System

Abstract

An object of a system according to an embodiment is to create a custom recipe on the basis of health information of a user and to manage ingredients in stock and expiration dates.SOLUTION: A system includes an acquisition part, a creation part, an instruction part, a proposal part, a management part, and a reminder part. The acquisition unit acquires health information of a user. The creation unit creates a custom recipe based on the health information acquired by the acquisition unit. The guidance unit provides cooking guidance based on the custom recipe created by the creation unit. The suggestion unit suggests an alternative to the ingredient on the basis of the cooking instructed by the instruction unit. The management part manages the stock food material or the expiration date based on the alternative proposed by the proposal part. The reminder unit performs food ingredient consumption reminder based on the stocked food ingredient or the use-by date managed by the management unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately create custom recipes based on the user's health information or manage inventory and expiration dates, leaving room for improvement.

[0005] The system according to the embodiment aims to create custom recipes based on the user's health information and manage ingredients in stock and expiration dates. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a creation unit, an instruction unit, a suggestion unit, a management unit, and a reminder unit. The acquisition unit acquires health information of a user. The creation unit creates a custom recipe based on the health information acquired by the acquisition unit. The instruction unit provides cooking instruction based on the custom recipe created by the creation unit. The suggestion unit suggests alternative ingredients based on cooking instruction given by the instruction unit. The management unit manages stock ingredients or expiration dates based on the alternatives suggested by the suggestion unit. The reminder unit provides ingredient consumption reminders based on stock ingredients or expiration dates managed by the management unit. [Effects of the Invention]

[0007] The system according to the embodiment can create custom recipes based on the user's health information and manage ingredients in stock and expiration dates. [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) An AI cooking assistant according to an embodiment of the present invention is a system that acquires a user's health information, creates custom recipes, provides cooking guidance, suggests ingredient substitutions, manages inventory and expiration dates, and reminds users when ingredients are consumed. The AI ​​cooking assistant acquires the user's health information and creates custom recipes based on that information. It then provides cooking guidance based on the custom recipes and suggests ingredient substitutions as needed. It also manages inventory and expiration dates and sends ingredient consumption reminders at appropriate times. For example, the AI ​​cooking assistant acquires the user's health information from a wearable device. It then creates custom recipes that take nutritional balance into consideration based on the acquired health information. It also provides real-time advice on cooking procedures, temperature control, and cooking times. It suggests ingredient substitutions based on allergy information and food culture. It manages inventory and expiration dates using barcode scanning and voice input, and sends reminders when ingredients are approaching their expiration dates. This allows the AI ​​cooking assistant to improve the user's cooking experience. The AI ​​cooking assistant can create custom recipes based on the user's health information, provide cooking guidance, suggest ingredient substitutions, manage inventory and expiration dates, and send ingredient consumption reminders. For example, it can track the stock and expiration dates of ingredients the user owns and remind them to consume them at the appropriate time. It can also suggest recipes that take into account the user's health condition and nutritional balance. This allows users to be provided with meals that are appropriate for their health condition, and if they have any trouble cooking, the AI ​​can provide immediate advice.

[0029] The AI ​​cooking assistant according to the embodiment includes an acquisition unit, a creation unit, an instruction unit, a suggestion unit, a management unit, and a reminder unit. The acquisition unit acquires a user's health information. The health information includes, but is not limited to, heart rate, blood pressure, and weight. The acquisition unit acquires the health information from, for example, a wearable device. The acquisition unit can also select the optimal acquisition method based on the user's input method (voice, text, image, etc.). The creation unit creates a custom recipe based on the acquired health information. The custom recipe is created taking into consideration, for example, nutritional balance and calorie restriction, but is not limited to, the example. The creation unit uses a generation AI to create a custom recipe based on the user's health information. The instruction unit provides cooking guidance based on the custom recipe created by the creation unit. The cooking guidance includes, for example, cooking procedures, temperature control, cooking time, etc., but is not limited to, the example. The instruction unit can provide advice on cooking procedures, temperature control, and cooking time in real time. The suggestion unit suggests alternative ingredients based on the cooking guidance provided by the instruction unit. The ingredient substitutes are proposed taking into consideration, for example, allergy information, food culture, etc., but are not limited to these examples. The suggestion unit can propose ingredient substitutes based on allergy information and food culture. The management unit manages inventory ingredients and expiration dates based on the alternatives proposed by the suggestion unit. The inventory ingredients and expiration dates can be managed using, for example, barcode scanning, voice input, etc., but are not limited to these examples. The management unit can manage inventory ingredients and expiration dates using barcode scanning or voice input. The reminder unit provides ingredient consumption reminders based on the inventory ingredients and expiration dates managed by the management unit. The reminder can, for example, notify the user about ingredients whose expiration dates are approaching, but is not limited to these examples. The reminder unit can remind the user about ingredients whose expiration dates are approaching. As a result, the AI ​​cooking assistant according to the embodiment can create custom recipes based on the user's health information, provide cooking guidance, suggest ingredient substitutes, manage inventory ingredients and expiration dates, and provide ingredient consumption reminders.

[0030] The acquisition unit can acquire the user's health information from a wearable device. Examples of wearable devices include, but are not limited to, smartwatches and fitness trackers. For example, the acquisition unit can acquire heart rate and blood pressure data from a smartwatch. The acquisition unit can also acquire weight and exercise data from a fitness tracker. Furthermore, the acquisition unit can analyze the data acquired from the wearable device in real time to understand the user's health condition. By acquiring health information from the wearable device, custom recipes can be created based on more accurate data. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the data acquired from the wearable device into a generation AI, which then analyzes the data.

[0031] The creation unit can create a custom recipe that takes nutritional balance into consideration based on the acquired health information. Nutritional balance includes, but is not limited to, vitamins, minerals, and calories. The creation unit can create a custom recipe that takes vitamin and mineral balance into consideration. The creation unit can also create a custom recipe that takes calorie restriction into consideration. Furthermore, the creation unit can adjust the level of detail of the custom recipe based on the user's health goals. For example, the creation unit can provide a recipe that takes calorie restriction into consideration for a user who is trying to lose weight. The creation unit can also provide a recipe that is high in protein for a user who is trying to build muscle. By creating a custom recipe that takes nutritional balance into consideration, meals suitable for the user's health condition can be provided. Some or all of the above-described processing in the creation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the creation unit can input the acquired health information into the generation AI, and the generation AI can create the custom recipe.

[0032] The instructor can provide advice on cooking procedures, temperature control, and cooking time in real time. Examples of real-time include, but are not limited to, updates every second and live feedback. For example, the instructor can provide cooking procedures in real time. The instructor can also provide advice on temperature control in real time. The instructor can also provide advice on cooking time in real time. For example, the instructor can monitor in real time whether the temperature is appropriate during cooking and provide advice on adjusting the temperature as needed. The instructor can also monitor in real time whether the cooking time is appropriate and adjust the cooking time as needed. This allows the user to cook appropriately by providing advice on cooking procedures, temperature control, and cooking time in real time. Some or all of the above-described processing in the instructor can be performed using, for example, AI, or without AI. For example, the instructor can input data during cooking into a generation AI, which can then provide advice in real time.

[0033] The suggestion unit can suggest ingredient substitution suggestions based on allergy information and food culture. Allergy information includes, but is not limited to, food allergies and environmental allergies. Food culture includes, but is not limited to, ingredients and cooking methods unique to a region. The suggestion unit, for example, suggests ingredient substitution suggestions taking into account the user's allergy information. The suggestion unit can also suggest ingredient substitution suggestions taking into account the user's food culture. The suggestion unit can also suggest ingredient substitution suggestions taking into account the user's preferences. For example, the suggestion unit provides substitution suggestions that exclude ingredients to which the user is allergic. The suggestion unit can also provide substitution suggestions based on the user's food culture. In this way, suggesting ingredient substitution suggestions based on allergy information and food culture allows the user to use ingredients that are suitable for their health and preferences. Some or all of the above-mentioned processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input data on the user's allergy information and food culture into a generation AI, which then suggests ingredient substitution suggestions.

[0034] The management unit can manage inventory and expiration dates using barcode scanning or voice input. Examples of barcode scanning include, but are not limited to, a smartphone app or a dedicated scanner. Examples of voice input include, but are not limited to, voice recognition software or a microphone. The management unit can manage inventory and expiration dates using, for example, barcode scanning. The management unit can also manage expiration dates using voice input. Furthermore, the management unit can manage inventory and expiration dates by combining barcode scanning and voice input. For example, the management unit can scan barcodes of inventory ingredients using a smartphone app to manage inventory. The management unit can also manage expiration dates by voice input using voice recognition software. This improves the efficiency of managing inventory and expiration dates using barcode scanning and voice input. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or without AI. For example, the management unit can input data from barcode scanning or voice input into a generation AI, which can then manage inventory and expiration dates.

[0035] The reminding unit can remind the user about food ingredients whose expiration date is approaching. Examples of reminders include, but are not limited to, notification frequency and notification method (email, app notification, etc.). For example, the reminding unit notifies the user about food ingredients whose expiration date is approaching by email. The reminding unit can also remind the user about food ingredients whose expiration date is approaching using app notifications. Furthermore, the reminding unit can adjust the frequency of notifications. For example, the reminding unit notifies the user about food ingredients whose expiration date is approaching one week ago. The reminding unit can also notify the user again about food ingredients whose expiration date is approaching one day ago. In this way, by reminding the user about food ingredients whose expiration date is approaching, food waste can be reduced. Some or all of the above-described processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit can input expiration date data into a generation AI, which can determine the timing of the reminder.

[0036] The acquisition unit can analyze the user's past health data and select the optimal acquisition method. Examples of past health data include, but are not limited to, past diagnostic results and fitness tracker data. The acquisition unit can, for example, select the most effective data acquisition method from the user's past health data. The acquisition unit can also adjust the frequency of data acquisition based on the user's past health data. The acquisition unit can also analyze the user's past health data and acquire data at a specific time period. For example, the acquisition unit can select the optimal data acquisition method based on the user's past diagnostic results. The acquisition unit can also adjust the frequency of data acquisition based on fitness tracker data. In this way, the optimal data acquisition method can be selected by analyzing the past health data. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input past health data into a generation AI, have the generation AI analyze the data, and select the optimal acquisition method.

[0037] When acquiring health information, the acquisition unit can filter the health information based on the user's current living situation and activity level. Examples of living situations include, but are not limited to, daily activity levels and work schedules. For example, when the user is exercising, the acquisition unit can prioritize acquiring exercise data. Furthermore, when the user is resting, the acquisition unit can prioritize acquiring heart rate and sleep data. Furthermore, when the user is working, the acquisition unit can prioritize acquiring stress level and concentration data. For example, when the user is exercising, the acquisition unit can prioritize acquiring exercise data. Furthermore, when the user is resting, the acquisition unit can prioritize acquiring heart rate and sleep data. In this way, by filtering data according to the user's living situation and activity level, more relevant information can be acquired. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's living situation and activity level into a generation AI, which can then filter the data.

[0038] When acquiring health information, the acquisition unit can select the optimal acquisition means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the acquisition unit can acquire the health information using voice recognition technology. Furthermore, when the user uses text input, the acquisition unit can also acquire the health information using text analysis technology. Furthermore, when the user uses image input, the acquisition unit can acquire the health information using image analysis technology. For example, when the user uses voice input, the acquisition unit can acquire the health information using voice recognition technology. Furthermore, when the user uses text input, the acquisition unit can also acquire the health information using text analysis technology. This allows health information to be acquired efficiently by selecting the optimal acquisition means depending on the user's input method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can input the user's input data to a generation AI, which can select the optimal acquisition means.

