System

Abstract

An object of the system according to the embodiment is to integrate personal health data and ingredient information in a refrigerator, create a healthy menu, and procure ingredients.SOLUTION: A system includes a collection part, an analysis part, a food ingredient analysis part, a menu creation part, a material procurement part, and a recipe provision part. The collection part collects health data of a heart rate, the number of steps, and calorie consumption. The analysis unit analyzes the data collected by the collection unit. The food ingredient analysis unit analyzes the food ingredient information collected by the collection unit. The menu creation unit creates a menu based on the data obtained by the analysis unit and the food analysis unit. The material procurement unit procures necessary materials based on the menu created by the menu creation unit. The recipe providing unit provides a method of making the menu created by the menu creating 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 technology has had the problem of not being able to fully integrate personal health data with information about ingredients in the refrigerator to create healthy menus and procure ingredients.

[0005] The system according to the embodiment aims to create healthy menus and procure ingredients by integrating personal health data with information on ingredients in the refrigerator. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an ingredient analysis unit, a menu creation unit, an ingredient procurement unit, and a recipe provision unit. The collection unit collects health data such as heart rate, number of steps, and calories burned. The analysis unit analyzes the data collected by the collection unit. The ingredient analysis unit analyzes the ingredient information collected by the collection unit. The menu creation unit creates a menu based on the data obtained by the analysis unit and the ingredient analysis unit. The ingredient procurement unit procures the necessary ingredients based on the menu created by the menu creation unit. The recipe provision unit provides instructions for making the menu created by the menu creation unit. [Effects of the Invention]

[0007] The system according to the embodiment can integrate personal health data with information about ingredients in the refrigerator to create healthy menus and procure ingredients. [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) A health management support system according to an embodiment of the present invention utilizes AI to support health management, menu planning, and ingredient procurement. The system involves users wearing a wearable device to collect daily health data, such as heart rate, steps taken, and calories burned. This data is then analyzed by AI to determine the user's health status. Next, the refrigerator IoT collects information about the ingredients in the refrigerator, such as the type and quantity of ingredients in the refrigerator and their expiration dates. This data is then analyzed by AI to determine available ingredients. Based on this data, the AI ​​creates a menu tailored to the user's health preferences. For example, if the user is on a diet, low-calorie, nutritionally balanced menus are suggested. If the user has specific allergies, menus that do not contain the allergens are suggested. Furthermore, the AI ​​also assists in procuring the necessary ingredients based on the suggested menu. For example, if an ingredient is missing from the refrigerator, the AI ​​can automatically order it from an online store. Finally, the AI ​​teaches the user how to prepare the suggested menu. For example, it displays recipes on a smartphone or tablet, clearly explaining the cooking steps. It can also provide real-time advice during cooking. This allows the health management support system to enable users to easily prepare delicious meals while managing their health. It also eliminates the need to procure ingredients, making it extremely convenient for busy modern people. This allows the health management support system to grasp the user's health condition, provide optimal menus, and support ingredient procurement and recipe provision. For example, it allows users to easily prepare delicious meals while managing their health, eliminating the need to procure ingredients, making it extremely convenient for busy modern people.

[0029] A health management support system according to an embodiment includes a collection unit, an analysis unit, an ingredient analysis unit, a menu creation unit, an ingredient procurement unit, and a recipe provision unit. The collection unit collects health data such as heart rate, step count, and calories burned. The collection unit measures heart rate, for example, using a wearable device. The collection unit can also count steps using a pedometer. The collection unit can also record the user's activity level to calculate calories burned. The analysis unit analyzes the data collected by the collection unit to understand the user's health condition. The analysis unit can analyze heart rate fluctuations, for example, using AI. The analysis unit can also evaluate the user's exercise level based on step count data. The analysis unit can also evaluate the user's energy consumption based on calorie consumption data. The ingredient analysis unit collects and analyzes information about ingredients in a refrigerator. The ingredient analysis unit can recognize the types of ingredients using, for example, a camera in the refrigerator. The ingredient analysis unit can also measure the amount of ingredients. The ingredient analysis unit can also record the expiration dates of ingredients. The menu creation unit creates a menu based on the data obtained by the analysis unit and the ingredient analysis unit. The menu creation unit, for example, uses AI to suggest a menu tailored to the user's health preferences. The menu creation unit can also create a menu taking into account the nutritional value of ingredients. The menu creation unit can also create a menu taking into account the user's allergy information. The ingredient procurement unit procures necessary ingredients based on the menu created by the menu creation unit. The ingredient procurement unit, for example, orders missing ingredients from an online store. The ingredient procurement unit can also purchase ingredients at a local supermarket. The ingredient procurement unit can also arrange for ingredient delivery. The recipe providing unit provides instructions for making the menu created by the menu creation unit. The recipe providing unit, for example, displays the recipe on a smartphone or tablet. The recipe providing unit can also explain cooking procedures in an easy-to-understand manner. The recipe providing unit can also provide advice in real time during cooking. As a result, the health management support system according to the embodiment can grasp the user's health condition, provide optimal menus, and support ingredient procurement and recipe provision.

[0030] The collection unit can collect health data such as heart rate, number of steps, and calories burned. The collection unit measures the heart rate using, for example, a wearable device. The collection unit records the user's heart rate in real time using, for example, a heart rate sensor. The collection unit can also count the number of steps using a pedometer. The collection unit accurately counts the number of steps taken by the user using, for example, an acceleration sensor. The collection unit can also record the user's activity level in order to calculate calories burned. The collection unit calculates the user's calories burned using, for example, an activity meter. By collecting health data in this way, the user's health condition can be understood.

[0031] The analysis unit analyzes the collected health data and can grasp the user's health condition. The analysis unit, for example, uses AI to analyze heart rate fluctuations. The analysis unit, for example, evaluates the user's stress level based on the heart rate data. The analysis unit can also evaluate the user's exercise amount based on the step count data. The analysis unit, for example, analyzes the user's exercise habits based on the step count data. The analysis unit can also evaluate the user's energy consumption based on the calorie consumption data. The analysis unit, for example, evaluates the effectiveness of the user's diet based on the calorie consumption data. In this way, by analyzing the health data, the user's health condition can be grasped in detail.

