace of mind even if they forget. As a result, the support system allows people who forget recipes due to illness or other reasons to cook with peace of mind.

[0029] A support system according to an embodiment includes a reminder generation unit, a recipe display unit, and an ingredient confirmation unit. The reminder generation unit generates a reminder to prompt the user to start cooking based on the user's schedule or past cooking history. For example, the reminder generation unit analyzes data from the user's calendar app and notifies the user of the cooking start time. The reminder generation unit can also generate a reminder to prompt the user to start cooking based on the user's past cooking history. For example, the reminder can remind the user to make curry next time based on the date of the previous curry. The recipe display unit automatically displays a necessary recipe based on the reminder generated by the reminder generation unit and provides audio guidance. For example, when the user inputs the name of a dish they want to make, the recipe display unit displays the recipe for that dish and provides audio instructions with step-by-step instructions. The recipe display unit can also display necessary ingredients based on the user's ingredient list and provide audio guidance. For example, the recipe display unit provides instructions such as, "First, finely chop the onion." The ingredient confirmation unit confirms the necessary ingredients based on the recipe displayed by the recipe display unit and prompts the user to prepare them. For example, the ingredient confirmation unit displays a list of ingredients for a dish the user wants to make and confirms whether the necessary ingredients are available. The ingredient confirmation unit can also provide audio guidance when the user is preparing ingredients. For example, the ingredient confirmation unit may confirm by saying, "To make curry, you need onions, carrots, potatoes, and curry powder. Do you have all of these?" This allows the support system according to the embodiment to allow even those who forget recipes due to illness or other reasons to cook with peace of mind. For example, the reminder generation unit may remind the user to start cooking based on the user's schedule, and the recipe display unit may automatically display the necessary recipe and provide audio guidance. The ingredient confirmation unit confirms the necessary ingredients and prompts the user to prepare them, allowing the user to start cooking smoothly.

[0030] The reminder generation unit can customize the content of the reminder based on the user's past cooking success or failure rate and suggest recipes that are likely to be successful. For example, the reminder generation unit uses a generation AI to analyze the user's past cooking history and suggest recipes with a high success rate as a reminder. For example, the reminder generation unit sends a reminder based on a list of recipes that the user has successfully made in the past. The reminder generation unit also avoids recipes with a high failure rate for the user and suggests recipes that are likely to be successful as a reminder. For example, it excludes dishes with a high failure rate from the list and prioritizes suggesting dishes with a high success rate. The reminder generation unit also analyzes the user's cooking skill level and suggests recipes that match the skill as a reminder. For example, suggesting easy recipes for beginners increases the success rate. This allows the unit to suggest recipes that are likely to be successful based on the user's past cooking success or failure rate.

[0031] The reminder generation unit can enable the user to start cooking smoothly by including the preparation of cooking tools in the reminder. For example, the reminder generation unit generates a list of cooking tools using a generation AI and includes it in the reminder. For example, the reminder generation unit notifies the user in the form of, "Today is the day to make curry. Prepare a knife, cutting board, and pot." The reminder generation unit also includes frequently used tools in the reminder based on the user's past cooking history. For example, the reminder generation unit makes a list of tools that the user uses frequently and adds them to the reminder. The reminder generation unit also includes tools required for each cooking step in the reminder. For example, the reminder generation unit notifies the user in the form of, "Next, prepare a knife and cutting board to chop the onion." In this way, by including the preparation of cooking tools in the reminder, the user can start cooking smoothly.

[0032] The reminder generation unit can apply the reminder function to other housework or daily life tasks to build a comprehensive life support system. For example, the reminder generation unit uses a generation AI to analyze the user's schedule and notify them of housework or daily life tasks other than cooking as reminders. For example, the reminder generation unit can send a notification in the form of, "Today is laundry day. Let's get the washing machine ready." The reminder generation unit can also customize the content of reminders based on the user's past housework history. For example, it can list tasks that the user frequently forgets and notify them as reminders. The reminder generation unit can also centrally manage housework and daily life tasks to build a comprehensive life support system. For example, tasks such as cooking, cleaning, and shopping can be managed in a single app and reminders can be sent. This allows the reminder function to be applied to other housework and daily life tasks to build a comprehensive life support system.

[0033] The reminder generation unit can notify the user's family or caregiver of the reminder, thereby strengthening the support system. For example, the generation AI in the reminder generation unit registers the contact information of the user's family or caregiver and shares the reminder. For example, the reminder generation unit sends a notification in the form of, "Today is the day to make curry. Please help me prepare the ingredients." The reminder generation unit also strengthens the support system by having the user's family or caregiver receive the reminder. For example, a family member receives the reminder and prompts the user to start cooking. The reminder generation unit also shares the contents of the reminder with the family or caregiver, allowing them to jointly manage tasks. For example, a family member receives the reminder and cooks together with the user. This strengthens the support system by notifying the family and caregiver of the reminder.

[0034] The recipe display unit can provide personalized recipes by reflecting the user's preferences or allergy information. For example, the recipe display unit uses a generation AI to register the user's preferences and allergy information in a database and suggest recipes based on that. For example, it can provide recipes that take into account the user's favorite ingredients and ingredients they want to avoid. The recipe display unit can also analyze the user's past cooking history to suggest recipes that reflect the user's preferences and allergy information. For example, it can suggest recipes that the user likes based on data on dishes they have made in the past. The recipe display unit can also reflect the user's preferences and allergy information in real time in the recipe display and audio guide. For example, it can provide recipes that instantly reflect newly registered allergy information by the user. This makes it possible to provide personalized recipes based on the user's preferences and allergy information.

[0035] The recipe display unit can support the user in improving their cooking skills by including cooking tips or trivia in the audio guide. For example, the recipe display unit includes tips and trivia in the audio guide for each cooking step using a generation AI. For example, the recipe display unit may provide guidance such as, "When chopping onions, keep them in the refrigerator to prevent tears." The recipe display unit also provides appropriate tips and trivia according to the user's cooking skill level. For example, it provides basic tips for beginners and advanced techniques for advanced cooks. The recipe display unit also provides tips and trivia that are useful for improving skills based on the user's past cooking history. For example, it provides advice on how to improve a dish that failed in the past. In this way, the inclusion of cooking tips and trivia in the audio guide can support the user in improving their cooking skills.