[0039] When acquiring health information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, when the user is at a high altitude, the acquisition unit can prioritize acquiring health data related to altitude. Furthermore, when the user is in an urban area, the acquisition unit can prioritize acquiring health data related to air quality and noise levels. Furthermore, when the user is outdoors, the acquisition unit can prioritize acquiring health data related to ultraviolet light levels and temperature. For example, when the user is at a high altitude, the acquisition unit can prioritize acquiring health data related to altitude. Furthermore, when the user is in an urban area, the acquisition unit can prioritize acquiring health data related to air quality and noise levels. In this way, by taking the user's geographical location information into account, highly relevant health information can be prioritized. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to a generation AI, which can then prioritize acquiring highly relevant information.

[0040] When acquiring health information, the acquisition unit can analyze the user's social media activity and acquire related information. Social media activity includes, but is not limited to, post content, the number of likes, and comments. For example, if the user posts about exercise on social media, the acquisition unit can prioritize acquiring exercise data. Furthermore, if the user posts about diet on social media, the acquisition unit can prioritize acquiring diet data. Furthermore, if the user posts about stress on social media, the acquisition unit can prioritize acquiring stress level data. For example, if the user posts about exercise on social media, the acquisition unit can prioritize acquiring exercise data. Furthermore, if the user posts about diet on social media, the acquisition unit can prioritize acquiring diet data. In this way, health information related to the user can be acquired by analyzing social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data about the user's social media activity into a generation AI, which can acquire related information.

[0041] When acquiring health information, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, user reviews and survey results. For example, the acquisition unit can adjust the frequency of data acquisition based on feedback previously provided by the user. The acquisition unit can also change the data acquisition method based on feedback previously provided by the user. Furthermore, the acquisition unit can prioritize the acquisition of specific health data based on feedback previously provided by the user. For example, the acquisition unit can adjust the frequency of data acquisition based on feedback previously provided by the user. The acquisition unit can also change the data acquisition method based on feedback previously provided by the user. This allows the user to be provided with an optimal acquisition method by reflecting past feedback. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's feedback data into a generation AI and have the generation AI customize the acquisition method.

[0042] When creating a custom recipe, the creation unit can adjust the level of detail of the recipe based on the user's health goals. Health goals include, but are not limited to, weight loss and muscle building. For example, if the user is aiming to lose weight, the creation unit can provide recipes that take calorie restriction into consideration. Furthermore, if the user is aiming to build muscle, the creation unit can provide recipes that are high in protein. Furthermore, if the user is aiming to maintain health, the creation unit can provide recipes that include balanced nutrients. For example, if the user is aiming to lose weight, the creation unit can provide recipes that take calorie restriction into consideration. Furthermore, if the user is aiming to build muscle, the creation unit can provide recipes that are high in protein. By adjusting the level of detail of the recipe based on the user's health goals, it is possible to provide recipes that match the goals. Some or all of the above-described processing in the creation unit may be performed using, for example, a generation AI. For example, the creation unit can input the user's health goal data into the generation AI and have the generation AI adjust the level of detail of the recipe.

[0043] When creating a custom recipe, the creation unit can improve the accuracy of the recipe by referring to the user's meal history. Examples of meal history include, but are not limited to, a meal recording app or a handwritten food diary. For example, the creation unit can provide recipes that match the user's preferences based on dishes the user has made in the past. The creation unit can also provide recipes that take into account allergies or disliked ingredients based on the user's meal history. Furthermore, the creation unit can analyze the user's meal history and provide recipes that take nutritional balance into account. For example, the creation unit can provide recipes that match the user's preferences based on dishes the user has made in the past. The creation unit can also provide recipes that take into account allergies or disliked ingredients based on the user's meal history. By referring to the meal history, recipes that match the user's preferences can be provided. Some or all of the above-described processing in the creation unit may be performed using, or without, a generation AI. For example, the creation unit can input the user's meal history data into the generation AI, which can then improve the accuracy of the recipe.

[0044] When creating a custom recipe, the creation unit can customize the recipe by taking into account the user's allergy information. Allergy information includes, but is not limited to, food allergies and environmental allergies. For example, the creation unit can provide a recipe that excludes ingredients to which the user is allergic. The creation unit can also provide a recipe that uses alternative ingredients based on the user's allergy information. Furthermore, the creation unit can provide a recipe that does not cause an allergic reaction by taking into account the user's allergy information. For example, the creation unit can provide a recipe that excludes ingredients to which the user is allergic. The creation unit can also provide a recipe that uses alternative ingredients based on the user's allergy information. By taking into account the allergy information, an appropriate recipe can be provided while protecting the user's health. Some or all of the above-described processing in the creation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the creation unit can input the user's allergy information data into the generation AI and customize the recipe using the generation AI.

[0045] When creating a custom recipe, the creation unit can determine the priority of recipes based on the user's meal timings. Meal timings include, but are not limited to, breakfast, lunch, and dinner. For example, when a user is making breakfast, the creation unit can prioritize providing recipes suitable for breakfast. Furthermore, when a user is making lunch, the creation unit can prioritize providing recipes suitable for lunch. Furthermore, when a user is making dinner, the creation unit can prioritize providing recipes suitable for dinner. For example, when a user is making breakfast, the creation unit can prioritize providing recipes suitable for breakfast. Furthermore, when a user is making lunch, the creation unit can prioritize providing recipes suitable for lunch. Thus, by determining the priority of recipes based on meal timings, recipes can be provided at appropriate times. Some or all of the above-described processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the creation unit can input the user's meal timing data into the generation AI, and the generation AI can determine the priority of recipes.

[0046] When creating a custom recipe, the creation unit can adjust the order of recipes based on the user's meal associations. Meal associations include, but are not limited to, nutritional balance and ingredient combinations. For example, the creation unit can provide recipes related to dishes the user made the previous day. The creation unit can also provide recipes that reuse ingredients from dishes the user made in the past. Furthermore, the creation unit can adjust the order of recipes to provide a balanced meal based on the user's meal history. For example, the creation unit can provide recipes related to dishes the user made the previous day. The creation unit can also provide recipes that reuse ingredients from dishes the user made in the past. By adjusting the order of recipes based on the meal associations, a balanced meal can be provided. Some or all of the above-described processing in the creation unit may be performed using, or without, a generation AI. For example, the creation unit can input the user's meal history data into the generation AI and adjust the order of recipes using the generation AI.

[0047] When creating a custom recipe, the creation unit can adjust the use of technical terms in the recipe according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the user is a beginner, the creation unit can provide a recipe explained in simple terms. Furthermore, if the user is an intermediate user, the creation unit can provide a recipe containing slightly more technical terms. Furthermore, if the user is an advanced user, the creation unit can provide a detailed recipe using a lot of technical terms. For example, if the user is a beginner, the creation unit can provide a recipe explained in simple terms. Furthermore, if the user is an intermediate user, the creation unit can provide a recipe containing slightly more technical terms. In this way, by adjusting the technical terms in the recipe according to the user's level of expertise, it is possible to provide a recipe that is easy to understand. Some or all of the above-described processing in the creation unit may be performed using, or without, a generation AI. For example, the creation unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terms in the recipe.

[0048] When providing cooking instruction, the instruction unit can improve the accuracy of the instruction by referring to the user's past cooking history. Cooking history includes, for example, past cooking records, cooking app data, etc., but is not limited to these examples. For example, the instruction unit provides instruction tailored to the user's preferences based on dishes the user has made in the past. The instruction unit can also provide instruction that takes into account allergies and disliked ingredients based on the user's cooking history. Furthermore, the instruction unit can analyze the user's cooking history and provide instruction aimed at improving cooking skills. For example, the instruction unit provides instruction tailored to the user's preferences based on dishes the user has made in the past. The instruction unit can also provide instruction that takes into account allergies and disliked ingredients based on the user's cooking history. By referring to the past cooking history, instruction tailored to the user's preferences can be provided. Some or all of the above-described processing by the instruction unit may be performed, for example, using AI, or may be performed without AI. For example, the instruction unit can input the user's cooking history data into a generation AI, which can then improve the accuracy of the instruction.

[0049] When providing cooking instruction, the instruction unit can customize the instruction content based on the user's current cooking environment. The cooking environment includes, but is not limited to, kitchen equipment and cooking utensils used. For example, the instruction unit provides instruction tailored to the user's cooking utensils. The instruction unit can also provide instruction on efficient cooking methods based on the size and layout of the user's kitchen. Furthermore, the instruction unit can provide instruction on appropriate cooking temperatures and times according to the user's cooking environment. For example, the instruction unit provides instruction tailored to the user's cooking utensils. The instruction unit can also provide instruction on efficient cooking methods based on the size and layout of the user's kitchen. This allows efficient cooking to be supported by customizing the instruction content based on the current cooking environment. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without AI. For example, the instruction unit can input the user's cooking environment data into a generation AI and have the generation AI customize the instruction content.

[0050] When providing cooking instruction, the instruction unit can improve the instruction method by reflecting user feedback. Examples of feedback include, but are not limited to, user reviews and survey results. For example, the instruction unit can adjust the instruction method based on feedback previously provided by the user. The instruction unit can also improve specific cooking procedures by reflecting user feedback. Furthermore, the instruction unit can customize the instruction content based on user feedback. For example, the instruction unit can adjust the instruction method based on feedback previously provided by the user. The instruction unit can also improve specific cooking procedures by reflecting user feedback. In this way, by reflecting feedback, the optimal instruction method can be provided to the user. Some or all of the above-described processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input user feedback data into a generation AI, which can then improve the instruction method.

[0051] When providing cooking instruction, the instruction unit can select the optimal instruction method by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, if the user is at high altitude, the instruction unit can provide instruction on a cooking method appropriate for the altitude. Furthermore, if the user is in an urban area, the instruction unit can provide instruction on a cooking method using ingredients that are easily available in the urban area. Furthermore, if the user is cooking outdoors, the instruction unit can provide instruction on a cooking method appropriate for outdoor use. For example, if the user is at high altitude, the instruction unit can provide instruction on a cooking method appropriate for the altitude. Furthermore, if the user is in an urban area, the instruction unit can provide instruction on a cooking method using ingredients that are easily available in the urban area. In this way, by taking geographical location information into consideration, an instruction method appropriate for the user can be provided. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input the user's geographical location information data into a generation AI, which can select the optimal instruction method.

[0052] When providing cooking instruction, the instruction unit can analyze the user's social media activity to suggest instructional content. Social media activity includes, but is not limited to, post content, the number of likes, and comments. For example, the instruction unit can suggest instructional content based on the user's shared social media posts. The instruction unit can also analyze the user's social media posts to suggest related cooking methods. Furthermore, the instruction unit can suggest related cooking methods based on the user's social media friends' activities. For example, the instruction unit can suggest instructional content based on the user's shared social media posts. The instruction unit can also analyze the user's social media posts to suggest related cooking methods. In this way, by analyzing social media activity, relevant instructional content can be provided to the user. Some or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the user's social media activity data into a generation AI, which then suggests instructional content.

[0053] When providing cooking instruction, the instruction unit can customize the instruction method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, user reviews and survey results. For example, the instruction unit can adjust the instruction method based on feedback previously provided by the user. The instruction unit can also improve specific cooking procedures by reflecting the user's feedback. Furthermore, the instruction unit can customize the instruction content based on the user's feedback. For example, the instruction unit can adjust the instruction method based on feedback previously provided by the user. The instruction unit can also improve specific cooking procedures by reflecting the user's feedback. In this way, by reflecting the past feedback, the optimal instruction method can be provided to the user. Some or all of the above-described processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the user's feedback data into a generation AI and use the generation AI to customize the instruction method.

[0054] When proposing ingredient alternatives, the suggestion unit can customize the suggestions by taking into account the user's allergy information. Allergy information includes, but is not limited to, food allergies and environmental allergies. For example, the suggestion unit can provide alternatives that exclude ingredients to which the user is allergic. The suggestion unit can also make suggestions using alternative ingredients based on the user's allergy information. Furthermore, the suggestion unit can provide alternatives that do not cause allergic reactions by taking into account the user's allergy information. For example, the suggestion unit can provide alternatives that exclude ingredients to which the user is allergic. The suggestion unit can also make suggestions using alternative ingredients based on the user's allergy information. By taking into account the allergy information, appropriate alternatives can be provided while protecting the user's health. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's allergy information data into a generation AI, which can then customize the suggestions.