[0032] The collection unit can collect information about ingredients in the refrigerator. For example, the collection unit recognizes the type of ingredients using a camera in the refrigerator. For example, the collection unit automatically identifies ingredients in the refrigerator using image recognition technology. The collection unit can also measure the amount of ingredients. For example, the collection unit can accurately measure the amount of ingredients in the refrigerator using a weight sensor. The collection unit can also record the expiration dates of ingredients. For example, the collection unit can automatically record the expiration dates of ingredients using a barcode scanner. In this way, by collecting information about ingredients in the refrigerator, it is possible to know which ingredients are available.

[0033] The ingredient analysis unit analyzes the collected ingredient information and can identify available ingredients. The ingredient analysis unit analyzes the collected ingredient information using, for example, image recognition technology. The ingredient analysis unit identifies the type of ingredient based on, for example, image data of the inside of the refrigerator. The ingredient analysis unit can also evaluate the nutritional value of the ingredient. The ingredient analysis unit evaluates the nutritional value of the ingredient based on, for example, nutritional component data of the ingredient. The ingredient analysis unit can also evaluate the expiration date of the ingredient. The ingredient analysis unit identifies available ingredients based on, for example, the expiration date data of the ingredient. In this way, by analyzing the ingredient information, available ingredients can be identified in detail.

[0034] The menu creation unit can create a menu tailored to the user's health preferences. The menu creation unit, for example, uses AI to suggest menus tailored to the user's health preferences. The menu creation unit, for example, creates a low-calorie, nutritionally balanced menu based on the user's health data. The menu creation unit can also create a menu taking into account the nutritional value of ingredients. The menu creation unit, for example, creates a nutritionally balanced menu based on nutritional component data of ingredients. The menu creation unit can also create a menu taking into account the user's allergy information. For example, the menu creation unit creates a menu that does not contain allergens based on the user's allergen information. This supports health management by providing a menu tailored to the user's health preferences.

[0035] The material procurement department can order necessary ingredients from an online store. For example, the material procurement department orders ingredients that are in short supply from an online store. For example, the material procurement department uses AI to check inventory information from the online store and automatically order the necessary ingredients. The material procurement department can also purchase ingredients from a local supermarket. For example, the material procurement department purchases the necessary ingredients from the nearest supermarket based on the user's location information. The material procurement department can also arrange for the delivery of ingredients. For example, the material procurement department arranges for ingredients ordered from an online store to be delivered to the user's home. This automatically procures the necessary ingredients, eliminating the hassle of procuring ingredients.

[0036] The recipe providing unit can display recipes on a smartphone or tablet and explain cooking steps in an easy-to-understand manner. The recipe providing unit displays recipes on a smartphone or tablet, for example. The recipe providing unit displays recipes optimized for the user's device, for example, using AI. The recipe providing unit can also explain cooking steps in an easy-to-understand manner. The recipe providing unit can, for example, display cooking steps step by step, allowing the user to cook easily. The recipe providing unit can also provide advice in real time during cooking. The recipe providing unit can, for example, use AI to provide advice in real time according to the user's cooking status. In this way, by providing recipes in an easy-to-understand manner, the user can cook easily.

[0037] The collection unit can analyze the user's past health data and select the optimal collection method. The collection unit, for example, uses AI to analyze the user's past health data. The collection unit, for example, identifies the optimal collection timing based on past heart rate data. The collection unit can also select the optimal collection method based on past step count data. The collection unit, for example, optimizes the pedometer settings based on the past step count data. The collection unit can also select the optimal collection method based on past calorie consumption data. The collection unit, for example, optimizes the activity meter settings based on past calorie consumption data. In this way, the optimal collection method can be selected by analyzing past data.

[0038] When collecting health data, the collection unit can filter the data based on the user's current activity status and lifestyle rhythm. The collection unit, for example, uses an activity meter to grasp the user's current activity status. The collection unit, for example, uses the activity meter to record the user's exercise amount in real time. The collection unit can also use a sleep tracker to grasp the user's lifestyle rhythm. The collection unit, for example, uses the sleep tracker to record the user's sleep patterns. This allows for more accurate data collection by filtering the data based on the activity status and lifestyle rhythm.

[0039] When collecting health data, the collection unit can select the optimal collection means according to the user's input method. The collection unit, for example, uses voice recognition technology to identify the user's input method. The collection unit, for example, uses a microphone to analyze the user's voice input and selects the optimal collection means. The collection unit can also identify the user's input method using text analysis technology. The collection unit, for example, analyzes text data entered by the user and selects the optimal collection means. The collection unit can also identify the user's input method using image recognition technology. The collection unit, for example, uses a camera to analyze the user's image input and selects the optimal collection means. This improves the efficiency of data collection by selecting the collection means according to the user's input method.

[0040] When collecting health data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, uses GPS technology to grasp the user's geographical location information. The collection unit, for example, uses a GPS device to record the user's location information in real time. The collection unit can also grasp the geographical location information based on the user's address information. The collection unit, for example, prioritizes collecting highly relevant data based on the user's registered address. In this way, highly relevant data can be prioritized by taking into account the geographical location information.

[0041] When collecting health data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit uses text analysis technology to analyze the user's social media activities. For example, the collection unit analyzes the content of the user's social media posts and collects related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the social media activities of the user's friends and collects related data. In this way, related data can be efficiently collected by analyzing social media activities.

[0042] When collecting health data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, uses text analysis technology to analyze the user's past feedback. The collection unit, for example, analyzes the user's past comments and ratings and customizes the collection method. The collection unit can also customize the collection method based on the user's past survey results. The collection unit, for example, analyzes the user's survey results and optimizes the collection method. In this way, the collection method can be optimized by reflecting the past feedback.