[0036] The recipe display unit can make the automatic recipe display and audio guide compatible with other languages, making it available to international users. For example, the recipe display unit uses a generation AI to make the automatic recipe display and audio guide compatible with multiple languages. For example, recipes are provided in multiple languages, such as English, French, and Chinese. The recipe display unit also automatically switches the recipe display and language guide depending on the user's language setting. For example, if the user selects English, the recipe is displayed and audio guide is provided in English. The recipe display unit also builds a multilingual recipe database, making it available to international users. For example, recipes in each language are registered in the database and provided according to the user's language setting. This makes it possible to make the automatic recipe display and audio guide compatible with other languages, making it available to international users.

[0037] The recipe display unit can display the recipe directly in the user's field of vision using smart glasses or a projector. For example, the generation AI uses smart glasses to display the recipe directly in the user's field of vision. For example, the user wears smart glasses while cooking, and the recipe steps are displayed in the user's field of vision. The recipe display unit also uses a projector to project the recipe onto a kitchen wall or counter. For example, the user uses a projector while cooking to visually display the recipe steps. The recipe display unit also links the smart glasses or projector to simultaneously display the recipe and provide audio guidance. For example, the recipe is displayed in the user's field of vision while providing audio guidance. This allows the recipe to be displayed directly in the user's field of vision using smart glasses or a projector.

[0038] The ingredient confirmation unit can reflect the user's past purchase history or preferences in ingredient preparation and suggest the most suitable ingredients. For example, the ingredient confirmation unit uses a generation AI to analyze the user's past purchase history and suggest ingredients that match their preferences. For example, it lists and suggests ingredients that the user frequently purchases. The ingredient confirmation unit also registers the user's preferences in a database and suggests the most suitable ingredients based on that. For example, it generates an ingredient list that takes into account the user's favorite ingredients and ingredients that the user wants to avoid. The ingredient confirmation unit also suggests the most suitable ingredients based on the user's past cooking history. For example, it lists the necessary ingredients based on data on dishes made in the past. This makes it possible to suggest the most suitable ingredients based on the user's past purchase history and preferences.

[0039] The ingredient confirmation unit can send a reminder the day before cooking to prepare and check ingredients, encouraging advance preparation. For example, the generation AI can send a reminder the day before cooking to encourage ingredient preparation. For example, the notification can be in the form of, "Tomorrow is the day to make curry. Please check the ingredients." The ingredient confirmation unit can also analyze the user's schedule and send reminders at the optimal time. For example, it can send reminders during times when the user is relaxing. The ingredient confirmation unit can also include a list of ingredients in the reminder content to help the user prepare smoothly. For example, it can send a notification in the form of, "Tomorrow is the day to make curry. Please prepare onions, carrots, potatoes, and curry powder." This allows a reminder to be sent the day before cooking to encourage advance preparation.

[0040] The material confirmation unit can link the material preparation and confirmation functions with online shopping to automatically order missing materials. For example, the material confirmation unit uses a generation AI to list missing materials and link with online shopping sites to automatically order them. For example, it automatically purchases the necessary materials based on the user's account information. The material confirmation unit also selects the most suitable online shopping site based on the user's past purchase history and orders the materials. For example, it prioritizes sites that the user frequently uses. The material confirmation unit also uses the API of the online shopping site to build a system that automatically orders missing materials. For example, it obtains inventory information through the API and orders the necessary materials. This allows it to link with online shopping and automatically order missing materials.

[0041] The ingredient confirmation unit can apply ingredient preparation and confirmation to other housework or daily life tasks, building a comprehensive life support system. For example, the ingredient confirmation unit uses a generative AI to analyze the user's schedule and send reminders for housework or daily life tasks other than cooking. For example, it can send a notification such as, "Today is laundry day. Let's get the washing machine ready." The ingredient confirmation unit can also customize the content of reminders based on the user's past housework history. For example, it can list tasks that the user frequently forgets and send them as reminders. The ingredient confirmation unit can also centrally manage housework and daily life tasks, building a comprehensive life support system. For example, tasks such as cooking, cleaning, and shopping can be managed in a single app and reminders can be sent. This allows ingredient preparation and confirmation to be applied to other housework and daily life tasks, building a comprehensive life support system.

[0042] The recipe display unit reflects the user's past cooking history or failures and can notify the user in advance of areas where mistakes are likely to occur. For example, the recipe display unit uses a generation AI to analyze the user's past cooking history and notify the user in advance of areas where mistakes are likely to occur. For example, the unit may provide advice such as, "Since you burned your onions in the past, be careful with the heat this time." The recipe display unit also registers the user's past failures in a database and provides advice in real time based on that information. For example, the unit may notify the user of points to be careful about based on data from past failed dishes. The recipe display unit also reflects the user's past successful experiences in providing support during cooking. For example, the unit may provide advice to increase the success rate based on the points from past successful dishes. This allows the user's past cooking history and failures to be reflected and the user to be notified in advance of areas where mistakes are likely to occur.

[0043] The recipe display unit can provide personalized advice by reflecting the user's preferences or allergy information. For example, the recipe display unit uses a generation AI to register the user's preferences and allergy information in a database and provides personalized advice based on that. For example, advice using ingredients that the user likes is provided. The recipe display unit also provides advice that reflects the user's preferences and allergy information based on the user's past cooking history. For example, advice that the user likes is provided based on data on dishes made in the past. The recipe display unit also reflects the user's preferences and allergy information in real time when providing support during cooking. For example, advice that instantly reflects newly registered allergy information by the user is provided. This makes it possible to provide personalized advice based on the user's preferences and allergy information.

[0044] The recipe display unit can apply the support provided during cooking to other household chores or daily life tasks, thereby building a comprehensive life support system. For example, the recipe display unit uses a generation AI to analyze the user's schedule and provide support for household chores and daily life tasks other than cooking. For example, it can provide a notification such as, "Today is cleaning day. Get the vacuum cleaner ready." The recipe display unit can also customize the support content based on the user's past household chore history. For example, it can list tasks that the user frequently forgets and provide support. The recipe display unit can also centrally manage household chores and daily life tasks, building a comprehensive life support system. For example, a single app can manage tasks such as cooking, cleaning, and shopping, and provide support. This allows the support provided during cooking to be applied to other household chores and daily life tasks, thereby building a comprehensive life support system.

[0045] The recipe display unit can work with smart kitchen devices to automate the operation of cooking appliances. For example, the recipe display unit uses a generative AI to work with smart kitchen devices to automate the operation of cooking appliances. For example, a smart oven can be used to automatically cook at a specified temperature and time. The recipe display unit also optimizes the settings of the smart kitchen device based on the user's past cooking history. For example, it automates the operation of the device based on the settings of dishes that have been successfully made in the past. The recipe display unit also works with smart kitchen devices to support the operation of cooking appliances in real time. For example, a smart scale can be used to measure accurate portions. This allows the recipe display unit to work with smart kitchen devices to automate the operation of cooking appliances.