[0055] When proposing ingredient alternatives, the suggestion unit can adjust the content of the suggestions by taking into account the user's food culture and preferences. Food culture includes, for example, regional ingredients and cooking methods, but is not limited to such examples. The suggestion unit, for example, provides alternatives based on the user's food culture. The suggestion unit can also provide alternatives that match the user's preferences by taking into account the user's tastes. Furthermore, the suggestion unit can provide alternatives that match the user's preferences based on the user's past eating history. For example, the suggestion unit provides alternatives based on the user's food culture. The suggestion unit can also provide alternatives that match the user's preferences by taking into account the user's tastes. In this way, alternatives that match the user can be provided by taking into account the food culture and preferences. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's food culture and preference data into a generation AI, and the generation AI can adjust the content of the suggestions.

[0056] When proposing ingredient alternatives, the suggestion unit can improve the accuracy of the suggestions by referring to the user's past suggestion history. The suggestion history includes, for example, past suggestion records, suggestion app data, etc., but is not limited to these examples. For example, the suggestion unit adjusts the suggestion content based on alternatives previously accepted by the user. The suggestion unit can also analyze the user's past suggestion history to provide optimal alternatives. Furthermore, the suggestion unit can provide alternatives that match the user's preferences based on the user's past suggestion history. For example, the suggestion unit adjusts the suggestion content based on alternatives previously accepted by the user. The suggestion unit can also analyze the user's past suggestion history to provide optimal alternatives. By referring to the past suggestion history, optimal alternatives can be provided to the user. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's suggestion history data into a generation AI, which can improve the accuracy of the suggestions.

[0057] When proposing ingredient alternatives, the suggestion unit can propose optimal alternatives by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in an urban area, the suggestion unit can provide alternatives that are easily available in the urban area. Furthermore, if the user is in a rural area, the suggestion unit can provide alternatives that are easily available in the rural area. Furthermore, if the user is in a specific region, the suggestion unit can provide alternatives that are easily available in the region. For example, if the user is in an urban area, the suggestion unit can provide alternatives that are easily available in the urban area. Furthermore, if the user is in a rural area, the suggestion unit can provide alternatives that are easily available in the rural area. In this way, by taking geographical location information into consideration, alternatives suitable for the user can be provided. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information data into a generation AI, which can then propose optimal alternatives.

[0058] When proposing ingredient alternatives, the suggestion unit can analyze the user's social media activity to suggest relevant alternatives. Social media activity includes, but is not limited to, post content, the number of likes, and comments. For example, the suggestion unit can suggest alternatives based on dishes shared by the user on social media. The suggestion unit can also analyze the user's social media posts to suggest relevant alternatives. Furthermore, the suggestion unit can suggest relevant alternatives based on the activities of the user's friends on social media. For example, the suggestion unit can suggest alternatives based on dishes shared by the user on social media. The suggestion unit can also analyze the user's social media posts to suggest relevant alternatives. In this way, by analyzing social media activity, relevant alternatives can be provided to the user. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's social media activity data into a generation AI, which then suggests relevant alternatives.

[0059] When proposing ingredient alternatives, the suggestion unit can customize the suggestions by reflecting the user's past feedback. Examples of feedback include, but are not limited to, user reviews and survey results. For example, the suggestion unit can adjust the suggestions based on feedback previously provided by the user. The suggestion unit can also improve specific alternatives by reflecting user feedback. Furthermore, the suggestion unit can customize the suggestions based on user feedback. For example, the suggestion unit can adjust the suggestions based on feedback previously provided by the user. The suggestion unit can also improve specific alternatives by reflecting user feedback. In this way, optimal alternatives can be provided to the user by reflecting past feedback. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI or without AI. For example, the suggestion unit can input user feedback data into a generation AI and have the generation AI customize the suggestions.

[0060] The management unit can improve the accuracy of management of inventory ingredients and expiration dates by referring to the user's past consumption history. Consumption history includes, for example, past consumption records and consumption app data, but is not limited to these examples. For example, the management unit can propose an optimal management method based on the user's past consumption history. The management unit can also analyze the user's past consumption history to optimize expiration date management. Furthermore, the management unit can adjust the inventory ingredient management method based on the user's past consumption history. For example, the management unit can propose an optimal management method based on the user's past consumption history. The management unit can also analyze the user's past consumption history to optimize expiration date management. By referring to the past consumption history, the management accuracy of inventory ingredients and expiration dates can be improved. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without AI. For example, the management unit can input the user's consumption history data into a generation AI, which can improve the management accuracy.

[0061] The management unit can customize the management content based on the user's current lifestyle when managing inventory and expiration dates. Examples of lifestyle include, but are not limited to, daily activity levels and work schedules. For example, the management unit can provide a simple management method when the user is busy. Furthermore, the management unit can also provide a detailed management method when the user is relaxed. Furthermore, the management unit can adjust the management method of inventory and expiration dates according to the user's lifestyle. For example, the management unit can provide a simple management method when the user is busy. Furthermore, the management unit can also provide a detailed management method when the user is relaxed. By customizing the management content based on the user's current lifestyle, the optimal management method can be provided to the user. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without AI. For example, the management unit can input the user's lifestyle data into a generation AI, which can customize the management content.

[0062] The management unit can improve the management method by reflecting user feedback when managing inventory ingredients and expiration dates. Examples of feedback include, but are not limited to, user reviews and survey results. For example, the management unit can adjust the management method based on feedback previously provided by the user. The management unit can also improve specific management procedures by reflecting user feedback. Furthermore, the management unit can customize the management content based on user feedback. For example, the management unit can adjust the management method based on feedback previously provided by the user. The management unit can also improve specific management procedures by reflecting user feedback. By reflecting the feedback, the optimal management method can be provided to the user. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input user feedback data into a generation AI, which can then improve the management method.

[0063] The management unit can select an optimal management method by taking into account the user's geographical location information when managing inventory ingredients and expiration dates. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in an urban area, the management unit can prioritize managing ingredients that are easily available in the urban area. Furthermore, if the user is in a rural area, the management unit can prioritize managing ingredients that are easily available in the rural area. Furthermore, if the user is in a specific region, the management unit can prioritize managing ingredients that are easily available in that region. For example, if the user is in an urban area, the management unit can prioritize managing ingredients that are easily available in the urban area. Furthermore, if the user is in a rural area, the management unit can prioritize managing ingredients that are easily available in the rural area. This allows a management method suited to the user to be provided by taking into account the geographical location information. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's geographical location information data into a generation AI, which can select an optimal management method.

[0064] The management unit can analyze a user's social media activity to suggest management content when managing inventory and expiration dates of ingredients. Social media activity includes, but is not limited to, post content, number of likes, and comments. For example, the management unit manages inventory based on recipes shared by the user on social media. The management unit can also analyze the user's social media posts to manage expiration dates of related ingredients. Furthermore, the management unit can suggest management methods for related ingredients based on the activity of the user's friends on social media. For example, the management unit manages inventory based on recipes shared by the user on social media. The management unit can also analyze the user's social media posts to manage expiration dates of related ingredients. In this way, by analyzing social media activity, relevant management content can be provided to the user. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's social media activity data into a generation AI, which then suggests management content.

[0065] The management unit can customize the management method by reflecting the user's past feedback when managing inventory ingredients and expiration dates. Examples of feedback include, but are not limited to, user reviews and survey results. For example, the management unit can adjust the management method based on feedback previously provided by the user. The management unit can also improve specific management procedures by reflecting user feedback. Furthermore, the management unit can customize the management content based on user feedback. For example, the management unit can adjust the management method based on feedback previously provided by the user. The management unit can also improve specific management procedures by reflecting user feedback. In this way, by reflecting past feedback, the optimal management method can be provided to the user. Some or all of the above-described processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can input user feedback data into a generation AI and have the generation AI customize the management method.

[0066] The reminding unit can improve the accuracy of reminders by referring to the user's past reminder history. Reminder history includes, but is not limited to, past reminder records and reminder app data. For example, the reminding unit can suggest an optimal reminder method based on the user's past reminder history. The reminding unit can also analyze the user's past reminder history and adjust the timing of reminders. Furthermore, the reminding unit can customize the content of reminders based on the user's past reminder history. For example, the reminding unit can suggest an optimal reminder method based on the user's past reminder history. The reminding unit can also analyze the user's past reminder history and adjust the timing of reminders. By referring to the past reminder history, the accuracy of reminders is improved. Some or all of the above-described processing in the reminding unit may be performed using, for example, AI, or may be performed without AI. For example, the reminding unit can input the user's reminder history data into a generation AI, which can improve the accuracy of reminders.

[0067] The reminder unit can customize the reminder content based on the user's current living situation when providing a reminder. Examples of living situations include, but are not limited to, daily activity levels and work schedules. For example, if the user is busy, the reminder unit can provide an easily understandable reminder. Furthermore, if the user is relaxed, the reminder unit can provide a detailed reminder. Furthermore, the reminder unit can adjust the reminder content according to the user's living situation. For example, if the user is busy, the reminder unit can provide an easily understandable reminder. Furthermore, if the user is relaxed, the reminder unit can provide a detailed reminder. By customizing the reminder content based on the user's current living situation, the user can be provided with an optimal reminder. Some or all of the above-described processing in the reminder unit may be performed using, for example, AI, or may be performed without AI. For example, the reminder unit can input the user's living situation data into a generation AI, which can customize the reminder content.

[0068] The reminding unit can improve the reminding method by reflecting user feedback when reminding. Examples of feedback include, but are not limited to, user reviews and survey results. The reminding unit can adjust the reminding method based on, for example, feedback previously provided by the user. The reminding unit can also improve specific reminding procedures by reflecting user feedback. Furthermore, the reminding unit can customize the reminding content based on user feedback. For example, the reminding unit can adjust the reminding method based on feedback previously provided by the user. The reminding unit can also improve specific reminding procedures by reflecting user feedback. By reflecting past feedback, the optimal reminding method can be provided to the user. Some or all of the above-described processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit can input user feedback data into a generation AI, which can then improve the reminding method.

[0069] The reminding unit can select an optimal reminding method by taking into account the user's geographical location information when reminding. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, if the user is in an urban area, the reminding unit can provide a reminding method for the urban area. Furthermore, if the user is in a rural area, the reminding unit can provide a reminding method for the rural area. Furthermore, if the user is in a specific region, the reminding unit can provide a reminding method suitable for that region. For example, if the user is in an urban area, the reminding unit can provide a reminding method for the urban area. Furthermore, if the user is in a rural area, the reminding unit can provide a reminding method suitable for the rural area. In this way, a reminding method suitable for the user can be provided by taking into account the geographical location information. Some or all of the above-described processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit can input the user's geographical location information data into a generation AI, which can select an optimal reminding method.

[0070] The reminder unit may analyze the user's social media activity to suggest reminder content when providing a reminder. Social media activity includes, but is not limited to, post content, the number of likes, and comments. For example, the reminder unit may provide reminders based on content shared by the user on social media. The reminder unit may also analyze the user's social media posts and provide related reminders. Furthermore, the reminder unit may provide related reminders based on the activities of the user's friends on social media. For example, the reminder unit may provide reminders based on content shared by the user on social media. The reminder unit may also analyze the user's social media posts and provide related reminders. In this way, by analyzing social media activity, relevant reminder content can be provided to the user. Some or all of the above-described processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit may input the user's social media activity data into a generation AI, which may then suggest reminder content.