[0043] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health data. The analysis unit uses AI, for example, to evaluate the importance of the health data. The analysis unit performs a detailed analysis of data with high importance based on, for example, heart rate data. The analysis unit can also simply analyze data with low importance based on step count data. The analysis unit simply evaluates the user's amount of exercise based on, for example, step count data. The analysis unit can also analyze data with high importance in detail based on calorie consumption data. The analysis unit evaluates the user's energy consumption in detail based on, for example, calorie consumption data. In this way, important data can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the health data.

[0044] During analysis, the analysis unit can apply different analysis algorithms depending on the category of health data. For example, the analysis unit uses AI to classify the categories of health data. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. The analysis unit can also apply a walking pattern analysis algorithm to step count data. For example, the analysis unit analyzes the user's walking pattern based on the step count data. The analysis unit can also apply an energy consumption analysis algorithm to calorie consumption data. For example, the analysis unit analyzes the user's energy consumption based on the calorie consumption data. This allows for more accurate analysis by applying an analysis algorithm according to the category.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, uses AI to analyze the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on past heart rate analysis results. The analysis unit can also correct the current analysis result based on past step count analysis results. The analysis unit, for example, corrects the current step count data based on past step count analysis results. The analysis unit can also correct the current analysis result based on past calorie consumption analysis results. The analysis unit, for example, corrects the current calorie consumption data based on past calorie consumption analysis results. In this way, the accuracy of the analysis is improved by referring to past analysis results.

[0046] During analysis, the analysis unit can determine the priority of analysis based on when the health data was collected. The analysis unit, for example, uses AI to evaluate when the health data was collected. The analysis unit, for example, prioritizes analysis of recently collected data. The analysis unit can also prioritize analysis of data collected during a specific time period. For example, the analysis unit prioritizes analysis of data collected at night. The analysis unit can also prioritize analysis of data from important time periods based on the user's lifestyle rhythm. For example, the analysis unit prioritizes analysis of data from time periods when the user is most active. In this way, by determining the priority of analysis based on when the health data was collected, important data can be analyzed preferentially.

[0047] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the health data. The analysis unit, for example, uses AI to evaluate the relevance of the health data. The analysis unit can adjust the order of analysis, for example, taking into account the relevance between heart rate and stress level. The analysis unit can also adjust the order of analysis, for example, taking into account the relevance between step count and calorie consumption. The analysis unit can also prioritize analyzing highly relevant data based on, for example, step count data and calorie consumption data. The analysis unit can also prioritize analyzing highly relevant data according to the user's health condition. For example, if the user's health condition is deteriorating, the analysis unit prioritizes analyzing highly relevant data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0048] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, uses a questionnaire to evaluate the user's level of expertise. The analysis unit, for example, adjusts the use of technical terms based on the user's questionnaire results. The analysis unit can also adjust the use of technical terms based on the user's past feedback. The analysis unit, for example, adjusts the use of technical terms based on the user's past comments and ratings. The analysis unit can also adjust the use of technical terms based on the user's behavioral data. The analysis unit, for example, adjusts the use of technical terms based on the user's past behavioral data. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide analysis results that are easy to understand.

[0049] The ingredient analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between ingredients when analyzing ingredients. The ingredient analysis unit, for example, uses AI to evaluate the interrelationships between ingredients. The ingredient analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between nutrients in ingredients, for example. The ingredient analysis unit can also improve the accuracy of the analysis by taking into account the interrelationships between the flavors of ingredients. The ingredient analysis unit can, for example, suggest an optimal combination of ingredients based on the interrelationships between the flavors of ingredients. The ingredient analysis unit can also improve the accuracy of the analysis by taking into account the interrelationships between the cooking methods of ingredients. The ingredient analysis unit can, for example, suggest an optimal cooking method based on the interrelationships between the cooking methods of ingredients. In this way, the accuracy of the analysis is improved by taking into account the interrelationships between ingredients.

[0050] When analyzing ingredients, the ingredient analysis unit can take into account attribute information of the ingredient provider. The ingredient analysis unit, for example, uses AI to evaluate attribute information of the ingredient provider. For example, if the provider practices organic farming, the ingredient analysis unit can take that information into account when performing the analysis. Furthermore, if the provider is limited to a specific region, the ingredient analysis unit can also take that information into account when performing the analysis. For example, the ingredient analysis unit can take into account regional characteristics based on regional information about the provider when performing the analysis. Furthermore, if the provider meets specific quality standards, the ingredient analysis unit can also take that information into account when performing the analysis. For example, the ingredient analysis unit selects highly reliable ingredients based on quality standard information about the provider. This enables more reliable analysis by taking into account the attribute information of the provider.

[0051] When analyzing ingredients, the ingredient analysis unit can weight the analysis based on the consumption frequency of the ingredients. The ingredient analysis unit, for example, uses AI to evaluate the consumption frequency of ingredients. The ingredient analysis unit, for example, sets a high analysis weight for ingredients that the user consumes frequently. The ingredient analysis unit can also set a low analysis weight for ingredients that the user consumes less often. The ingredient analysis unit, for example, prioritizes the analysis of important ingredients based on the user's past consumption frequency data. The ingredient analysis unit can also adjust the analysis weight for seasonal ingredients. For example, the ingredient analysis unit prioritizes the analysis of seasonal ingredients. In this way, by weighting the analysis based on the consumption frequency, important ingredients can be prioritized in the analysis.

[0052] The ingredient analysis unit can take into account the geographical distribution of ingredients when analyzing ingredients. For example, the ingredient analysis unit uses AI to evaluate the geographical distribution of ingredients. For example, the ingredient analysis unit prioritizes the analysis of locally produced ingredients. The ingredient analysis unit can also analyze imported ingredients by taking into account transportation time and storage conditions. For example, the ingredient analysis unit evaluates the quality based on transportation time data for imported ingredients. The ingredient analysis unit can also analyze ingredients that are only available in a specific region by taking into account the characteristics of that region. For example, the ingredient analysis unit can propose the optimal way to use ingredients based on the characteristics of regional specialties. This makes it possible to perform analysis according to regional characteristics by taking geographical distribution into account.