[0046] The recipe display unit can reflect the user's past feedback or preferences and provide advice that will be useful for the next cooking. For example, the recipe display unit uses a generation AI to analyze the user's past feedback and provide advice that will be useful for the next cooking based on that. For example, the recipe display unit may provide advice such as, "The curry you made last time was a little spicy, so this time, make it less spicy." The recipe display unit also registers the user's preferences in a database and provides feedback based on that. For example, the recipe display unit may provide advice that takes into account the user's favorite seasonings and ingredients. The recipe display unit may also provide advice that will be useful for the next cooking based on the user's past cooking history. For example, the recipe display unit may provide advice for the next cooking based on the key points of successful cooking in the past. This allows the recipe display unit to provide advice that will be useful for the next cooking based on the user's past feedback and preferences.

[0047] The recipe display unit can share post-cooking feedback with the user's family or friends, thereby strengthening social support. For example, the recipe display unit constructs a system in which a generation AI shares a user's post-cooking feedback with family and friends. For example, it notifies family and friends of feedback such as, "Today's curry was very delicious!" The recipe display unit also strengthens social support by sharing the user's feedback with family and friends. For example, family and friends receive the feedback and send messages praising the user. The recipe display unit also shares the content of the feedback with family and friends, allowing them to jointly consider ways to improve the cooking. For example, family and friends can provide advice for the next cooking session based on the feedback. In this way, sharing post-cooking feedback with family and friends can strengthen social support.

[0048] The recipe display unit can apply the post-cooking feedback and records to other housework or daily life tasks, building a comprehensive life support system. For example, the recipe display unit uses a generation AI to apply the user's post-cooking feedback and records to other housework or daily life tasks. For example, it provides feedback such as, "You cleaned up very well today!" The recipe display unit also customizes the content of the feedback based on the user's past housework history. For example, it can list and provide feedback on housework that the user frequently performs. The recipe display unit also centrally manages housework and daily life tasks, building a comprehensive life support system. For example, tasks such as cooking, cleaning, and shopping can be managed in a single app and feedback can be provided. This allows the post-cooking feedback and records to be applied to other housework and daily life tasks, building a comprehensive life support system.

[0049] The recipe display unit can link post-cooking feedback with an online community and share information with other users. For example, the recipe display unit uses a generation AI to link a user's post-cooking feedback with an online community and build a system for sharing information with other users. For example, feedback such as "Today's curry was very delicious!" is posted to the community. The recipe display unit also shares the user's feedback with the online community to collect advice and opinions from other users. For example, community members can provide advice for the next cooking step based on the feedback. The recipe display unit also shares the content of the feedback with the online community and the users can jointly consider ways to improve the cooking step. For example, community members can provide advice for the next cooking step based on the feedback. In this way, post-cooking feedback can be linked with the online community and information can be shared with other users.

[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 reminder generation unit can customize the content of the reminder based on the user's past cooking success rate or failure rate and suggest recipes that are likely to be successful. For example, the reminder generation unit can analyze the user's past cooking history and suggest recipes with a high success rate as a reminder. The reminder generation unit can also avoid recipes with a high failure rate and suggest recipes that are likely to be successful as a reminder. Furthermore, the reminder generation unit can analyze the user's cooking skill level and suggest recipes that match the skill as a reminder. This makes it possible to suggest recipes that are likely to be successful based on the user's past cooking success rate or failure rate.

[0052] The reminder generation unit can include a reminder for preparing the utensils needed for cooking in the reminder, allowing the user to start cooking smoothly. For example, the reminder generation unit generates a list of utensils needed for cooking and includes it in the reminder. The reminder generation unit can also include frequently used utensils in the reminder based on the user's past cooking history. Furthermore, the reminder generation unit can also include utensils needed for each cooking step in the reminder. In this way, by including a reminder for preparing the utensils needed for cooking in the reminder, the user can start cooking smoothly.

[0053] The reminder generation unit can apply the reminder function to other housework or daily life tasks to build a comprehensive life support system. For example, the reminder generation unit analyzes the user's schedule and notifies them of housework or daily life tasks other than cooking as reminders. The reminder generation unit can also customize the content of reminders based on the user's past housework history. Furthermore, the reminder generation unit can centrally manage housework and daily life tasks to build a comprehensive life support system. This makes it possible to apply the reminder function to other housework and daily life tasks to build a comprehensive life support system.

[0054] The reminder generation unit can notify the user's family or caregiver of a reminder, thereby strengthening the support system. For example, the reminder generation unit can register contact information for the user's family or caregiver and share reminders. The reminder generation unit can also strengthen the support system by having the user's family or caregiver receive reminders. Furthermore, the reminder generation unit can share the contents of the reminder with the family or caregiver and jointly manage tasks. In this way, the support system can be strengthened by notifying the family or caregiver of the reminder.

[0055] The recipe display unit can provide personalized recipes by reflecting the user's preferences or allergy information. For example, the recipe display unit can register the user's preferences and allergy information in a database and suggest recipes based on that information. The recipe display unit can also analyze the user's past cooking history and suggest recipes that reflect the user's preferences and allergy information. Furthermore, the recipe display unit can reflect the user's preferences and allergy information in the recipe display and audio guide in real time. This allows personalized recipes to be provided based on the user's preferences and allergy information.

[0056] The recipe display unit can support the user in improving their cooking skills by including cooking tips or trivia in the audio guide. For example, the recipe display unit can include tips and trivia in the audio guide for each cooking step. The recipe display unit can also provide appropriate tips and trivia according to the user's cooking skill level. Furthermore, the recipe display unit can also provide tips and trivia that are useful for skill improvement based on the user's past cooking history. In this way, the inclusion of cooking tips and trivia in the audio guide can support the user in improving their cooking skills.