[0071] The reminding unit can customize the reminding method by reflecting the user's past feedback when reminding. Examples of feedback include, but are not limited to, user reviews and survey results. The reminding unit can adjust the reminding method based on, for example, feedback provided by the user in the past. The reminding unit can also improve specific reminding procedures by reflecting the user's feedback. Furthermore, the reminding unit can customize the reminding content based on the user's feedback. For example, the reminding unit can adjust the reminding method based on the user's past feedback. The reminding unit can also improve specific reminding procedures by reflecting the user's feedback. In this way, by reflecting the past feedback, the optimal reminding method can be provided to the user. Some or all of the above-described processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit can input the user's feedback data into a generation AI and use the generation AI to customize the reminding method.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] When acquiring the user's health information, the acquisition unit can analyze the user's past health data and select the optimal acquisition method. For example, the acquisition unit can select the most effective data acquisition method based on the user's past diagnosis results and fitness tracker data. The acquisition unit can also adjust the frequency of data acquisition based on the user's past health data. Furthermore, the acquisition unit can analyze the user's past health data and acquire data at specific time periods. This allows the optimal data acquisition method to be selected by analyzing the past health data.

[0074] When creating a custom recipe, the creation unit can improve the accuracy of the recipe by referring to the user's dietary history. For example, the creation unit can provide recipes that match the user's preferences based on dishes the user has made in the past. The creation unit can also provide recipes that take into account allergies and disliked ingredients based on the user's dietary history. Furthermore, the creation unit can analyze the user's dietary history and provide recipes that take nutritional balance into consideration. In this way, by referring to the dietary history, recipes that match the user's preferences can be provided.

[0075] When providing cooking instruction, the instruction unit can improve the accuracy of the instruction by referring to the user's past cooking history. For example, the instruction can be tailored to the user's preferences based on dishes the user has made in the past. The instruction unit can also provide instruction that takes into account allergies and disliked ingredients based on the user's cooking history. Furthermore, the instruction unit can analyze the user's cooking history and provide instruction aimed at improving cooking skills. This allows the instruction to be tailored to the user's preferences by referring to the user's past cooking history.

[0076] When proposing ingredient substitution suggestions, the suggestion unit can customize the suggestions by taking into account the user's allergy information. For example, the suggestion unit can provide alternative suggestions that exclude ingredients to which the user has allergies. The suggestion unit can also make suggestions using alternative ingredients based on the user's allergy information. Furthermore, the suggestion unit can take into account the user's allergy information and provide alternative suggestions that do not cause allergic reactions. In this way, by taking into account the allergy information, appropriate alternative suggestions can be provided while protecting the user's health.

[0077] The management unit can improve the accuracy of management when managing inventory ingredients and expiration dates by referring to the user's past consumption history. For example, the management unit can propose an optimal management method based on the user's past consumption history. The management unit can also analyze the user's past consumption history and optimize expiration date management. Furthermore, the management unit can adjust the inventory ingredient management method based on the user's past consumption history. In this way, by referring to the past consumption history, the accuracy of management of inventory ingredients and expiration dates can be improved.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The acquisition unit acquires the user's health information. The health information includes, but is not limited to, for example, heart rate, blood pressure, and weight. The acquisition unit acquires the health information from, for example, a wearable device. The acquisition unit can also select the optimal acquisition means depending on the user's input method (voice, text, image, etc.). Step 2: The creation unit creates a custom recipe based on the acquired health information. The custom recipe is created taking into consideration, for example, nutritional balance, calorie restriction, etc., but is not limited to these examples. The creation unit uses a generation AI to create a custom recipe based on the user's health information. Step 3: The instruction unit provides cooking instruction based on the custom recipe created by the creation unit. The cooking instruction may include, but is not limited to, cooking procedures, temperature control, cooking time, etc. The instruction unit may provide advice on cooking procedures, temperature control, and cooking time in real time. Step 4: The suggestion unit suggests alternative ingredients based on the cooking instructions provided by the instruction unit. The alternative ingredients are suggested taking into consideration, for example, allergy information, food culture, etc., but are not limited to these examples. The suggestion unit can suggest alternative ingredients based on allergy information and food culture. Step 5: The management unit manages the inventory of ingredients and expiration dates based on the alternatives proposed by the proposal unit. For example, barcode scanning, voice input, etc. are used to manage the inventory of ingredients and expiration dates, but are not limited to these examples. The management unit can manage the inventory of ingredients and expiration dates using barcode scanning or voice input. Step 6: The reminding unit performs a food ingredient consumption reminder based on the inventory of food ingredients and expiration dates managed by the management unit. The reminder may, for example, notify the user about food ingredients whose expiration dates are approaching, but is not limited to such an example. The reminding unit may remind the user about food ingredients whose expiration dates are approaching.

[0080] (Example 2) An AI cooking assistant according to an embodiment of the present invention is a system that acquires a user's health information, creates custom recipes, provides cooking guidance, suggests ingredient substitutions, manages inventory and expiration dates, and reminds users when ingredients are consumed. The AI ​​cooking assistant acquires the user's health information and creates custom recipes based on that information. It then provides cooking guidance based on the custom recipes and suggests ingredient substitutions as needed. It also manages inventory and expiration dates and sends ingredient consumption reminders at appropriate times. For example, the AI ​​cooking assistant acquires the user's health information from a wearable device. It then creates custom recipes that take nutritional balance into consideration based on the acquired health information. It also provides real-time advice on cooking procedures, temperature control, and cooking times. It suggests ingredient substitutions based on allergy information and food culture. It manages inventory and expiration dates using barcode scanning and voice input, and sends reminders when ingredients are approaching their expiration dates. This allows the AI ​​cooking assistant to improve the user's cooking experience. The AI ​​cooking assistant can create custom recipes based on the user's health information, provide cooking guidance, suggest ingredient substitutions, manage inventory and expiration dates, and send ingredient consumption reminders. For example, it can track the stock and expiration dates of ingredients the user owns and remind them to consume them at the appropriate time. It can also suggest recipes that take into account the user's health condition and nutritional balance. This allows users to be provided with meals that are appropriate for their health condition, and if they have any trouble cooking, the AI ​​can provide immediate advice.

[0081] The AI ​​cooking assistant according to the embodiment includes an acquisition unit, a creation unit, an instruction unit, a suggestion unit, a management unit, and a reminder unit. The acquisition unit acquires a user's health information. The health information includes, but is not limited to, heart rate, blood pressure, and weight. The acquisition unit acquires the health information from, for example, a wearable device. The acquisition unit can also select the optimal acquisition method based on the user's input method (voice, text, image, etc.). The creation unit creates a custom recipe based on the acquired health information. The custom recipe is created taking into consideration, for example, nutritional balance and calorie restriction, but is not limited to, the example. The creation unit uses a generation AI to create a custom recipe based on the user's health information. The instruction unit provides cooking guidance based on the custom recipe created by the creation unit. The cooking guidance includes, for example, cooking procedures, temperature control, cooking time, etc., but is not limited to, the example. The instruction unit can provide advice on cooking procedures, temperature control, and cooking time in real time. The suggestion unit suggests alternative ingredients based on the cooking guidance provided by the instruction unit. The ingredient substitutes are proposed taking into consideration, for example, allergy information, food culture, etc., but are not limited to these examples. The suggestion unit can propose ingredient substitutes based on allergy information and food culture. The management unit manages inventory ingredients and expiration dates based on the alternatives proposed by the suggestion unit. The inventory ingredients and expiration dates can be managed using, for example, barcode scanning, voice input, etc., but are not limited to these examples. The management unit can manage inventory ingredients and expiration dates using barcode scanning or voice input. The reminder unit provides ingredient consumption reminders based on the inventory ingredients and expiration dates managed by the management unit. The reminder can, for example, notify the user about ingredients whose expiration dates are approaching, but is not limited to these examples. The reminder unit can remind the user about ingredients whose expiration dates are approaching. As a result, the AI ​​cooking assistant according to the embodiment can create custom recipes based on the user's health information, provide cooking guidance, suggest ingredient substitutes, manage inventory ingredients and expiration dates, and provide ingredient consumption reminders.

[0082] The acquisition unit can acquire the user's health information from a wearable device. Examples of wearable devices include, but are not limited to, smartwatches and fitness trackers. For example, the acquisition unit can acquire heart rate and blood pressure data from a smartwatch. The acquisition unit can also acquire weight and exercise data from a fitness tracker. Furthermore, the acquisition unit can analyze the data acquired from the wearable device in real time to understand the user's health condition. By acquiring health information from the wearable device, custom recipes can be created based on more accurate data. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the data acquired from the wearable device into a generation AI, which then analyzes the data.

[0083] The creation unit can create a custom recipe that takes nutritional balance into consideration based on the acquired health information. Nutritional balance includes, but is not limited to, vitamins, minerals, and calories. The creation unit can create a custom recipe that takes vitamin and mineral balance into consideration. The creation unit can also create a custom recipe that takes calorie restriction into consideration. Furthermore, the creation unit can adjust the level of detail of the custom recipe based on the user's health goals. For example, the creation unit can provide a recipe that takes calorie restriction into consideration for a user who is trying to lose weight. The creation unit can also provide a recipe that is high in protein for a user who is trying to build muscle. By creating a custom recipe that takes nutritional balance into consideration, meals suitable for the user's health condition can be provided. Some or all of the above-described processing in the creation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the creation unit can input the acquired health information into the generation AI, and the generation AI can create the custom recipe.

[0084] The instructor can provide advice on cooking procedures, temperature control, and cooking time in real time. Examples of real-time include, but are not limited to, updates every second and live feedback. For example, the instructor can provide cooking procedures in real time. The instructor can also provide advice on temperature control in real time. The instructor can also provide advice on cooking time in real time. For example, the instructor can monitor in real time whether the temperature is appropriate during cooking and provide advice on adjusting the temperature as needed. The instructor can also monitor in real time whether the cooking time is appropriate and adjust the cooking time as needed. This allows the user to cook appropriately by providing advice on cooking procedures, temperature control, and cooking time in real time. Some or all of the above-described processing in the instructor can be performed using, for example, AI, or without AI. For example, the instructor can input data during cooking into a generation AI, which can then provide advice in real time.

[0085] The suggestion unit can suggest ingredient substitution suggestions based on allergy information and food culture. Allergy information includes, but is not limited to, food allergies and environmental allergies. Food culture includes, but is not limited to, ingredients and cooking methods unique to a region. The suggestion unit, for example, suggests ingredient substitution suggestions taking into account the user's allergy information. The suggestion unit can also suggest ingredient substitution suggestions taking into account the user's food culture. The suggestion unit can also suggest ingredient substitution suggestions taking into account the user's preferences. For example, the suggestion unit provides substitution suggestions that exclude ingredients to which the user is allergic. The suggestion unit can also provide substitution suggestions based on the user's food culture. In this way, suggesting ingredient substitution suggestions based on allergy information and food culture allows the user to use ingredients that are suitable for their health and preferences. Some or all of the above-mentioned processing by the suggestion unit may be performed using, for example, AI, or may be performed without AI. For example, the suggestion unit can input data on the user's allergy information and food culture into a generation AI, which then suggests ingredient substitution suggestions.

[0086] The management unit can manage inventory and expiration dates using barcode scanning or voice input. Examples of barcode scanning include, but are not limited to, a smartphone app or a dedicated scanner. Examples of voice input include, but are not limited to, voice recognition software or a microphone. The management unit can manage inventory and expiration dates using, for example, barcode scanning. The management unit can also manage expiration dates using voice input. Furthermore, the management unit can manage inventory and expiration dates by combining barcode scanning and voice input. For example, the management unit can scan barcodes of inventory ingredients using a smartphone app to manage inventory. The management unit can also manage expiration dates by voice input using voice recognition software. This improves the efficiency of managing inventory and expiration dates using barcode scanning and voice input. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or without AI. For example, the management unit can input data from barcode scanning or voice input into a generation AI, which can then manage inventory and expiration dates.

[0087] The reminding unit can remind the user about food ingredients whose expiration date is approaching. Examples of reminders include, but are not limited to, notification frequency and notification method (email, app notification, etc.). For example, the reminding unit notifies the user about food ingredients whose expiration date is approaching by email. The reminding unit can also remind the user about food ingredients whose expiration date is approaching using app notifications. Furthermore, the reminding unit can adjust the frequency of notifications. For example, the reminding unit notifies the user about food ingredients whose expiration date is approaching one week ago. The reminding unit can also notify the user again about food ingredients whose expiration date is approaching one day ago. In this way, by reminding the user about food ingredients whose expiration date is approaching, food waste can be reduced. Some or all of the above-described processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit can input expiration date data into a generation AI, which can determine the timing of the reminder.