[0053] When analyzing ingredients, the ingredient analysis unit can improve the accuracy of the analysis by referring to literature related to ingredients. The ingredient analysis unit, for example, uses AI to refer to literature related to ingredients. The ingredient analysis unit, for example, performs its analysis by referring to the latest research papers on the nutritional value of ingredients. The ingredient analysis unit can also perform its analysis by referring to literature on the health benefits of ingredients. The ingredient analysis unit, for example, selects optimal ingredients based on literature on the health benefits of ingredients. The ingredient analysis unit can also perform its analysis by referring to literature on cooking methods for ingredients. The ingredient analysis unit, for example, suggests optimal cooking methods based on literature on cooking methods for ingredients. In this way, by referring to related literature, the accuracy of the analysis is improved.

[0054] The ingredient analysis unit can take into account the market value of ingredients when analyzing ingredients. The ingredient analysis unit, for example, uses AI to evaluate the market value of ingredients. For expensive ingredients, the ingredient analysis unit, for example, performs analysis taking cost performance into account. For inexpensive ingredients, the ingredient analysis unit can also perform analysis emphasizing nutritional value and health benefits. For example, the ingredient analysis unit can suggest the optimal way to use inexpensive ingredients based on the nutritional value. The ingredient analysis unit can also suggest optimal ingredients taking into account fluctuations in market value. For example, the ingredient analysis unit selects ingredients with high cost performance based on data on fluctuations in market value. In this way, ingredients with high cost performance can be suggested by taking market value into account.

[0055] When creating a menu, the menu creation unit can adjust the level of detail of the menu based on the importance of the health data. The menu creation unit uses AI, for example, to evaluate the importance of the health data. The menu creation unit creates a menu that reflects data with high importance in detail, based on heart rate data, for example. The menu creation unit can also create a menu that simply reflects data with low importance, based on step count data. The menu creation unit creates a menu that takes into account the user's amount of exercise, based on step count data, for example. The menu creation unit can also create a menu that reflects data with high importance in detail, based on calorie consumption data. The menu creation unit creates a menu that takes into account the user's energy consumption, based on calorie consumption data, for example. In this way, by adjusting the level of detail of the menu based on the importance of the health data, it is possible to provide a menu that reflects important data.

[0056] When creating a menu, the menu creation unit can apply different menu creation algorithms depending on the category of ingredients. The menu creation unit uses AI, for example, to classify ingredient categories. For example, the menu creation unit applies a menu creation algorithm that emphasizes nutritional balance to the vegetable category. The menu creation unit can also apply a menu creation algorithm that emphasizes protein intake to the meat category. For example, the menu creation unit creates a menu that emphasizes protein intake based on ingredients in the meat category. The menu creation unit can also apply a menu creation algorithm that emphasizes omega-3 fatty acid intake to the seafood category. For example, the menu creation unit creates a menu that emphasizes omega-3 fatty acid intake based on ingredients in the seafood category. In this way, by applying an algorithm depending on the ingredient category, more appropriate menus can be created.

[0057] When creating a menu, the menu creation unit can improve the accuracy of the menu by referring to the user's past menu results. The menu creation unit, for example, uses AI to analyze the user's past menu results. The menu creation unit, for example, corrects the current menu based on the past menu results. The menu creation unit can also extract specific patterns from the past menu results and reflect them in the current menu. The menu creation unit, for example, creates a menu that takes into account the user's preferences and health condition based on the past menu results. The menu creation unit can also optimize the menu creation algorithm based on the past menu results. For example, the menu creation unit adjusts the parameters of the algorithm based on the past menu results. In this way, the accuracy of the menu is improved by referring to the past menu results.

[0058] When creating a menu, the menu creation unit can determine the priority of the menu based on when the ingredients were collected. The menu creation unit, for example, uses AI to evaluate when the ingredients were collected. The menu creation unit, for example, creates a menu that prioritizes the use of recently collected ingredients. The menu creation unit can also create a menu that prioritizes the use of ingredients that are close to their expiration date. The menu creation unit, for example, creates an optimal menu based on ingredients that are close to their expiration date. The menu creation unit can also create a menu that is suitable for important time periods based on the user's lifestyle. For example, the menu creation unit creates a menu that is suitable for time periods when the user is most active. In this way, by determining the priority of the menu based on when the ingredients were collected, it is possible to provide a menu that uses the most optimal ingredients.

[0059] When creating a menu, the menu creation unit can adjust the order of the menu based on the relevance of ingredients. The menu creation unit, for example, uses AI to evaluate the relevance of ingredients. The menu creation unit, for example, adjusts the order of the menu taking into account the interrelationships between nutrients. The menu creation unit can also adjust the order of the menu taking into account the interrelationships between flavors. The menu creation unit, for example, suggests an optimal combination of ingredients based on the interrelationships between flavors. The menu creation unit can also adjust the order of the menu taking into account the interrelationships between cooking methods. For example, the menu creation unit suggests an optimal cooking method based on the interrelationships between cooking methods. In this way, by adjusting the order of the menu based on the relevance of ingredients, a more balanced menu can be provided.

[0060] When creating a menu, the menu creation unit can adjust the use of technical terms in the menu according to the user's level of expertise. The menu creation unit, for example, uses a questionnaire to evaluate the user's level of expertise. The menu creation unit, for example, adjusts the use of technical terms based on the user's questionnaire results. The menu creation unit can also adjust the use of technical terms based on the user's past feedback. The menu creation unit, for example, adjusts the use of technical terms based on the user's past comments and ratings. The menu creation unit can also adjust the use of technical terms based on the user's behavioral data. The menu creation unit, for example, adjusts the use of technical terms based on the user's past behavioral data. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide a menu that is easy to understand.