[0057] The recipe display unit can support automatic recipe display and audio guidance in other languages, making it available to international users. For example, the recipe display unit can support automatic recipe display and audio guidance in multiple languages. The recipe display unit can also automatically switch the recipe display and language guidance depending on the user's language setting. Furthermore, the recipe display unit can build a recipe database that supports multiple languages, making it available to international users. This allows automatic recipe display and audio guidance to support other languages, making it available to international users.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The reminder generation unit generates a reminder to encourage the user to start cooking based on the user's schedule or past cooking history. For example, the reminder generation unit analyzes data from the user's calendar app and notifies the user of the time to start cooking. The reminder generation unit can also generate a reminder to encourage the user to start cooking based on the user's past cooking history. For example, the reminder generation unit may remind the user to make curry next time based on the last date that curry was made. Step 2: The recipe display unit automatically displays the necessary recipe based on the reminder generated by the reminder generation unit and provides audio guidance. For example, when the user inputs the name of a dish they want to make, the recipe display unit displays the recipe for that dish and provides audio instructions with step-by-step instructions. The recipe display unit can also display the necessary ingredients based on the user's ingredient list and provide audio guidance. For example, the recipe display unit provides instructions such as, "First, chop the onion." Step 3: The ingredient confirmation unit confirms the necessary ingredients based on the recipe displayed by the recipe display unit and prompts the user to prepare them. For example, the ingredient confirmation unit displays a list of ingredients for the dish the user wants to make and confirms whether the necessary ingredients are available. The ingredient confirmation unit can also provide audio guidance when the user is preparing the ingredients. For example, the ingredient confirmation unit may confirm by saying, "To make curry, you need onions, carrots, potatoes, and curry powder. Do you have all of these?"

[0060] (Example 2) The support system according to an embodiment of the present invention is a system that provides support to people who forget recipes due to illness or other reasons. This system provides support from the pre-cooking stage, allowing people to cook with peace of mind even if they forget. As a result, the support system allows people who forget recipes due to illness or other reasons to cook with peace of mind.

[0061] A support system according to an embodiment includes a reminder generation unit, a recipe display unit, and an ingredient confirmation unit. The reminder generation unit generates a reminder to prompt the user to start cooking based on the user's schedule or past cooking history. For example, the reminder generation unit analyzes data from the user's calendar app and notifies the user of the cooking start time. The reminder generation unit can also generate a reminder to prompt the user to start cooking based on the user's past cooking history. For example, the reminder can remind the user to make curry next time based on the date of the previous curry. The recipe display unit automatically displays a necessary recipe based on the reminder generated by the reminder generation unit and provides audio guidance. For example, when the user inputs the name of a dish they want to make, the recipe display unit displays the recipe for that dish and provides audio instructions with step-by-step instructions. The recipe display unit can also display necessary ingredients based on the user's ingredient list and provide audio guidance. For example, the recipe display unit provides instructions such as, "First, finely chop the onion." The ingredient confirmation unit confirms the necessary ingredients based on the recipe displayed by the recipe display unit and prompts the user to prepare them. For example, the ingredient confirmation unit displays a list of ingredients for a dish the user wants to make and confirms whether the necessary ingredients are available. The ingredient confirmation unit can also provide audio guidance when the user is preparing ingredients. For example, the ingredient confirmation unit may confirm by saying, "To make curry, you need onions, carrots, potatoes, and curry powder. Do you have all of these?" This allows the support system according to the embodiment to allow even those who forget recipes due to illness or other reasons to cook with peace of mind. For example, the reminder generation unit may remind the user to start cooking based on the user's schedule, and the recipe display unit may automatically display the necessary recipe and provide audio guidance. The ingredient confirmation unit confirms the necessary ingredients and prompts the user to prepare them, allowing the user to start cooking smoothly.

[0062] The reminder generation unit can analyze the user's physical condition or mood using an emotion estimation function and send reminders at the optimal timing. For example, the reminder generation unit uses a generation AI to collect the user's physical condition data and send reminders when the user is feeling well. For example, the reminder generation unit analyzes the user's heart rate and sleep data and sends reminders when it is determined that the user is feeling well. The reminder generation unit also uses the emotion estimation function to analyze the user's mood in real time and send reminders when the user is feeling well. For example, the reminder generation unit analyzes the user's facial expressions and voice tone and sends reminders when the user is feeling strongly positive. The reminder generation unit also predicts the optimal timing to send reminders based on the user's past physical condition and mood data. For example, the reminder generation unit analyzes past data and sends reminders when the user is most relaxed. This allows reminders to be sent at the optimal timing based on the user's physical condition and mood.

[0063] The reminder generation unit can customize the content of the reminder based on the user's past cooking success or failure rate and suggest recipes that are likely to be successful. For example, the reminder generation unit uses a generation AI to analyze the user's past cooking history and suggest recipes with a high success rate as a reminder. For example, the reminder generation unit sends a reminder based on a list of recipes that the user has successfully made in the past. The reminder generation unit also avoids recipes with a high failure rate for the user and suggests recipes that are likely to be successful as a reminder. For example, it excludes dishes with a high failure rate from the list and prioritizes suggesting dishes with a high success rate. The reminder generation unit also analyzes the user's cooking skill level and suggests recipes that match the skill as a reminder. For example, suggesting easy recipes for beginners increases the success rate. This allows the unit to suggest recipes that are likely to be successful based on the user's past cooking success or failure rate.

[0064] The reminder generation unit can enable the user to start cooking smoothly by including the preparation of cooking tools in the reminder. For example, the reminder generation unit generates a list of cooking tools using a generation AI and includes it in the reminder. For example, the reminder generation unit notifies the user in the form of, "Today is the day to make curry. Prepare a knife, cutting board, and pot." The reminder generation unit also includes frequently used tools in the reminder based on the user's past cooking history. For example, the reminder generation unit makes a list of tools that the user uses frequently and adds them to the reminder. The reminder generation unit also includes tools required for each cooking step in the reminder. For example, the reminder generation unit notifies the user in the form of, "Next, prepare a knife and cutting board to chop the onion." In this way, by including the preparation of cooking tools in the reminder, the user can start cooking smoothly.

[0065] The reminder generation unit can apply the reminder function to other housework or daily life tasks to build a comprehensive life support system. For example, the reminder generation unit uses a generation AI to analyze the user's schedule and notify them of housework or daily life tasks other than cooking as reminders. For example, the reminder generation unit can send a notification in the form of, "Today is laundry day. Let's get the washing machine ready." The reminder generation unit can also customize the content of reminders based on the user's past housework history. For example, it can list tasks that the user frequently forgets and notify them as reminders. The reminder generation unit can also centrally manage housework and daily life tasks to build a comprehensive life support system. For example, tasks such as cooking, cleaning, and shopping can be managed in a single app and reminders can be sent. This allows the reminder function to be applied to other housework and daily life tasks to build a comprehensive life support system.