[0088] The acquisition unit can estimate the user's emotions and adjust the timing of health information acquisition based on the estimated user emotions. For example, if the user is feeling stressed, the acquisition unit can acquire health information during a time when the user is able to relax. Furthermore, if the user is relaxed, the acquisition unit can collect data for a long period of time to acquire detailed health information. Furthermore, if the user is in a hurry, the acquisition unit can acquire necessary health information in a short period of time. This allows data acquisition at a more appropriate time by adjusting the timing of health information acquisition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the acquisition unit can input the user's emotion data into the generation AI, have the generation AI estimate the emotion, and adjust the acquisition timing.

[0089] The acquisition unit can analyze the user's past health data and select the optimal acquisition method. Examples of past health data include, but are not limited to, past diagnostic results and fitness tracker data. The acquisition unit can, for example, select the most effective data acquisition method from the user's past health data. The acquisition unit can also adjust the frequency of data acquisition based on the user's past health data. The acquisition unit can also analyze the user's past health data and acquire data at a specific time period. For example, the acquisition unit can select the optimal data acquisition method based on the user's past diagnostic results. The acquisition unit can also adjust the frequency of data acquisition based on fitness tracker data. In this way, the optimal data acquisition method can be selected by analyzing the past health data. Some or all of the above-described processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input past health data into a generation AI, have the generation AI analyze the data, and select the optimal acquisition method.

[0090] When acquiring health information, the acquisition unit can filter the health information based on the user's current living situation and activity level. Examples of living situations include, but are not limited to, daily activity levels and work schedules. For example, when the user is exercising, the acquisition unit can prioritize acquiring exercise data. Furthermore, when the user is resting, the acquisition unit can prioritize acquiring heart rate and sleep data. Furthermore, when the user is working, the acquisition unit can prioritize acquiring stress level and concentration data. For example, when the user is exercising, the acquisition unit can prioritize acquiring exercise data. Furthermore, when the user is resting, the acquisition unit can prioritize acquiring heart rate and sleep data. In this way, by filtering data according to the user's living situation and activity level, more relevant information can be acquired. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data on the user's living situation and activity level into a generation AI, which can then filter the data.

[0091] When acquiring health information, the acquisition unit can select the optimal acquisition means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, when the user uses voice input, the acquisition unit can acquire the health information using voice recognition technology. Furthermore, when the user uses text input, the acquisition unit can also acquire the health information using text analysis technology. Furthermore, when the user uses image input, the acquisition unit can acquire the health information using image analysis technology. For example, when the user uses voice input, the acquisition unit can acquire the health information using voice recognition technology. Furthermore, when the user uses text input, the acquisition unit can also acquire the health information using text analysis technology. This allows health information to be acquired efficiently by selecting the optimal acquisition means depending on the user's input method. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit can input the user's input data to a generation AI, which can select the optimal acquisition means.

[0092] The acquisition unit can estimate the user's emotions and determine the priority of health information to be acquired based on the estimated user's emotions. Priorities include, but are not limited to, importance and urgency. For example, when the user is stressed, the acquisition unit prioritizes acquiring stress level data. Furthermore, when the user is relaxed, the acquisition unit can also acquire overall health data in a balanced manner. Furthermore, when the user is in a hurry, the acquisition unit can prioritize acquiring the most important health data. For example, when the user is stressed, the acquisition unit prioritizes acquiring stress level data. Furthermore, when the user is relaxed, the acquisition unit can also acquire overall health data in a balanced manner. Thus, by determining the priority of health information according to the user's emotions, important information can be acquired preferentially. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's emotional data into the generation AI, have the generation AI estimate the emotion, and determine the priority of the health information to be acquired.

[0093] When acquiring health information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, when the user is at a high altitude, the acquisition unit can prioritize acquiring health data related to altitude. Furthermore, when the user is in an urban area, the acquisition unit can prioritize acquiring health data related to air quality and noise levels. Furthermore, when the user is outdoors, the acquisition unit can prioritize acquiring health data related to ultraviolet light levels and temperature. For example, when the user is at a high altitude, the acquisition unit can prioritize acquiring health data related to altitude. Furthermore, when the user is in an urban area, the acquisition unit can prioritize acquiring health data related to air quality and noise levels. In this way, by taking the user's geographical location information into account, highly relevant health information can be prioritized. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's geographical location information to a generation AI, which can then prioritize acquiring highly relevant information.

[0094] When acquiring health information, the acquisition unit can analyze the user's social media activity and acquire related information. Social media activity includes, but is not limited to, post content, the number of likes, and comments. For example, if the user posts about exercise on social media, the acquisition unit can prioritize acquiring exercise data. Furthermore, if the user posts about diet on social media, the acquisition unit can prioritize acquiring diet data. Furthermore, if the user posts about stress on social media, the acquisition unit can prioritize acquiring stress level data. For example, if the user posts about exercise on social media, the acquisition unit can prioritize acquiring exercise data. Furthermore, if the user posts about diet on social media, the acquisition unit can prioritize acquiring diet data. In this way, health information related to the user can be acquired by analyzing social media activity. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input data about the user's social media activity into a generation AI, which can acquire related information.

[0095] When acquiring health information, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, user reviews and survey results. For example, the acquisition unit can adjust the frequency of data acquisition based on feedback previously provided by the user. The acquisition unit can also change the data acquisition method based on feedback previously provided by the user. Furthermore, the acquisition unit can prioritize the acquisition of specific health data based on feedback previously provided by the user. For example, the acquisition unit can adjust the frequency of data acquisition based on feedback previously provided by the user. The acquisition unit can also change the data acquisition method based on feedback previously provided by the user. This allows the user to be provided with an optimal acquisition method by reflecting past feedback. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the user's feedback data into a generation AI and have the generation AI customize the acquisition method.

[0096] The creation unit can estimate the user's emotions and adjust the presentation of the custom recipe based on the estimated user's emotions. Examples of presentation methods include, but are not limited to, the length of text and the use of images. For example, if the user is feeling stressed, the creation unit can provide a simple and easy-to-understand recipe. Furthermore, if the user is feeling relaxed, the creation unit can provide a recipe with detailed instructions and tips. Furthermore, if the user is in a hurry, the creation unit can provide a recipe that can be made in a short time. For example, if the user is feeling stressed, the creation unit can provide a simple and easy-to-understand recipe. Furthermore, if the user is feeling relaxed, the creation unit can provide a recipe with detailed instructions and tips. This allows for adjusting the presentation of the custom recipe according to the user's emotions to provide a more appropriate recipe. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the creation unit may be performed using, for example, AI, or without AI. For example, the creation unit can input the user's emotional data into the generation AI, which can then estimate the emotion and adjust the way the custom recipe is expressed.

[0097] When creating a custom recipe, the creation unit can adjust the level of detail of the recipe based on the user's health goals. Health goals include, but are not limited to, weight loss and muscle building. For example, if the user is aiming to lose weight, the creation unit can provide recipes that take calorie restriction into consideration. Furthermore, if the user is aiming to build muscle, the creation unit can provide recipes that are high in protein. Furthermore, if the user is aiming to maintain health, the creation unit can provide recipes that include balanced nutrients. For example, if the user is aiming to lose weight, the creation unit can provide recipes that take calorie restriction into consideration. Furthermore, if the user is aiming to build muscle, the creation unit can provide recipes that are high in protein. By adjusting the level of detail of the recipe based on the user's health goals, it is possible to provide recipes that match the goals. Some or all of the above-described processing in the creation unit may be performed using, for example, a generation AI. For example, the creation unit can input the user's health goal data into the generation AI and have the generation AI adjust the level of detail of the recipe.

[0098] When creating a custom recipe, the creation unit can improve the accuracy of the recipe by referring to the user's meal history. Examples of meal history include, but are not limited to, a meal recording app or a handwritten food diary. For example, the creation unit can provide recipes that match the user's preferences based on dishes the user has made in the past. The creation unit can also provide recipes that take into account allergies or disliked ingredients based on the user's meal history. Furthermore, the creation unit can analyze the user's meal history and provide recipes that take nutritional balance into account. For example, the creation unit can provide recipes that match the user's preferences based on dishes the user has made in the past. The creation unit can also provide recipes that take into account allergies or disliked ingredients based on the user's meal history. By referring to the meal history, recipes that match the user's preferences can be provided. Some or all of the above-described processing in the creation unit may be performed using, or without, a generation AI. For example, the creation unit can input the user's meal history data into the generation AI, which can then improve the accuracy of the recipe.

[0099] When creating a custom recipe, the creation unit can customize the recipe by taking into account the user's allergy information. Allergy information includes, but is not limited to, food allergies and environmental allergies. For example, the creation unit can provide a recipe that excludes ingredients to which the user is allergic. The creation unit can also provide a recipe that uses alternative ingredients based on the user's allergy information. Furthermore, the creation unit can provide a recipe that does not cause an allergic reaction by taking into account the user's allergy information. For example, the creation unit can provide a recipe that excludes ingredients to which the user is allergic. The creation unit can also provide a recipe that uses alternative ingredients based on the user's allergy information. By taking into account the allergy information, an appropriate recipe can be provided while protecting the user's health. Some or all of the above-described processing in the creation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the creation unit can input the user's allergy information data into the generation AI and customize the recipe using the generation AI.

[0100] The creation unit can estimate the user's emotions and adjust the length of the custom recipe based on the estimated user's emotions. Examples of recipe length include, but are not limited to, the number of steps and the number of characters. For example, if the user is feeling stressed, the creation unit can provide a short and simple recipe. Furthermore, if the user is feeling relaxed, the creation unit can provide a recipe with detailed instructions. Furthermore, if the user is in a hurry, the creation unit can provide a recipe that can be made in a short time. For example, if the user is feeling stressed, the creation unit can provide a short and simple recipe. Furthermore, if the user is feeling relaxed, the creation unit can provide a recipe with detailed instructions. In this way, by adjusting the length of the recipe according to the user's emotions, a recipe that suits the user's situation can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the creation unit may be performed using, for example, AI, or without AI. For example, the creation unit can input the user's emotional data into the generation AI, which can then estimate the emotion and adjust the length of the custom recipe.

[0101] When creating a custom recipe, the creation unit can determine the priority of recipes based on the user's meal timings. Meal timings include, but are not limited to, breakfast, lunch, and dinner. For example, when a user is making breakfast, the creation unit can prioritize providing recipes suitable for breakfast. Furthermore, when a user is making lunch, the creation unit can prioritize providing recipes suitable for lunch. Furthermore, when a user is making dinner, the creation unit can prioritize providing recipes suitable for dinner. For example, when a user is making breakfast, the creation unit can prioritize providing recipes suitable for breakfast. Furthermore, when a user is making lunch, the creation unit can prioritize providing recipes suitable for lunch. Thus, by determining the priority of recipes based on meal timings, recipes can be provided at appropriate times. Some or all of the above-described processing in the creation unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the creation unit can input the user's meal timing data into the generation AI, and the generation AI can determine the priority of recipes.

[0102] When creating a custom recipe, the creation unit can adjust the order of recipes based on the user's meal associations. Meal associations include, but are not limited to, nutritional balance and ingredient combinations. For example, the creation unit can provide recipes related to dishes the user made the previous day. The creation unit can also provide recipes that reuse ingredients from dishes the user made in the past. Furthermore, the creation unit can adjust the order of recipes to provide a balanced meal based on the user's meal history. For example, the creation unit can provide recipes related to dishes the user made the previous day. The creation unit can also provide recipes that reuse ingredients from dishes the user made in the past. By adjusting the order of recipes based on the meal associations, a balanced meal can be provided. Some or all of the above-described processing in the creation unit may be performed using, or without, a generation AI. For example, the creation unit can input the user's meal history data into the generation AI and adjust the order of recipes using the generation AI.