[0061] When procuring materials, the material procurement department can analyze the user's past consumption behavior and select the optimal procurement method. The material procurement department, for example, uses AI to analyze the user's past consumption behavior. The material procurement department, for example, selects the most efficient procurement method based on past purchase history. The material procurement department can also select the optimal procurement method for a specific time period based on past consumption frequency. The material procurement department, for example, selects the optimal procurement timing based on past consumption frequency data. The material procurement department can also customize the procurement method based on past consumption behavior. For example, the material procurement department suggests a procurement method that suits the user's preferences based on past consumption behavior data. In this way, the optimal procurement method can be selected by analyzing past consumption behavior.

[0062] When procuring materials, the material procurement department can customize the procurement method based on the user's current living situation. For example, the material procurement department uses a questionnaire to evaluate the user's current living situation. For example, the material procurement department selects the optimal procurement method based on the user's questionnaire results. The material procurement department can also customize the procurement method based on the user's behavioral data. For example, the material procurement department suggests the optimal procurement method based on the user's past behavioral data. This allows for more efficient material procurement by customizing the procurement method based on the user's current living situation.

[0063] The material procurement department can improve the procurement method by reflecting user feedback when procuring materials. For example, the material procurement department uses text analysis technology to analyze user feedback. For example, the material procurement department improves the procurement method based on user comments and ratings. The material procurement department can also improve the procurement method based on user survey results. For example, the material procurement department analyzes user survey results and optimizes the procurement method. In this way, the procurement method can be optimized by reflecting feedback.

[0064] When procuring materials, the material procurement department can select the optimal procurement method by taking into account the user's geographical location information. The material procurement department, for example, uses GPS technology to grasp the user's geographical location information. The material procurement department, for example, uses a GPS device to record the user's location information in real time. The material procurement department can also grasp the geographical location information based on the user's address information. The material procurement department selects the optimal procurement method based on the user's registered address, for example. This allows the optimal procurement method to be selected by taking into account the geographical location information.

[0065] When procuring ingredients, the ingredient procurement department can analyze the user's social media activity and suggest procurement methods. The ingredient procurement department, for example, uses text analysis technology to analyze the user's social media activity. The ingredient procurement department, for example, analyzes the content of the user's social media posts and suggests related ingredients. The ingredient procurement department can also suggest related ingredients by referring to the activity of the user's friends on social media. For example, the ingredient procurement department analyzes the social media activity of the user's friends and suggests related ingredients. This allows related ingredients to be procured efficiently by analyzing social media activity.

[0066] When procuring materials, the material procurement department can customize the procurement method by reflecting the user's past feedback. The material procurement department, for example, uses text analysis technology to analyze the user's past feedback. The material procurement department customizes the procurement method based on the user's past comments and ratings, for example. The material procurement department can also customize the procurement method based on the user's past survey results. The material procurement department, for example, analyzes the user's survey results and optimizes the procurement method. In this way, the procurement method can be optimized by reflecting the past feedback.

[0067] When providing a recipe, the recipe providing unit can select the optimal providing method by referring to the user's past cooking history. The recipe providing unit, for example, uses AI to analyze the user's past cooking history. The recipe providing unit, for example, provides recipes that match the user's preferences and cooking skills based on the past cooking history. The recipe providing unit can also extract specific patterns from the past cooking history and select the optimal providing method. The recipe providing unit, for example, provides recipes that match the user's preferences and cooking skills based on the past cooking history. The recipe providing unit can also optimize the recipe providing algorithm based on the past cooking history. The recipe providing unit, for example, adjusts the parameters of the algorithm based on the past cooking history. In this way, the optimal recipe providing method can be selected by referring to the past cooking history.

[0068] When providing a recipe, the recipe providing unit can customize the content to be provided based on the user's current cooking skills. The recipe providing unit, for example, uses a questionnaire to evaluate the user's cooking skills. The recipe providing unit, for example, provides an optimal recipe based on the user's questionnaire results. The recipe providing unit can also evaluate the cooking skills based on the user's past cooking history and customize the content to be provided. The recipe providing unit, for example, provides a recipe tailored to the user's cooking skills based on the past cooking history. In this way, by customizing the content to be provided based on the user's current cooking skills, a more appropriate recipe can be provided.

[0069] When providing a recipe, the recipe providing unit can improve the providing method by reflecting user feedback. The recipe providing unit, for example, uses text analysis technology to analyze user feedback. The recipe providing unit improves the providing method based on, for example, user comments and ratings. The recipe providing unit can also improve the providing method based on user survey results. The recipe providing unit, for example, analyzes user survey results and optimizes the providing method. In this way, the providing method can be optimized by reflecting feedback.

[0070] When providing a recipe, the recipe providing unit can select the optimal providing method by taking into account the user's device information. For example, the recipe providing unit refers to device setting information to understand the user's device information. For example, the recipe providing unit provides a recipe that matches the screen size of the user's smartphone. The recipe providing unit can also provide a recipe optimized for the screen size of the user's tablet. For example, the recipe providing unit can provide a recipe optimized for the large screen of a tablet. The recipe providing unit can also provide a concise and highly visible recipe to the user's smartwatch. For example, the recipe providing unit can provide a recipe optimized for the small screen of a smartwatch. This makes it possible to select the optimal recipe providing method by taking into account the device information.

[0071] When providing a recipe, the recipe providing unit can make the provided content multilingual according to the user's language setting. The recipe providing unit, for example, references the language setting of the user's device. The recipe providing unit, for example, automatically sets the language of the recipe based on the language setting of the device. The recipe providing unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the recipe providing unit provides the recipe in that language. This allows for multilingual support according to the language setting, making it possible to accommodate a larger number of users.