[0066] The reminder generation unit can notify the user's family or caregiver of the reminder, thereby strengthening the support system. For example, the generation AI in the reminder generation unit registers the contact information of the user's family or caregiver and shares the reminder. For example, the reminder generation unit sends a notification in the form of, "Today is the day to make curry. Please help me prepare the ingredients." The reminder generation unit also strengthens the support system by having the user's family or caregiver receive the reminder. For example, a family member receives the reminder and prompts the user to start cooking. The reminder generation unit also shares the contents of the reminder with the family or caregiver, allowing them to jointly manage tasks. For example, a family member receives the reminder and cooks together with the user. This strengthens the support system by notifying the family and caregiver of the reminder.

[0067] The reminder generation unit can use the emotion estimation function to analyze the emotional response of the user when receiving a reminder and generate reminder content that elicits a positive response. The reminder generation unit, for example, uses the emotion estimation function to analyze the emotional response of the user when receiving a reminder in real time. For example, it analyzes the user's facial expression and voice tone and calculates an emotion score. The reminder generation unit also generates reminder content that elicits a positive response based on the user's emotional response data. For example, it sends a reminder that makes the user feel happy or excited. The reminder generation unit also dynamically adjusts the reminder content based on the emotion estimation data. For example, if the user's emotion is negative, it sends a reminder that includes an encouraging message. This makes it possible to analyze the emotional response of the user when receiving a reminder and generate reminder content that elicits a positive response.

[0068] The recipe display unit can analyze the user's cooking skill level using an emotion estimation function and suggest recipes with an appropriate level of difficulty. For example, the recipe display unit uses a generation AI to analyze the user's past cooking history and assess the cooking skill level. For example, the skill level is calculated based on the number of successful dishes and the difficulty level. The recipe display unit also uses the emotion estimation function to analyze the user's emotional reactions while cooking and assess the skill level. For example, the skill level is determined based on stress and satisfaction while cooking. The recipe display unit also suggests recipes according to the user's skill level. For example, it suggests simple recipes for beginners and complex recipes for advanced cooks. This makes it possible to suggest recipes with an appropriate level of difficulty according to the user's cooking skill level.

[0069] The recipe display unit can provide personalized recipes by reflecting the user's preferences or allergy information. For example, the recipe display unit uses a generation AI to register the user's preferences and allergy information in a database and suggest recipes based on that. For example, it can provide recipes that take into account the user's favorite ingredients and ingredients they want to avoid. The recipe display unit can also analyze the user's past cooking history to suggest recipes that reflect the user's preferences and allergy information. For example, it can suggest recipes that the user likes based on data on dishes they have made in the past. The recipe display unit can also reflect the user's preferences and allergy information in real time in the recipe display and audio guide. For example, it can provide recipes that instantly reflect newly registered allergy information by the user. This makes it possible to provide personalized recipes based on the user's preferences and allergy information.

[0070] The recipe display unit can support the user in improving their cooking skills by including cooking tips or trivia in the audio guide. For example, the recipe display unit includes tips and trivia in the audio guide for each cooking step using a generation AI. For example, the recipe display unit may provide guidance such as, "When chopping onions, keep them in the refrigerator to prevent tears." The recipe display unit also provides appropriate tips and trivia according to the user's cooking skill level. For example, it provides basic tips for beginners and advanced techniques for advanced cooks. The recipe display unit also provides tips and trivia that are useful for improving skills based on the user's past cooking history. For example, it provides advice on how to improve a dish that failed in the past. In this way, the inclusion of cooking tips and trivia in the audio guide can support the user in improving their cooking skills.

[0071] The recipe display unit can make the automatic recipe display and audio guide compatible with other languages, making it available to international users. For example, the recipe display unit uses a generation AI to make the automatic recipe display and audio guide compatible with multiple languages. For example, recipes are provided in multiple languages, such as English, French, and Chinese. The recipe display unit also automatically switches the recipe display and language guide depending on the user's language setting. For example, if the user selects English, the recipe is displayed and audio guide is provided in English. The recipe display unit also builds a multilingual recipe database, making it available to international users. For example, recipes in each language are registered in the database and provided according to the user's language setting. This makes it possible to make the automatic recipe display and audio guide compatible with other languages, making it available to international users.

[0072] The recipe display unit can display the recipe directly in the user's field of vision using smart glasses or a projector. For example, the generation AI uses smart glasses to display the recipe directly in the user's field of vision. For example, the user wears smart glasses while cooking, and the recipe steps are displayed in the user's field of vision. The recipe display unit also uses a projector to project the recipe onto a kitchen wall or counter. For example, the user uses a projector while cooking to visually display the recipe steps. The recipe display unit also links the smart glasses or projector to simultaneously display the recipe and provide audio guidance. For example, the recipe is displayed in the user's field of vision while providing audio guidance. This allows the recipe to be displayed directly in the user's field of vision using smart glasses or a projector.

[0073] The recipe display unit can use the emotion estimation function to analyze the emotional response of the user when viewing a recipe and generate recipe content that elicits a positive response. The recipe display unit, for example, uses the emotion estimation function to analyze the emotional response of the user when viewing a recipe in real time. For example, it analyzes the user's facial expression and tone of voice and calculates an emotion score. The recipe display unit also generates recipe content that elicits a positive response based on the user's emotional response data. For example, it suggests recipes that make the user feel happy or excited. The recipe display unit also dynamically adjusts the recipe content based on the emotion estimation data. For example, if the user's emotion is negative, it suggests an easy and fun recipe. This makes it possible to analyze the emotional response of the user when viewing a recipe and generate recipe content that elicits a positive response.

[0074] The ingredient confirmation unit can analyze inventory information in the user's refrigerator or pantry using an emotion estimation function and automatically list any missing ingredients. For example, the generation AI in the ingredient confirmation unit collects inventory information using cameras and sensors inside the refrigerator and lists any missing ingredients. For example, it analyzes images inside the refrigerator to confirm whether the necessary ingredients are available. The ingredient confirmation unit also registers the user's pantry inventory information in a database, and the generation AI uses that information to list any missing ingredients. For example, it checks the necessary ingredients based on the list of ingredients in the pantry. The ingredient confirmation unit also uses an emotion estimation function to analyze the user's emotional response when checking ingredients and generates an ingredient list that elicits a positive response. For example, it suggests an ingredient list that makes the user feel happy or relieved. This allows the inventory information in the user's refrigerator or pantry to be analyzed and any missing ingredients to be automatically listed.