[0103] When creating a custom recipe, the creation unit can adjust the use of technical terms in the recipe according to the user's level of expertise. Examples of levels of expertise include, but are not limited to, beginner, intermediate, and advanced. For example, if the user is a beginner, the creation unit can provide a recipe explained in simple terms. Furthermore, if the user is an intermediate user, the creation unit can provide a recipe containing slightly more technical terms. Furthermore, if the user is an advanced user, the creation unit can provide a detailed recipe using a lot of technical terms. For example, if the user is a beginner, the creation unit can provide a recipe explained in simple terms. Furthermore, if the user is an intermediate user, the creation unit can provide a recipe containing slightly more technical terms. In this way, by adjusting the technical terms in the recipe according to the user's level of expertise, it is possible to provide a recipe that is easy to understand. Some or all of the above-described processing in the creation unit may be performed using, or without, a generation AI. For example, the creation unit can input the user's level of expertise data into the generation AI, and the generation AI can adjust the use of technical terms in the recipe.

[0104] The instruction unit can estimate the user's emotions and adjust the cooking instruction method based on the estimated user emotions. Examples of cooking instruction methods include, but are not limited to, video tutorials and text guides. For example, if the user is stressed, the instruction unit can provide simple, easy-to-understand instruction. Furthermore, if the user is relaxed, the instruction unit can provide instruction with detailed explanations and tips. Furthermore, if the user is in a hurry, the instruction unit can provide instruction that covers the key points in a short amount of time. For example, if the user is stressed, the instruction unit can provide simple, easy-to-understand instruction. Furthermore, if the user is relaxed, the instruction unit can provide instruction with detailed explanations and tips. This allows the cooking instruction method to be adjusted according to the user's emotions, thereby providing more appropriate instruction. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the instruction unit may be performed using, for example, an AI, or without an AI. For example, the instruction department can input the user's emotional data into the generation AI, which can then estimate the emotion and adjust the cooking instruction method.

[0105] When providing cooking instruction, the instruction unit can improve the accuracy of the instruction by referring to the user's past cooking history. Cooking history includes, for example, past cooking records, cooking app data, etc., but is not limited to these examples. For example, the instruction unit provides instruction tailored to the user's preferences based on dishes the user has made in the past. The instruction unit can also provide instruction that takes into account allergies and disliked ingredients based on the user's cooking history. Furthermore, the instruction unit can analyze the user's cooking history and provide instruction aimed at improving cooking skills. For example, the instruction unit provides instruction tailored to the user's preferences based on dishes the user has made in the past. The instruction unit can also provide instruction that takes into account allergies and disliked ingredients based on the user's cooking history. By referring to the past cooking history, instruction tailored to the user's preferences can be provided. Some or all of the above-described processing by the instruction unit may be performed, for example, using AI, or may be performed without AI. For example, the instruction unit can input the user's cooking history data into a generation AI, which can then improve the accuracy of the instruction.

[0106] When providing cooking instruction, the instruction unit can customize the instruction content based on the user's current cooking environment. The cooking environment includes, but is not limited to, kitchen equipment and cooking utensils used. For example, the instruction unit provides instruction tailored to the user's cooking utensils. The instruction unit can also provide instruction on efficient cooking methods based on the size and layout of the user's kitchen. Furthermore, the instruction unit can provide instruction on appropriate cooking temperatures and times according to the user's cooking environment. For example, the instruction unit provides instruction tailored to the user's cooking utensils. The instruction unit can also provide instruction on efficient cooking methods based on the size and layout of the user's kitchen. This allows efficient cooking to be supported by customizing the instruction content based on the current cooking environment. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without AI. For example, the instruction unit can input the user's cooking environment data into a generation AI and have the generation AI customize the instruction content.

[0107] When providing cooking instruction, the instruction unit can improve the instruction method by reflecting user feedback. Examples of feedback include, but are not limited to, user reviews and survey results. For example, the instruction unit can adjust the instruction method based on feedback previously provided by the user. The instruction unit can also improve specific cooking procedures by reflecting user feedback. Furthermore, the instruction unit can customize the instruction content based on user feedback. For example, the instruction unit can adjust the instruction method based on feedback previously provided by the user. The instruction unit can also improve specific cooking procedures by reflecting user feedback. In this way, by reflecting feedback, the optimal instruction method can be provided to the user. Some or all of the above-described processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input user feedback data into a generation AI, which can then improve the instruction method.

[0108] The instructor can estimate the user's emotions and determine the priority of cooking instruction based on the estimated user's emotions. Priorities include, but are not limited to, importance and urgency. For example, if the user is feeling stressed, the instructor can prioritize instruction on relaxing cooking methods. Furthermore, if the user is relaxed, the instructor can prioritize detailed cooking instructions. Furthermore, if the user is in a hurry, the instructor can prioritize instruction on cooking methods that can be made in a short time. For example, if the user is feeling stressed, the instructor can prioritize instruction on relaxing cooking methods. Furthermore, if the user is relaxed, the instructor can prioritize detailed cooking instructions. Thus, by determining the priority of cooking instruction according to the user's emotions, important instruction can be provided preferentially. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the instructor can be performed using, for example, AI, or without AI. For example, the instruction department can input the user's emotional data into the generation AI, which can then estimate the emotions and determine the priority of cooking instruction.

[0109] When providing cooking instruction, the instruction unit can select the optimal instruction method by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, if the user is at high altitude, the instruction unit can provide instruction on a cooking method appropriate for the altitude. Furthermore, if the user is in an urban area, the instruction unit can provide instruction on a cooking method using ingredients that are easily available in the urban area. Furthermore, if the user is cooking outdoors, the instruction unit can provide instruction on a cooking method appropriate for outdoor use. For example, if the user is at high altitude, the instruction unit can provide instruction on a cooking method appropriate for the altitude. Furthermore, if the user is in an urban area, the instruction unit can provide instruction on a cooking method using ingredients that are easily available in the urban area. In this way, by taking geographical location information into consideration, an instruction method appropriate for the user can be provided. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input the user's geographical location information data into a generation AI, which can select the optimal instruction method.

[0110] When providing cooking instruction, the instruction unit can analyze the user's social media activity to suggest instructional content. Social media activity includes, but is not limited to, post content, the number of likes, and comments. For example, the instruction unit can suggest instructional content based on the user's shared social media posts. The instruction unit can also analyze the user's social media posts to suggest related cooking methods. Furthermore, the instruction unit can suggest related cooking methods based on the user's social media friends' activities. For example, the instruction unit can suggest instructional content based on the user's shared social media posts. The instruction unit can also analyze the user's social media posts to suggest related cooking methods. In this way, by analyzing social media activity, relevant instructional content can be provided to the user. Some or all of the above-mentioned processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the user's social media activity data into a generation AI, which then suggests instructional content.

[0111] When providing cooking instruction, the instruction unit can customize the instruction method by reflecting the user's past feedback. Examples of feedback include, but are not limited to, user reviews and survey results. For example, the instruction unit can adjust the instruction method based on feedback previously provided by the user. The instruction unit can also improve specific cooking procedures by reflecting the user's feedback. Furthermore, the instruction unit can customize the instruction content based on the user's feedback. For example, the instruction unit can adjust the instruction method based on feedback previously provided by the user. The instruction unit can also improve specific cooking procedures by reflecting the user's feedback. In this way, by reflecting the past feedback, the optimal instruction method can be provided to the user. Some or all of the above-described processing in the instruction unit may be performed, for example, using AI, or may be performed without using AI. For example, the instruction unit can input the user's feedback data into a generation AI and use the generation AI to customize the instruction method.

[0112] The suggestion unit can estimate the user's emotions and adjust the presentation of ingredient alternative suggestions based on the estimated user emotions. Examples of presentation methods include, but are not limited to, the length of text and the use of images. For example, if the user is stressed, the suggestion unit can provide simple and easy-to-understand alternative suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide alternative suggestions that include detailed explanations and tips. Furthermore, if the user is in a hurry, the suggestion unit can provide alternative suggestions that can be obtained quickly. For example, if the user is stressed, the suggestion unit can provide simple and easy-to-understand alternative suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide alternative suggestions that include detailed explanations and tips. This allows for more appropriate alternative suggestions to be provided by adjusting the presentation of ingredient alternative suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's emotional data into the generation AI, which can then estimate the emotion and adjust the way the ingredient substitution suggestions are presented.

[0113] When proposing ingredient alternatives, the suggestion unit can customize the suggestions by taking into account the user's allergy information. Allergy information includes, but is not limited to, food allergies and environmental allergies. For example, the suggestion unit can provide alternatives that exclude ingredients to which the user is allergic. The suggestion unit can also make suggestions using alternative ingredients based on the user's allergy information. Furthermore, the suggestion unit can provide alternatives that do not cause allergic reactions by taking into account the user's allergy information. For example, the suggestion unit can provide alternatives that exclude ingredients to which the user is allergic. The suggestion unit can also make suggestions using alternative ingredients based on the user's allergy information. By taking into account the allergy information, appropriate alternatives can be provided while protecting the user's health. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's allergy information data into a generation AI, which can then customize the suggestions.

[0114] When proposing ingredient alternatives, the suggestion unit can adjust the content of the suggestions by taking into account the user's food culture and preferences. Food culture includes, for example, regional ingredients and cooking methods, but is not limited to such examples. The suggestion unit, for example, provides alternatives based on the user's food culture. The suggestion unit can also provide alternatives that match the user's preferences by taking into account the user's tastes. Furthermore, the suggestion unit can provide alternatives that match the user's preferences based on the user's past eating history. For example, the suggestion unit provides alternatives based on the user's food culture. The suggestion unit can also provide alternatives that match the user's preferences by taking into account the user's tastes. In this way, alternatives that match the user can be provided by taking into account the food culture and preferences. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's food culture and preference data into a generation AI, and the generation AI can adjust the content of the suggestions.

[0115] When proposing ingredient alternatives, the suggestion unit can improve the accuracy of the suggestions by referring to the user's past suggestion history. The suggestion history includes, for example, past suggestion records, suggestion app data, etc., but is not limited to these examples. For example, the suggestion unit adjusts the suggestion content based on alternatives previously accepted by the user. The suggestion unit can also analyze the user's past suggestion history to provide optimal alternatives. Furthermore, the suggestion unit can provide alternatives that match the user's preferences based on the user's past suggestion history. For example, the suggestion unit adjusts the suggestion content based on alternatives previously accepted by the user. The suggestion unit can also analyze the user's past suggestion history to provide optimal alternatives. By referring to the past suggestion history, optimal alternatives can be provided to the user. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input the user's suggestion history data into a generation AI, which can improve the accuracy of the suggestions.

[0116] The suggestion unit can estimate the user's emotions and prioritize ingredient alternatives based on the estimated user's emotions. Priorities include, but are not limited to, importance and urgency. For example, when the user is stressed, the suggestion unit can prioritize providing easily available alternatives. Furthermore, when the user is relaxed, the suggestion unit can prioritize providing alternatives that include detailed explanations. Furthermore, when the user is in a hurry, the suggestion unit can prioritize providing alternatives that can be obtained quickly. For example, when the user is stressed, the suggestion unit can prioritize providing easily available alternatives. Furthermore, when the user is relaxed, the suggestion unit can prioritize providing alternatives that include detailed explanations. Thus, by prioritizing alternatives according to the user's emotions, important alternatives can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input user emotion data into a generation AI, which may infer the emotion and determine the priority of ingredient substitution suggestions.

[0117] When proposing ingredient alternatives, the suggestion unit can propose optimal alternatives by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in an urban area, the suggestion unit can provide alternatives that are easily available in the urban area. Furthermore, if the user is in a rural area, the suggestion unit can provide alternatives that are easily available in the rural area. Furthermore, if the user is in a specific region, the suggestion unit can provide alternatives that are easily available in the region. For example, if the user is in an urban area, the suggestion unit can provide alternatives that are easily available in the urban area. Furthermore, if the user is in a rural area, the suggestion unit can provide alternatives that are easily available in the rural area. In this way, by taking geographical location information into consideration, alternatives suitable for the user can be provided. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information data into a generation AI, which can then propose optimal alternatives.