[0072] When providing a recipe, the recipe providing unit can analyze the user's social media activity and suggest related recipes. The recipe providing unit, for example, uses text analysis technology to analyze the user's social media activity. The recipe providing unit, for example, analyzes the content of the user's social media posts and suggests related recipes. The recipe providing unit can also suggest related recipes by referring to the activity of the user's friends on social media. For example, the recipe providing unit analyzes the social media activity of the user's friends and suggests related recipes. This allows related recipes to be efficiently suggested by analyzing social media activity.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The health management support system may further include a sleep analysis unit that collects and analyzes the user's sleep data. The sleep analysis unit may record the user's sleep patterns using, for example, a wearable device. The sleep analysis unit may also evaluate the user's sleep quality and provide advice for improvement. Furthermore, the sleep analysis unit may suggest optimal exercise and meal timings based on the user's sleep data. This may support the user's comprehensive health management.

[0075] The analysis unit can take weather data into account when analyzing a user's health data. For example, high temperatures and humidity can affect the analysis results of the user's exercise volume and calorie consumption. It can also provide appropriate exercise and dietary advice to the user based on weather data. This allows for more accurate health management.

[0076] The ingredient analysis unit can analyze the user's dietary history and suggest ingredients based on past eating patterns. For example, it can suggest new menu items based on ingredients that the user has liked to eat in the past. The ingredient analysis unit can also suggest ways to improve nutritional balance based on the user's dietary history. This allows ingredients to be used in accordance with the user's preferences and health condition.

[0077] The ingredient procurement department can analyze a user's purchasing history and suggest optimal procurement methods based on past trends in ingredients. For example, it can automatically list ingredients that a user frequently purchases, making ordering at the online store easier. Also, if a user has a preference for a particular brand or store, it can suggest procurement methods taking that information into account. This allows for efficient ingredient procurement that matches the user's purchasing trends.

[0078] When collecting a user's health data, the collection unit can adjust the timing of data collection taking into account the user's lifestyle. For example, if the user is a nocturnal person, the collection unit can strengthen data collection at night. Also, if the user is a morning person, the collection unit can strengthen data collection in the morning. This makes it possible to collect data appropriately in line with the user's lifestyle.

[0079] The processing flow of the first embodiment will be briefly explained below.

[0080] Step 1: The collection unit collects health data such as heart rate, number of steps, and calories burned. For example, the collection unit measures heart rate using a wearable device. The collection unit can also count steps using a pedometer. The collection unit can also record the user's activity level to calculate calories burned. The collection unit also collects information about ingredients in the refrigerator. For example, the collection unit uses a camera in the refrigerator to recognize the type of ingredients, measure the amount of ingredients, and record the expiration date. Step 2: The analysis unit analyzes the data collected by the collection unit and understands the user's health condition. For example, the analysis unit uses AI to analyze heart rate fluctuations. The analysis unit can also evaluate the user's exercise volume based on step count data. The analysis unit can also evaluate the user's energy consumption based on calorie consumption data. Step 3: The ingredient analysis unit collects and analyzes information about ingredients in the refrigerator. For example, the ingredient analysis unit uses a camera inside the refrigerator to recognize the type of ingredient. The ingredient analysis unit can also measure the amount of ingredients. The ingredient analysis unit can also record the expiration date of the ingredients. Step 4: The menu creation unit creates a menu based on the data obtained by the analysis unit and the ingredient analysis unit. The menu creation unit, for example, uses AI to suggest menus that match the user's health-consciousness. The menu creation unit can also create menus taking into account the nutritional value of ingredients. The menu creation unit can also create menus taking into account the user's allergy information. Step 5: The ingredient procurement department procures the necessary ingredients based on the menu created by the menu creation department. For example, the ingredient procurement department orders missing ingredients from an online store. Alternatively, the ingredient procurement department can purchase ingredients from a local supermarket. Alternatively, the ingredient procurement department can arrange for the ingredients to be delivered. Step 6: The recipe provider provides instructions on how to prepare the menu created by the menu creator. The recipe provider displays the recipe on a smartphone or tablet, for example. The recipe provider can also provide easy-to-understand explanations of the cooking steps. The recipe provider can also provide advice in real time during cooking.

[0081] (Example 2) A health management support system according to an embodiment of the present invention utilizes AI to support health management, menu planning, and ingredient procurement. The system involves users wearing a wearable device to collect daily health data, such as heart rate, steps taken, and calories burned. This data is then analyzed by AI to determine the user's health status. Next, the refrigerator IoT collects information about the ingredients in the refrigerator, such as the type and quantity of ingredients in the refrigerator and their expiration dates. This data is then analyzed by AI to determine available ingredients. Based on this data, the AI ​​creates a menu tailored to the user's health preferences. For example, if the user is on a diet, low-calorie, nutritionally balanced menus are suggested. If the user has specific allergies, menus that do not contain the allergens are suggested. Furthermore, the AI ​​also assists in procuring the necessary ingredients based on the suggested menu. For example, if an ingredient is missing from the refrigerator, the AI ​​can automatically order it from an online store. Finally, the AI ​​teaches the user how to prepare the suggested menu. For example, it displays recipes on a smartphone or tablet, clearly explaining the cooking steps. It can also provide real-time advice during cooking. This allows the health management support system to enable users to easily prepare delicious meals while managing their health. It also eliminates the need to procure ingredients, making it extremely convenient for busy modern people. This allows the health management support system to grasp the user's health condition, provide optimal menus, and support ingredient procurement and recipe provision. For example, it allows users to easily prepare delicious meals while managing their health, eliminating the need to procure ingredients, making it extremely convenient for busy modern people.