[0075] The ingredient confirmation unit can reflect the user's past purchase history or preferences in ingredient preparation and suggest the most suitable ingredients. For example, the ingredient confirmation unit uses a generation AI to analyze the user's past purchase history and suggest ingredients that match their preferences. For example, it lists and suggests ingredients that the user frequently purchases. The ingredient confirmation unit also registers the user's preferences in a database and suggests the most suitable ingredients based on that. For example, it generates an ingredient list that takes into account the user's favorite ingredients and ingredients that the user wants to avoid. The ingredient confirmation unit also suggests the most suitable ingredients based on the user's past cooking history. For example, it lists the necessary ingredients based on data on dishes made in the past. This makes it possible to suggest the most suitable ingredients based on the user's past purchase history and preferences.

[0076] The ingredient confirmation unit can send a reminder the day before cooking to prepare and check ingredients, encouraging advance preparation. For example, the generation AI can send a reminder the day before cooking to encourage ingredient preparation. For example, the notification can be in the form of, "Tomorrow is the day to make curry. Please check the ingredients." The ingredient confirmation unit can also analyze the user's schedule and send reminders at the optimal time. For example, it can send reminders during times when the user is relaxing. The ingredient confirmation unit can also include a list of ingredients in the reminder content to help the user prepare smoothly. For example, it can send a notification in the form of, "Tomorrow is the day to make curry. Please prepare onions, carrots, potatoes, and curry powder." This allows a reminder to be sent the day before cooking to encourage advance preparation.

[0077] The material confirmation unit can link the material preparation and confirmation functions with online shopping to automatically order missing materials. For example, the material confirmation unit uses a generation AI to list missing materials and link with online shopping sites to automatically order them. For example, it automatically purchases the necessary materials based on the user's account information. The material confirmation unit also selects the most suitable online shopping site based on the user's past purchase history and orders the materials. For example, it prioritizes sites that the user frequently uses. The material confirmation unit also uses the API of the online shopping site to build a system that automatically orders missing materials. For example, it obtains inventory information through the API and orders the necessary materials. This allows it to link with online shopping and automatically order missing materials.

[0078] The ingredient confirmation unit can apply ingredient preparation and confirmation to other housework or daily life tasks, building a comprehensive life support system. For example, the ingredient confirmation unit uses a generative AI to analyze the user's schedule and send reminders for housework or daily life tasks other than cooking. For example, it can send a notification such as, "Today is laundry day. Let's get the washing machine ready." The ingredient confirmation unit can also customize the content of reminders based on the user's past housework history. For example, it can list tasks that the user frequently forgets and send them as reminders. The ingredient confirmation unit can also centrally manage housework and daily life tasks, building a comprehensive life support system. For example, tasks such as cooking, cleaning, and shopping can be managed in a single app and reminders can be sent. This allows ingredient preparation and confirmation to be applied to other housework and daily life tasks, building a comprehensive life support system.

[0079] The ingredient confirmation unit can use the emotion estimation function to analyze the emotional response of the user when checking the ingredients and generate a list of ingredients that will elicit a positive response. The ingredient confirmation unit, for example, uses the emotion estimation function to analyze the emotional response of the user when checking the ingredients in real time. For example, it analyzes the user's facial expression and tone of voice to calculate an emotion score. The ingredient confirmation unit also generates an ingredient list that will elicit a positive response based on the user's emotional response data. For example, it suggests an ingredient list that will make the user feel joyful or relieved. The ingredient confirmation unit also dynamically adjusts the contents of the ingredient list based on the emotion estimation data. For example, if the user's emotion is negative, it suggests an ingredient list for easy and fun dishes. In this way, the emotional response of the user when checking the ingredients can be analyzed and an ingredient list that will elicit a positive response can be generated.

[0080] The recipe display unit provides support while cooking and can provide real-time support if the user gets stuck or forgets something. For example, the recipe display unit uses a generation AI to monitor the user's behavior while cooking using cameras and sensors and analyze it using an emotion estimation function. For example, it analyzes the user's facial expressions and behavior and provides advice if the user is feeling stressed. The recipe display unit also provides appropriate advice in real time based on the user's past cooking history. For example, it notifies the user in advance of past failures, increasing the success rate. The recipe display unit also uses the emotion estimation function to analyze the user's emotional reactions while cooking and provides advice that elicits a positive reaction. For example, it provides advice that helps the user relax. This allows for real-time support to be provided if the user gets stuck or forgets something while cooking.

[0081] The recipe display unit reflects the user's past cooking history or failures and can notify the user in advance of areas where mistakes are likely to occur. For example, the recipe display unit uses a generation AI to analyze the user's past cooking history and notify the user in advance of areas where mistakes are likely to occur. For example, the unit may provide advice such as, "Since you burned your onions in the past, be careful with the heat this time." The recipe display unit also registers the user's past failures in a database and provides advice in real time based on that information. For example, the unit may notify the user of points to be careful about based on data from past failed dishes. The recipe display unit also reflects the user's past successful experiences in providing support during cooking. For example, the unit may provide advice to increase the success rate based on the points from past successful dishes. This allows the user's past cooking history and failures to be reflected and the user to be notified in advance of areas where mistakes are likely to occur.

[0082] The recipe display unit can provide personalized advice by reflecting the user's preferences or allergy information. For example, the recipe display unit uses a generation AI to register the user's preferences and allergy information in a database and provides personalized advice based on that. For example, advice using ingredients that the user likes is provided. The recipe display unit also provides advice that reflects the user's preferences and allergy information based on the user's past cooking history. For example, advice that the user likes is provided based on data on dishes made in the past. The recipe display unit also reflects the user's preferences and allergy information in real time when providing support during cooking. For example, advice that instantly reflects newly registered allergy information by the user is provided. This makes it possible to provide personalized advice based on the user's preferences and allergy information.

[0083] The recipe display unit can apply the support provided during cooking to other household chores or daily life tasks, thereby building a comprehensive life support system. For example, the recipe display unit uses a generation AI to analyze the user's schedule and provide support for household chores and daily life tasks other than cooking. For example, it can provide a notification such as, "Today is cleaning day. Get the vacuum cleaner ready." The recipe display unit can also customize the support content based on the user's past household chore history. For example, it can list tasks that the user frequently forgets and provide support. The recipe display unit can also centrally manage household chores and daily life tasks, building a comprehensive life support system. For example, a single app can manage tasks such as cooking, cleaning, and shopping, and provide support. This allows the support provided during cooking to be applied to other household chores and daily life tasks, thereby building a comprehensive life support system.