[0118] When proposing ingredient alternatives, the suggestion unit can analyze the user's social media activity to suggest relevant alternatives. Social media activity includes, but is not limited to, post content, the number of likes, and comments. For example, the suggestion unit can suggest alternatives based on dishes shared by the user on social media. The suggestion unit can also analyze the user's social media posts to suggest relevant alternatives. Furthermore, the suggestion unit can suggest relevant alternatives based on the activities of the user's friends on social media. For example, the suggestion unit can suggest alternatives based on dishes shared by the user on social media. The suggestion unit can also analyze the user's social media posts to suggest relevant alternatives. In this way, by analyzing social media activity, relevant alternatives can be provided to the user. Some or all of the above-described processing by the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's social media activity data into a generation AI, which then suggests relevant alternatives.

[0119] When proposing ingredient alternatives, the suggestion unit can customize the suggestions by reflecting the user's past feedback. Examples of feedback include, but are not limited to, user reviews and survey results. For example, the suggestion unit can adjust the suggestions based on feedback previously provided by the user. The suggestion unit can also improve specific alternatives by reflecting user feedback. Furthermore, the suggestion unit can customize the suggestions based on user feedback. For example, the suggestion unit can adjust the suggestions based on feedback previously provided by the user. The suggestion unit can also improve specific alternatives by reflecting user feedback. In this way, optimal alternatives can be provided to the user by reflecting past feedback. Some or all of the above-described processing in the suggestion unit may be performed, for example, using AI or without AI. For example, the suggestion unit can input user feedback data into a generation AI and have the generation AI customize the suggestions.

[0120] The management unit can estimate the user's emotions and adjust the management method for inventory and expiration dates based on the estimated user emotions. Examples of management methods include, but are not limited to, inventory management systems and expiration date management apps. For example, the management unit can provide a simple and easy-to-understand management method when the user is stressed. Furthermore, the management unit can provide a management method with detailed explanations and tips when the user is relaxed. Furthermore, the management unit can provide a method that can be managed quickly when the user is in a hurry. For example, the management unit can provide a simple and easy-to-understand management method when the user is stressed. Furthermore, the management unit can provide a management method with detailed explanations and tips when the user is relaxed. This allows for efficient management of inventory and expiration dates by adjusting the management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the management unit can be performed using, for example, AI, or without AI. For example, the management department can input the user's emotional data into the generation AI, which can then estimate the emotion and adjust the management method for inventory ingredients and expiration dates.

[0121] The management unit can improve the accuracy of management of inventory ingredients and expiration dates by referring to the user's past consumption history. Consumption history includes, for example, past consumption records and consumption app data, but is not limited to these examples. For example, the management unit can propose an optimal management method based on the user's past consumption history. The management unit can also analyze the user's past consumption history to optimize expiration date management. Furthermore, the management unit can adjust the inventory ingredient management method based on the user's past consumption history. For example, the management unit can propose an optimal management method based on the user's past consumption history. The management unit can also analyze the user's past consumption history to optimize expiration date management. By referring to the past consumption history, the management accuracy of inventory ingredients and expiration dates can be improved. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without AI. For example, the management unit can input the user's consumption history data into a generation AI, which can improve the management accuracy.

[0122] The management unit can customize the management content based on the user's current lifestyle when managing inventory and expiration dates. Examples of lifestyle include, but are not limited to, daily activity levels and work schedules. For example, the management unit can provide a simple management method when the user is busy. Furthermore, the management unit can also provide a detailed management method when the user is relaxed. Furthermore, the management unit can adjust the management method of inventory and expiration dates according to the user's lifestyle. For example, the management unit can provide a simple management method when the user is busy. Furthermore, the management unit can also provide a detailed management method when the user is relaxed. By customizing the management content based on the user's current lifestyle, the optimal management method can be provided to the user. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without AI. For example, the management unit can input the user's lifestyle data into a generation AI, which can customize the management content.

[0123] The management unit can improve the management method by reflecting user feedback when managing inventory ingredients and expiration dates. Examples of feedback include, but are not limited to, user reviews and survey results. For example, the management unit can adjust the management method based on feedback previously provided by the user. The management unit can also improve specific management procedures by reflecting user feedback. Furthermore, the management unit can customize the management content based on user feedback. For example, the management unit can adjust the management method based on feedback previously provided by the user. The management unit can also improve specific management procedures by reflecting user feedback. By reflecting the feedback, the optimal management method can be provided to the user. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input user feedback data into a generation AI, which can then improve the management method.

[0124] The management unit can estimate the user's emotions and prioritize inventory ingredients and expiration dates based on the estimated user's emotions. Priorities include, but are not limited to, importance and urgency. For example, when the user is stressed, the management unit can prioritize providing ingredients that are easy to manage. Furthermore, when the user is relaxed, the management unit can prioritize providing detailed management methods. Furthermore, when the user is in a hurry, the management unit can prioritize providing ingredients that can be managed in a short time. For example, when the user is stressed, the management unit can prioritize providing ingredients that are easy to manage. Furthermore, when the user is relaxed, the management unit can prioritize providing detailed management methods. Thus, by determining priorities according to the user's emotions, important ingredients can be prioritized for management. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the management unit can be performed using, for example, AI, or without AI. For example, the management department can input user emotion data into the generation AI, which can then estimate the emotion and determine priorities for inventory ingredients and expiration dates.

[0125] The management unit can select an optimal management method by taking into account the user's geographical location information when managing inventory ingredients and expiration dates. Examples of geographical location information include, but are not limited to, GPS data and location information services. For example, if the user is in an urban area, the management unit can prioritize managing ingredients that are easily available in the urban area. Furthermore, if the user is in a rural area, the management unit can prioritize managing ingredients that are easily available in the rural area. Furthermore, if the user is in a specific region, the management unit can prioritize managing ingredients that are easily available in that region. For example, if the user is in an urban area, the management unit can prioritize managing ingredients that are easily available in the urban area. Furthermore, if the user is in a rural area, the management unit can prioritize managing ingredients that are easily available in the rural area. This allows a management method suited to the user to be provided by taking into account the geographical location information. Some or all of the above-described processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's geographical location information data into a generation AI, which can select an optimal management method.

[0126] The management unit can analyze a user's social media activity to suggest management content when managing inventory and expiration dates of ingredients. Social media activity includes, but is not limited to, post content, number of likes, and comments. For example, the management unit manages inventory based on recipes shared by the user on social media. The management unit can also analyze the user's social media posts to manage expiration dates of related ingredients. Furthermore, the management unit can suggest management methods for related ingredients based on the activity of the user's friends on social media. For example, the management unit manages inventory based on recipes shared by the user on social media. The management unit can also analyze the user's social media posts to manage expiration dates of related ingredients. In this way, by analyzing social media activity, relevant management content can be provided to the user. Some or all of the above-mentioned processing in the management unit may be performed using, for example, AI, or may be performed without using AI. For example, the management unit can input the user's social media activity data into a generation AI, which then suggests management content.

[0127] The management unit can customize the management method by reflecting the user's past feedback when managing inventory ingredients and expiration dates. Examples of feedback include, but are not limited to, user reviews and survey results. For example, the management unit can adjust the management method based on feedback previously provided by the user. The management unit can also improve specific management procedures by reflecting user feedback. Furthermore, the management unit can customize the management content based on user feedback. For example, the management unit can adjust the management method based on feedback previously provided by the user. The management unit can also improve specific management procedures by reflecting user feedback. In this way, by reflecting past feedback, the optimal management method can be provided to the user. Some or all of the above-described processing in the management unit may be performed, for example, using AI, or may be performed without using AI. For example, the management unit can input user feedback data into a generation AI and have the generation AI customize the management method.

[0128] The reminder unit can estimate the user's emotions and adjust the reminder method based on the estimated user emotions. Examples of reminder methods include, but are not limited to, notification frequency and notification method (email, app notification, etc.). For example, if the user is feeling stressed, the reminder unit can provide a simple and easy-to-understand reminder. Furthermore, if the user is relaxed, the reminder unit can provide a reminder with detailed explanations and tips. Furthermore, if the user is in a hurry, the reminder unit can provide a quick and concise reminder. For example, if the user is feeling stressed, the reminder unit can provide a simple and easy-to-understand reminder. Furthermore, if the user is relaxed, the reminder unit can provide a reminder with detailed explanations and tips. This allows for more effective reminders by adjusting the reminder method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit may input the user's emotion data into the generation AI, have the generation AI estimate the emotion, and adjust the reminding method.

[0129] The reminding unit can improve the accuracy of reminders by referring to the user's past reminder history. Reminder history includes, but is not limited to, past reminder records and reminder app data. For example, the reminding unit can suggest an optimal reminder method based on the user's past reminder history. The reminding unit can also analyze the user's past reminder history and adjust the timing of reminders. Furthermore, the reminding unit can customize the content of reminders based on the user's past reminder history. For example, the reminding unit can suggest an optimal reminder method based on the user's past reminder history. The reminding unit can also analyze the user's past reminder history and adjust the timing of reminders. By referring to the past reminder history, the accuracy of reminders is improved. Some or all of the above-described processing in the reminding unit may be performed using, for example, AI, or may be performed without AI. For example, the reminding unit can input the user's reminder history data into a generation AI, which can improve the accuracy of reminders.

[0130] The reminder unit can customize the reminder content based on the user's current living situation when providing a reminder. Examples of living situations include, but are not limited to, daily activity levels and work schedules. For example, if the user is busy, the reminder unit can provide an easily understandable reminder. Furthermore, if the user is relaxed, the reminder unit can provide a detailed reminder. Furthermore, the reminder unit can adjust the reminder content according to the user's living situation. For example, if the user is busy, the reminder unit can provide an easily understandable reminder. Furthermore, if the user is relaxed, the reminder unit can provide a detailed reminder. By customizing the reminder content based on the user's current living situation, the user can be provided with an optimal reminder. Some or all of the above-described processing in the reminder unit may be performed using, for example, AI, or may be performed without AI. For example, the reminder unit can input the user's living situation data into a generation AI, which can customize the reminder content.

[0131] The reminding unit can improve the reminding method by reflecting user feedback when reminding. Examples of feedback include, but are not limited to, user reviews and survey results. The reminding unit can adjust the reminding method based on, for example, feedback previously provided by the user. The reminding unit can also improve specific reminding procedures by reflecting user feedback. Furthermore, the reminding unit can customize the reminding content based on user feedback. For example, the reminding unit can adjust the reminding method based on feedback previously provided by the user. The reminding unit can also improve specific reminding procedures by reflecting user feedback. By reflecting past feedback, the optimal reminding method can be provided to the user. Some or all of the above-described processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit can input user feedback data into a generation AI, which can then improve the reminding method.

[0132] The reminder unit can estimate the user's emotions and determine the priority of reminders based on the estimated user emotions. Priorities include, but are not limited to, importance and urgency. For example, if the user is stressed, the reminder unit can prioritize reminders that can be easily addressed. Furthermore, if the user is relaxed, the reminder unit can prioritize detailed reminders. Furthermore, if the user is in a hurry, the reminder unit can prioritize reminders that can be quickly addressed. For example, if the user is stressed, the reminder unit can prioritize reminders that can be easily addressed. Furthermore, if the user is relaxed, the reminder unit can prioritize detailed reminders. Thus, by determining the priority of reminders according to the user's emotions, important reminders can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit may input user emotion data into a generating AI, which may estimate the emotion and determine the priority of reminders.

[0133] The reminding unit can select an optimal reminding method by taking into account the user's geographical location information when reminding. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, if the user is in an urban area, the reminding unit can provide a reminding method for the urban area. Furthermore, if the user is in a rural area, the reminding unit can provide a reminding method for the rural area. Furthermore, if the user is in a specific region, the reminding unit can provide a reminding method suitable for that region. For example, if the user is in an urban area, the reminding unit can provide a reminding method for the urban area. Furthermore, if the user is in a rural area, the reminding unit can provide a reminding method suitable for the rural area. In this way, a reminding method suitable for the user can be provided by taking into account the geographical location information. Some or all of the above-described processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit can input the user's geographical location information data into a generation AI, which can select an optimal reminding method.