[0082] A health management support system according to an embodiment includes a collection unit, an analysis unit, an ingredient analysis unit, a menu creation unit, an ingredient procurement unit, and a recipe provision unit. The collection unit collects health data such as heart rate, step count, and calories burned. The collection unit measures heart rate, for example, using a wearable device. The collection unit can also count steps using a pedometer. The collection unit can also record the user's activity level to calculate calories burned. The analysis unit analyzes the data collected by the collection unit to understand the user's health condition. The analysis unit can analyze heart rate fluctuations, for example, using AI. The analysis unit can also evaluate the user's exercise level based on step count data. The analysis unit can also evaluate the user's energy consumption based on calorie consumption data. The ingredient analysis unit collects and analyzes information about ingredients in a refrigerator. The ingredient analysis unit can recognize the types of ingredients using, for example, a camera in the refrigerator. The ingredient analysis unit can also measure the amount of ingredients. The ingredient analysis unit can also record the expiration dates of ingredients. The menu creation unit creates a menu based on the data obtained by the analysis unit and the ingredient analysis unit. The menu creation unit, for example, uses AI to suggest a menu tailored to the user's health preferences. The menu creation unit can also create a menu taking into account the nutritional value of ingredients. The menu creation unit can also create a menu taking into account the user's allergy information. The ingredient procurement unit procures necessary ingredients based on the menu created by the menu creation unit. The ingredient procurement unit, for example, orders missing ingredients from an online store. The ingredient procurement unit can also purchase ingredients at a local supermarket. The ingredient procurement unit can also arrange for ingredient delivery. The recipe providing unit provides instructions for making the menu created by the menu creation unit. The recipe providing unit, for example, displays the recipe on a smartphone or tablet. The recipe providing unit can also explain cooking procedures in an easy-to-understand manner. The recipe providing unit can also provide advice in real time during cooking. As a result, the health management support system according to the embodiment can grasp the user's health condition, provide optimal menus, and support ingredient procurement and recipe provision.

[0083] The collection unit can collect health data such as heart rate, number of steps, and calories burned. The collection unit measures the heart rate using, for example, a wearable device. The collection unit records the user's heart rate in real time using, for example, a heart rate sensor. The collection unit can also count the number of steps using a pedometer. The collection unit accurately counts the number of steps taken by the user using, for example, an acceleration sensor. The collection unit can also record the user's activity level in order to calculate calories burned. The collection unit calculates the user's calories burned using, for example, an activity meter. By collecting health data in this way, the user's health condition can be understood.

[0084] The analysis unit analyzes the collected health data and can grasp the user's health condition. The analysis unit, for example, uses AI to analyze heart rate fluctuations. The analysis unit, for example, evaluates the user's stress level based on the heart rate data. The analysis unit can also evaluate the user's exercise amount based on the step count data. The analysis unit, for example, analyzes the user's exercise habits based on the step count data. The analysis unit can also evaluate the user's energy consumption based on the calorie consumption data. The analysis unit, for example, evaluates the effectiveness of the user's diet based on the calorie consumption data. In this way, by analyzing the health data, the user's health condition can be grasped in detail.

[0085] The collection unit can collect information about ingredients in the refrigerator. For example, the collection unit recognizes the type of ingredients using a camera in the refrigerator. For example, the collection unit automatically identifies ingredients in the refrigerator using image recognition technology. The collection unit can also measure the amount of ingredients. For example, the collection unit can accurately measure the amount of ingredients in the refrigerator using a weight sensor. The collection unit can also record the expiration dates of ingredients. For example, the collection unit can automatically record the expiration dates of ingredients using a barcode scanner. In this way, by collecting information about ingredients in the refrigerator, it is possible to know which ingredients are available.

[0086] The ingredient analysis unit analyzes the collected ingredient information and can identify available ingredients. The ingredient analysis unit analyzes the collected ingredient information using, for example, image recognition technology. The ingredient analysis unit identifies the type of ingredient based on, for example, image data of the inside of the refrigerator. The ingredient analysis unit can also evaluate the nutritional value of the ingredient. The ingredient analysis unit evaluates the nutritional value of the ingredient based on, for example, nutritional component data of the ingredient. The ingredient analysis unit can also evaluate the expiration date of the ingredient. The ingredient analysis unit identifies available ingredients based on, for example, the expiration date data of the ingredient. In this way, by analyzing the ingredient information, available ingredients can be identified in detail.

[0087] The menu creation unit can create a menu tailored to the user's health preferences. The menu creation unit, for example, uses AI to suggest menus tailored to the user's health preferences. The menu creation unit, for example, creates a low-calorie, nutritionally balanced menu based on the user's health data. The menu creation unit can also create a menu taking into account the nutritional value of ingredients. The menu creation unit, for example, creates a nutritionally balanced menu based on nutritional component data of ingredients. The menu creation unit can also create a menu taking into account the user's allergy information. For example, the menu creation unit creates a menu that does not contain allergens based on the user's allergen information. This supports health management by providing a menu tailored to the user's health preferences.

[0088] The material procurement department can order necessary ingredients from an online store. For example, the material procurement department orders ingredients that are in short supply from an online store. For example, the material procurement department uses AI to check inventory information from the online store and automatically order the necessary ingredients. The material procurement department can also purchase ingredients from a local supermarket. For example, the material procurement department purchases the necessary ingredients from the nearest supermarket based on the user's location information. The material procurement department can also arrange for the delivery of ingredients. For example, the material procurement department arranges for ingredients ordered from an online store to be delivered to the user's home. This automatically procures the necessary ingredients, eliminating the hassle of procuring ingredients.

[0089] The recipe providing unit can display recipes on a smartphone or tablet and explain cooking steps in an easy-to-understand manner. The recipe providing unit displays recipes on a smartphone or tablet, for example. The recipe providing unit displays recipes optimized for the user's device, for example, using AI. The recipe providing unit can also explain cooking steps in an easy-to-understand manner. The recipe providing unit can, for example, display cooking steps step by step, allowing the user to cook easily. The recipe providing unit can also provide advice in real time during cooking. The recipe providing unit can, for example, use AI to provide advice in real time according to the user's cooking status. In this way, by providing recipes in an easy-to-understand manner, the user can cook easily.

[0090] The collection unit can estimate the user's emotions and adjust the timing of collecting health data based on the estimated user's emotions. The collection unit, for example, uses facial expression recognition technology to estimate the user's emotions. The collection unit, for example, uses a camera to analyze the user's facial expressions and estimate the emotions. The collection unit can also estimate the user's emotions using voice analysis technology. The collection unit, for example, uses a microphone to analyze the tone and speed of the user's voice and estimate the emotions. The collection unit can also estimate the user's emotions using text analysis technology. The collection unit, for example, analyzes text data entered by the user and estimates the emotions. This allows for more appropriate data collection by adjusting the collection timing according to the user's emotions.