[0084] The recipe display unit can work with smart kitchen devices to automate the operation of cooking appliances. For example, the recipe display unit uses a generative AI to work with smart kitchen devices to automate the operation of cooking appliances. For example, a smart oven can be used to automatically cook at a specified temperature and time. The recipe display unit also optimizes the settings of the smart kitchen device based on the user's past cooking history. For example, it automates the operation of the device based on the settings of dishes that have been successfully made in the past. The recipe display unit also works with smart kitchen devices to support the operation of cooking appliances in real time. For example, a smart scale can be used to measure accurate portions. This allows the recipe display unit to work with smart kitchen devices to automate the operation of cooking appliances.

[0085] The recipe display unit can use the emotion estimation function to analyze the stress or anxiety the user feels while cooking and provide advice that elicits positive emotions. The recipe display unit, for example, uses the emotion estimation function to analyze the stress or anxiety the user feels while cooking in real time. For example, it analyzes the user's facial expression and voice tone and calculates an emotion score. The recipe display unit also provides advice that elicits positive emotions based on the user's emotional response data. For example, it provides advice that helps the user relax. The recipe display unit also dynamically adjusts the content of advice provided while cooking based on the emotion estimation data. For example, if the user's emotions are negative, it provides advice that includes an encouraging message. This makes it possible to analyze the stress or anxiety the user feels while cooking and provide advice that elicits positive emotions.

[0086] The recipe display unit provides feedback and records after cooking, collects feedback from the user, and can use it to improve the next cooking experience. For example, the recipe display unit uses a generation AI to analyze the user's emotions after cooking in real time using an emotion estimation function and emphasizes positive feedback. For example, it analyzes the user's facial expressions and tone of voice to calculate an emotion score. The recipe display unit also provides positive feedback based on the user's emotional response data. For example, it provides feedback in the form of, "Today's curry was very delicious!" The recipe display unit also dynamically adjusts the content of the feedback based on the emotion estimation data. For example, if the user's emotions are positive, it provides feedback that includes more praise. This allows the system to provide feedback and records after cooking and use it to improve the next cooking experience.

[0087] The recipe display unit can reflect the user's past feedback or preferences and provide advice that will be useful for the next cooking. For example, the recipe display unit uses a generation AI to analyze the user's past feedback and provide advice that will be useful for the next cooking based on that. For example, the recipe display unit may provide advice such as, "The curry you made last time was a little spicy, so this time, make it less spicy." The recipe display unit also registers the user's preferences in a database and provides feedback based on that. For example, the recipe display unit may provide advice that takes into account the user's favorite seasonings and ingredients. The recipe display unit may also provide advice that will be useful for the next cooking based on the user's past cooking history. For example, the recipe display unit may provide advice for the next cooking based on the key points of successful cooking in the past. This allows the recipe display unit to provide advice that will be useful for the next cooking based on the user's past feedback and preferences.

[0088] The recipe display unit can share post-cooking feedback with the user's family or friends, thereby strengthening social support. For example, the recipe display unit constructs a system in which a generation AI shares a user's post-cooking feedback with family and friends. For example, it notifies family and friends of feedback such as, "Today's curry was very delicious!" The recipe display unit also strengthens social support by sharing the user's feedback with family and friends. For example, family and friends receive the feedback and send messages praising the user. The recipe display unit also shares the content of the feedback with family and friends, allowing them to jointly consider ways to improve the cooking. For example, family and friends can provide advice for the next cooking session based on the feedback. In this way, sharing post-cooking feedback with family and friends can strengthen social support.

[0089] The recipe display unit can apply the post-cooking feedback and records to other housework or daily life tasks, building a comprehensive life support system. For example, the recipe display unit uses a generation AI to apply the user's post-cooking feedback and records to other housework or daily life tasks. For example, it provides feedback such as, "You cleaned up very well today!" The recipe display unit also customizes the content of the feedback based on the user's past housework history. For example, it can list and provide feedback on housework that the user frequently performs. The recipe display unit also centrally manages housework and daily life tasks, building a comprehensive life support system. For example, tasks such as cooking, cleaning, and shopping can be managed in a single app and feedback can be provided. This allows the post-cooking feedback and records to be applied to other housework and daily life tasks, building a comprehensive life support system.

[0090] The recipe display unit can link post-cooking feedback with an online community and share information with other users. For example, the recipe display unit uses a generation AI to link a user's post-cooking feedback with an online community and build a system for sharing information with other users. For example, feedback such as "Today's curry was very delicious!" is posted to the community. The recipe display unit also shares the user's feedback with the online community to collect advice and opinions from other users. For example, community members can provide advice for the next cooking step based on the feedback. The recipe display unit also shares the content of the feedback with the online community and the users can jointly consider ways to improve the cooking step. For example, community members can provide advice for the next cooking step based on the feedback. In this way, post-cooking feedback can be linked with the online community and information can be shared with other users.

[0091] The recipe display unit can use the emotion estimation function to analyze the satisfaction or sense of accomplishment the user feels after cooking and provide feedback that elicits positive emotions. The recipe display unit, for example, uses the emotion estimation function to analyze the satisfaction or sense of accomplishment the user feels after cooking in real time. For example, it analyzes the user's facial expression and voice tone to calculate an emotion score. The recipe display unit also provides feedback that elicits positive emotions based on the user's emotional response data. For example, it provides feedback in the form of, "Today's curry was very delicious!" The recipe display unit also dynamically adjusts the content of the feedback based on the emotion estimation data. For example, if the user's emotion is positive, it provides feedback that includes more praise. This makes it possible to analyze the satisfaction or sense of accomplishment the user feels after cooking and provide feedback that elicits positive emotions.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The reminder generation unit can analyze the user's physical condition or mood using an emotion estimation function and send reminders at optimal timing. For example, the reminder generation unit can analyze the user's heart rate and sleep data and send reminders when it is determined that the user is in good physical condition. The reminder generation unit can also analyze the user's facial expressions and voice tone and send reminders when the user is feeling a strong positive emotion. Furthermore, the reminder generation unit can predict the optimal timing to send reminders based on past physical condition and mood data. This allows reminders to be sent at optimal timings according to the user's physical condition and mood.