[0134] The reminder unit may analyze the user's social media activity to suggest reminder content when providing a reminder. Social media activity includes, but is not limited to, post content, the number of likes, and comments. For example, the reminder unit may provide reminders based on content shared by the user on social media. The reminder unit may also analyze the user's social media posts and provide related reminders. Furthermore, the reminder unit may provide related reminders based on the activities of the user's friends on social media. For example, the reminder unit may provide reminders based on content shared by the user on social media. The reminder unit may also analyze the user's social media posts and provide related reminders. In this way, by analyzing social media activity, relevant reminder content can be provided to the user. Some or all of the above-described processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit may input the user's social media activity data into a generation AI, which may then suggest reminder content.

[0135] The reminding unit can customize the reminding method by reflecting the user's past feedback when reminding. Examples of feedback include, but are not limited to, user reviews and survey results. The reminding unit can adjust the reminding method based on, for example, feedback provided by the user in the past. The reminding unit can also improve specific reminding procedures by reflecting the user's feedback. Furthermore, the reminding unit can customize the reminding content based on the user's feedback. For example, the reminding unit can adjust the reminding method based on the user's past feedback. The reminding unit can also improve specific reminding procedures by reflecting the user's feedback. In this way, by reflecting the past feedback, the optimal reminding method can be provided to the user. Some or all of the above-described processing in the reminding unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminding unit can input the user's feedback data into a generation AI and use the generation AI to customize the reminding method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned acquisition unit, creation unit, instruction unit, suggestion unit, management unit, and remind unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit can acquire the user's health information using the camera 42 or microphone 38B of the smart device 14. The creation unit can create custom recipes using the specific processing unit 290 of the data processing device 12. The instruction unit can provide cooking guidance using the control unit 46A of the smart device 14. The suggestion unit can suggest alternative ingredients using the specific processing unit 290 of the data processing device 12. The management unit can manage inventory ingredients and expiration dates using the control unit 46A of the smart device 14. The reminder unit can provide ingredient consumption reminders using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned acquisition unit, creation unit, instruction unit, suggestion unit, management unit, and remind unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit can acquire the user's health information using the camera 42 or microphone 238 of the smart glasses 214. The creation unit can create custom recipes using the specific processing unit 290 of the data processing device 12. The instruction unit can provide cooking guidance using the control unit 46A of the smart glasses 214. The suggestion unit can suggest alternative ingredients using the specific processing unit 290 of the data processing device 12. The management unit can manage inventory ingredients and expiration dates using the control unit 46A of the smart glasses 214. The reminder unit can provide ingredient consumption reminders using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, creation unit, instruction unit, suggestion unit, management unit, and reminder unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit can acquire the user's health information using the camera 42 or microphone 238 of the headset terminal 314. The creation unit can create custom recipes using the specific processing unit 290 of the data processing device 12. The instruction unit can provide cooking guidance using the control unit 46A of the headset terminal 314. The suggestion unit can suggest alternative ingredients using the specific processing unit 290 of the data processing device 12. The management unit can manage inventory ingredients and expiration dates using the control unit 46A of the headset terminal 314. The reminder unit can provide ingredient consumption reminders using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned acquisition unit, creation unit, instruction unit, suggestion unit, management unit, and remind unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit can acquire the user's health information using the camera 42 or microphone 238 of the robot 414. The creation unit can create custom recipes using the specific processing unit 290 of the data processing device 12. The instruction unit can provide cooking guidance using the control unit 46A of the robot 414. The suggestion unit can suggest alternative ingredients using the specific processing unit 290 of the data processing device 12. The management unit can manage inventory ingredients and expiration dates using the control unit 46A of the robot 414. The reminder unit can provide ingredient consumption reminders using the specific processing unit 290 of the data processing device 12.

[0136] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0137] When acquiring the user's health information, the acquisition unit can analyze the user's past health data and select the optimal acquisition method. For example, the acquisition unit can select the most effective data acquisition method based on the user's past diagnosis results and fitness tracker data. The acquisition unit can also adjust the frequency of data acquisition based on the user's past health data. Furthermore, the acquisition unit can analyze the user's past health data and acquire data at specific time periods. This allows the optimal data acquisition method to be selected by analyzing the past health data.

[0138] When creating a custom recipe, the creation unit can improve the accuracy of the recipe by referring to the user's dietary history. For example, the creation unit can provide recipes that match the user's preferences based on dishes the user has made in the past. The creation unit can also provide recipes that take into account allergies and disliked ingredients based on the user's dietary history. Furthermore, the creation unit can analyze the user's dietary history and provide recipes that take nutritional balance into consideration. In this way, by referring to the dietary history, recipes that match the user's preferences can be provided.

[0139] When providing cooking instruction, the instruction unit can improve the accuracy of the instruction by referring to the user's past cooking history. For example, the instruction can be tailored to the user's preferences based on dishes the user has made in the past. The instruction unit can also provide instruction that takes into account allergies and disliked ingredients based on the user's cooking history. Furthermore, the instruction unit can analyze the user's cooking history and provide instruction aimed at improving cooking skills. This allows the instruction to be tailored to the user's preferences by referring to the user's past cooking history.

[0140] When proposing ingredient substitution suggestions, the suggestion unit can customize the suggestions by taking into account the user's allergy information. For example, the suggestion unit can provide alternative suggestions that exclude ingredients to which the user has allergies. The suggestion unit can also make suggestions using alternative ingredients based on the user's allergy information. Furthermore, the suggestion unit can take into account the user's allergy information and provide alternative suggestions that do not cause allergic reactions. In this way, by taking into account the allergy information, appropriate alternative suggestions can be provided while protecting the user's health.

[0141] The management unit can improve the accuracy of management when managing inventory ingredients and expiration dates by referring to the user's past consumption history. For example, the management unit can propose an optimal management method based on the user's past consumption history. The management unit can also analyze the user's past consumption history and optimize expiration date management. Furthermore, the management unit can adjust the inventory ingredient management method based on the user's past consumption history. In this way, by referring to the past consumption history, the accuracy of management of inventory ingredients and expiration dates can be improved.

[0142] The acquisition unit can estimate the user's emotions and adjust the timing of health information acquisition based on the estimated user emotions. For example, if the user is feeling stressed, health information is acquired during a time when the user is able to relax. Furthermore, if the user is relaxed, the acquisition unit can collect data for a long period of time to acquire detailed health information. Furthermore, if the user is in a hurry, the acquisition unit can acquire necessary health information in a short period of time. This allows data to be acquired at a more appropriate time by adjusting the timing of health information acquisition according to the user's emotions.

[0143] The creation unit can estimate the user's emotions and adjust the way the custom recipe is presented based on the estimated user's emotions. For example, if the user is feeling stressed, the creation unit can provide a simple and easy-to-understand recipe. If the user is relaxed, the creation unit can also provide a recipe that includes detailed instructions and tips. Furthermore, if the user is in a hurry, the creation unit can provide a recipe that can be made in a short amount of time. This allows the creation unit to provide a more appropriate recipe by adjusting the way the custom recipe is presented according to the user's emotions.

[0144] The teaching unit can estimate the user's emotions and adjust the cooking instruction method based on the estimated user's emotions. For example, if the user is feeling stressed, the teaching unit can provide simple, easy-to-understand instruction. If the user is relaxed, the teaching unit can also provide instruction that includes detailed explanations and tips. Furthermore, if the user is in a hurry, the teaching unit can provide instruction that focuses on the main points in a short amount of time. This allows the system to provide more appropriate instruction by adjusting the cooking instruction method according to the user's emotions.

[0145] The suggestion unit can estimate the user's emotions and adjust the way ingredient substitution suggestions are presented based on the estimated user's emotions. For example, if the user is feeling stressed, the suggestion unit can provide simple and easy-to-understand alternative suggestions. If the user is relaxed, the suggestion unit can also provide alternative suggestions that include detailed explanations and tips. Furthermore, if the user is in a hurry, the suggestion unit can provide alternative suggestions that can be obtained in a short time. In this way, by adjusting the way ingredient substitution suggestions are presented according to the user's emotions, more appropriate alternative suggestions can be provided.

[0146] The management unit can estimate the user's emotions and adjust the management method for inventory ingredients and expiration dates based on the estimated user's emotions. For example, if the user is feeling stressed, the management unit can provide a simple and easy-to-understand management method. If the user is relaxed, the management unit can also provide a management method that includes detailed explanations and tips. Furthermore, if the user is in a hurry, the management unit can provide a method that can be managed in a short amount of time. In this way, by adjusting the management method according to the user's emotions, inventory ingredients and expiration dates can be managed efficiently.

[0147] The processing flow of the second embodiment will be briefly explained below.

[0148] Step 1: The acquisition unit acquires the user's health information. The health information includes, but is not limited to, for example, heart rate, blood pressure, and weight. The acquisition unit acquires the health information from, for example, a wearable device. The acquisition unit can also select the optimal acquisition means depending on the user's input method (voice, text, image, etc.). Step 2: The creation unit creates a custom recipe based on the acquired health information. The custom recipe is created taking into consideration, for example, nutritional balance, calorie restriction, etc., but is not limited to these examples. The creation unit uses a generation AI to create a custom recipe based on the user's health information. Step 3: The instruction unit provides cooking instruction based on the custom recipe created by the creation unit. The cooking instruction may include, but is not limited to, cooking procedures, temperature control, cooking time, etc. The instruction unit may provide advice on cooking procedures, temperature control, and cooking time in real time. Step 4: The suggestion unit suggests alternative ingredients based on the cooking instructions provided by the instruction unit. The alternative ingredients are suggested taking into consideration, for example, allergy information, food culture, etc., but are not limited to these examples. The suggestion unit can suggest alternative ingredients based on allergy information and food culture. Step 5: The management unit manages the inventory of ingredients and expiration dates based on the alternatives proposed by the proposal unit. For example, barcode scanning, voice input, etc. are used to manage the inventory of ingredients and expiration dates, but are not limited to these examples. The management unit can manage the inventory of ingredients and expiration dates using barcode scanning or voice input. Step 6: The reminding unit performs a food ingredient consumption reminder based on the inventory of food ingredients and expiration dates managed by the management unit. The reminder may, for example, notify the user about food ingredients whose expiration dates are approaching, but is not limited to such an example. The reminding unit may remind the user about food ingredients whose expiration dates are approaching.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0154] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0177] 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.

[0178] 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.

[0179] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0185] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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).

[0206] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0207] 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."

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0219] 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.

[0220] [Explanation of symbols]

[0221] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that acquires health information of a user; a creation unit that creates a custom recipe based on the health information acquired by the acquisition unit; an instruction unit that provides cooking instruction based on the custom recipe created by the creation unit; a suggestion unit that suggests alternative ingredients based on the cooking instructions provided by the instruction unit; a management unit that manages inventory ingredients or expiration dates based on the alternatives proposed by the proposal unit; a reminder unit that reminds the user to consume ingredients based on the inventory of ingredients or expiration date managed by the management unit; A system characterized by:

2. The acquisition unit Obtaining user health information from wearable devices 2. The system of claim 1.

3. The creation unit Create custom recipes that take nutritional balance into account based on acquired health information 2. The system of claim 1.

4. The leadership team: Providing real-time advice on cooking procedures, temperature control, and cooking times 2. The system of claim 1.

5. The proposal unit Proposing alternative ingredients based on allergy information and food culture 2. The system of claim 1.

6. The management unit Manage inventory or expiration dates with barcode scanning or voice input 2. The system of claim 1.

7. The reminding unit Reminding users about food items that are nearing their expiration date 2. The system of claim 1.

8. The acquisition unit The system estimates the user's emotions and adjusts the timing of acquiring health information based on the estimated user emotions.

2. The system of claim 1.

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