[0094] The reminder generation unit can customize the content of the reminder based on the user's past cooking success rate or failure rate and suggest recipes that are likely to be successful. For example, the reminder generation unit can analyze the user's past cooking history and suggest recipes with a high success rate as a reminder. The reminder generation unit can also avoid recipes with a high failure rate and suggest recipes that are likely to be successful as a reminder. Furthermore, the reminder generation unit can analyze the user's cooking skill level and suggest recipes that match the skill as a reminder. This makes it possible to suggest recipes that are likely to be successful based on the user's past cooking success rate or failure rate.

[0095] The reminder generation unit can include a reminder for preparing the utensils needed for cooking in the reminder, allowing the user to start cooking smoothly. For example, the reminder generation unit generates a list of utensils needed for cooking and includes it in the reminder. The reminder generation unit can also include frequently used utensils in the reminder based on the user's past cooking history. Furthermore, the reminder generation unit can also include utensils needed for each cooking step in the reminder. In this way, by including a reminder for preparing the utensils needed for cooking in the reminder, the user can start cooking smoothly.

[0096] The reminder generation unit can apply the reminder function to other housework or daily life tasks to build a comprehensive life support system. For example, the reminder generation unit analyzes the user's schedule and notifies them of housework or daily life tasks other than cooking as reminders. The reminder generation unit can also customize the content of reminders based on the user's past housework history. Furthermore, the reminder generation unit can centrally manage housework and daily life tasks to build a comprehensive life support system. This makes it possible to apply the reminder function to other housework and daily life tasks to build a comprehensive life support system.

[0097] The reminder generation unit can notify the user's family or caregiver of a reminder, thereby strengthening the support system. For example, the reminder generation unit can register contact information for the user's family or caregiver and share reminders. The reminder generation unit can also strengthen the support system by having the user's family or caregiver receive reminders. Furthermore, the reminder generation unit can share the contents of the reminder with the family or caregiver and jointly manage tasks. In this way, the support system can be strengthened by notifying the family or caregiver of the reminder.

[0098] The reminder generation unit can use the emotion estimation function to analyze the user's emotional response when receiving a reminder and generate reminder content that elicits a positive response. For example, the reminder generation unit analyzes the user's emotional response when receiving a reminder in real time. The reminder generation unit can also generate reminder content that elicits a positive response based on the user's emotional response data. Furthermore, the reminder generation unit can dynamically adjust the reminder content based on the emotion estimation data. This makes it possible to analyze the user's emotional response when receiving a reminder and generate reminder content that elicits a positive response.

[0099] The recipe display unit can analyze the user's cooking skill level using the emotion estimation function and suggest recipes with an appropriate level of difficulty. For example, the recipe display unit can analyze the user's past cooking history and evaluate the cooking skill level. The recipe display unit can also use the emotion estimation function to analyze the user's emotional reactions while cooking and evaluate the skill level. Furthermore, the recipe display unit can suggest recipes according to the user's skill level. This makes it possible to suggest recipes with an appropriate level of difficulty according to the user's cooking skill level.

[0100] The recipe display unit can provide personalized recipes by reflecting the user's preferences or allergy information. For example, the recipe display unit can register the user's preferences and allergy information in a database and suggest recipes based on that information. The recipe display unit can also analyze the user's past cooking history and suggest recipes that reflect the user's preferences and allergy information. Furthermore, the recipe display unit can reflect the user's preferences and allergy information in the recipe display and audio guide in real time. This allows personalized recipes to be provided based on the user's preferences and allergy information.

[0101] The recipe display unit can support the user in improving their cooking skills by including cooking tips or trivia in the audio guide. For example, the recipe display unit can include tips and trivia in the audio guide for each cooking step. The recipe display unit can also provide appropriate tips and trivia according to the user's cooking skill level. Furthermore, the recipe display unit can also provide tips and trivia that are useful for skill improvement based on the user's past cooking history. In this way, the inclusion of cooking tips and trivia in the audio guide can support the user in improving their cooking skills.

[0102] The recipe display unit can support automatic recipe display and audio guidance in other languages, making it available to international users. For example, the recipe display unit can support automatic recipe display and audio guidance in multiple languages. The recipe display unit can also automatically switch the recipe display and language guidance depending on the user's language setting. Furthermore, the recipe display unit can build a recipe database that supports multiple languages, making it available to international users. This allows automatic recipe display and audio guidance to support other languages, making it available to international users.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reminder generation unit generates a reminder to encourage the user to start cooking based on the user's schedule or past cooking history. For example, the reminder generation unit analyzes data from the user's calendar app and notifies the user of the time to start cooking. The reminder generation unit can also generate a reminder to encourage the user to start cooking based on the user's past cooking history. For example, the reminder generation unit may remind the user to make curry next time based on the last date that curry was made. Step 2: The recipe display unit automatically displays the necessary recipe based on the reminder generated by the reminder generation unit and provides audio guidance. For example, when the user inputs the name of a dish they want to make, the recipe display unit displays the recipe for that dish and provides audio instructions with step-by-step instructions. The recipe display unit can also display the necessary ingredients based on the user's ingredient list and provide audio guidance. For example, the recipe display unit provides instructions such as, "First, chop the onion." Step 3: The ingredient confirmation unit confirms the necessary ingredients based on the recipe displayed by the recipe display unit and prompts the user to prepare them. For example, the ingredient confirmation unit displays a list of ingredients for the dish the user wants to make and confirms whether the necessary ingredients are available. The ingredient confirmation unit can also provide audio guidance when the user is preparing the ingredients. For example, the ingredient confirmation unit may confirm by saying, "To make curry, you need onions, carrots, potatoes, and curry powder. Do you have all of these?"

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] 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 AI 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.

[0122] 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.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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 AI 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.

[0137] 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.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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 AI 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] 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]

[0172] 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 reminder generation unit that generates a reminder to prompt the user to start cooking based on the user's schedule or past cooking history; a recipe display unit that automatically displays a necessary recipe based on the reminder generated by the reminder generation unit and provides audio guidance; an ingredient confirmation unit that confirms necessary ingredients based on the recipe displayed by the recipe display unit and prompts the user to prepare the ingredients; A system characterized by:

2. The reminder generation unit Analyze the user's physical condition or mood with emotion estimation and send reminders at the optimal time.

2. The system of claim 1.

3. The reminder generation unit Based on the user's past cooking success or failure rate, the content of the reminder is customized to suggest recipes that are likely to be successful.

2. The system of claim 1.

4. The reminder generation unit Reminders include preparing the necessary cooking tools, helping users get started quickly.

2. The system of claim 1.

5. The reminder generation unit Apply the reminder function to other household or daily life tasks to create a comprehensive life support system 2. The system of claim 1.

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