[0091] The collection unit can analyze the user's past health data and select the optimal collection method. The collection unit, for example, uses AI to analyze the user's past health data. The collection unit, for example, identifies the optimal collection timing based on past heart rate data. The collection unit can also select the optimal collection method based on past step count data. The collection unit, for example, optimizes the pedometer settings based on the past step count data. The collection unit can also select the optimal collection method based on past calorie consumption data. The collection unit, for example, optimizes the activity meter settings based on past calorie consumption data. In this way, the optimal collection method can be selected by analyzing past data.

[0092] When collecting health data, the collection unit can filter the data based on the user's current activity status and lifestyle rhythm. The collection unit, for example, uses an activity meter to grasp the user's current activity status. The collection unit, for example, uses the activity meter to record the user's exercise amount in real time. The collection unit can also use a sleep tracker to grasp the user's lifestyle rhythm. The collection unit, for example, uses the sleep tracker to record the user's sleep patterns. This allows for more accurate data collection by filtering the data based on the activity status and lifestyle rhythm.

[0093] When collecting health data, the collection unit can select the optimal collection means according to the user's input method. The collection unit, for example, uses voice recognition technology to identify the user's input method. The collection unit, for example, uses a microphone to analyze the user's voice input and selects the optimal collection means. The collection unit can also identify the user's input method using text analysis technology. The collection unit, for example, analyzes text data entered by the user and selects the optimal collection means. The collection unit can also identify the user's input method using image recognition technology. The collection unit, for example, uses a camera to analyze the user's image input and selects the optimal collection means. This improves the efficiency of data collection by selecting the collection means according to the user's input method.

[0094] The collection unit can estimate the user's emotions and determine the priority of health data to be collected based on the estimated user's emotions. The collection unit, for example, uses facial expression recognition technology to estimate the user's emotions. The collection unit, for example, uses a camera to analyze the user's facial expressions and estimate the emotions. The collection unit can also estimate the user's emotions using voice analysis technology. The collection unit, for example, uses a microphone to analyze the tone and speed of the user's voice and estimate the emotions. The collection unit can also estimate the user's emotions using text analysis technology. The collection unit, for example, analyzes text data entered by the user and estimates the emotions. This allows important data to be collected preferentially by determining the priority of data according to the user's emotions.

[0095] When collecting health data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit, for example, uses GPS technology to grasp the user's geographical location information. The collection unit, for example, uses a GPS device to record the user's location information in real time. The collection unit can also grasp the geographical location information based on the user's address information. The collection unit, for example, prioritizes collecting highly relevant data based on the user's registered address. In this way, highly relevant data can be prioritized by taking into account the geographical location information.

[0096] When collecting health data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit uses text analysis technology to analyze the user's social media activities. For example, the collection unit analyzes the content of the user's social media posts and collects related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the collection unit analyzes the social media activities of the user's friends and collects related data. In this way, related data can be efficiently collected by analyzing social media activities.

[0097] When collecting health data, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit, for example, uses text analysis technology to analyze the user's past feedback. The collection unit, for example, analyzes the user's past comments and ratings and customizes the collection method. The collection unit can also customize the collection method based on the user's past survey results. The collection unit, for example, analyzes the user's survey results and optimizes the collection method. In this way, the collection method can be optimized by reflecting the past feedback.

[0098] The analysis unit can estimate the user's emotion and adjust the method of expressing the analysis based on the estimated user's emotion. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotion. The analysis unit, for example, uses a camera to analyze the user's facial expression and estimate the emotion. The analysis unit can also estimate the user's emotion using voice analysis technology. The analysis unit, for example, uses a microphone to analyze the tone and speed of the user's voice and estimate the emotion. The analysis unit can also estimate the user's emotion using text analysis technology. The analysis unit, for example, analyzes text data input by the user and estimates the emotion. In this way, by adjusting the method of expressing the analysis according to the user's emotion, it is possible to provide analysis results that are easier to understand.

[0099] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the health data. The analysis unit uses AI, for example, to evaluate the importance of the health data. The analysis unit performs a detailed analysis of data with high importance based on, for example, heart rate data. The analysis unit can also simply analyze data with low importance based on step count data. The analysis unit simply evaluates the user's amount of exercise based on, for example, step count data. The analysis unit can also analyze data with high importance in detail based on calorie consumption data. The analysis unit evaluates the user's energy consumption in detail based on, for example, calorie consumption data. In this way, important data can be analyzed in detail by adjusting the level of detail of the analysis based on the importance of the health data.

[0100] During analysis, the analysis unit can apply different analysis algorithms depending on the category of health data. For example, the analysis unit uses AI to classify the categories of health data. For example, the analysis unit applies a heart rate variability analysis algorithm to heart rate data. The analysis unit can also apply a walking pattern analysis algorithm to step count data. For example, the analysis unit analyzes the user's walking pattern based on the step count data. The analysis unit can also apply an energy consumption analysis algorithm to calorie consumption data. For example, the analysis unit analyzes the user's energy consumption based on the calorie consumption data. This allows for more accurate analysis by applying an analysis algorithm according to the category.

[0101] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, uses AI to analyze the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on past heart rate analysis results. The analysis unit can also correct the current analysis result based on past step count analysis results. The analysis unit, for example, corrects the current step count data based on past step count analysis results. The analysis unit can also correct the current analysis result based on past calorie consumption analysis results. The analysis unit, for example, corrects the current calorie consumption data based on past calorie consumption analysis results. In this way, the accuracy of the analysis is improved by referring to past analysis results.

[0102] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotion. The analysis unit, for example, uses a camera to analyze the user's facial expression and estimate the emotion. The analysis unit can also estimate the user's emotion using voice analysis technology. The analysis unit, for example, uses a microphone to analyze the tone and speed of the user's voice and estimate the emotion. The analysis unit can also estimate the user's emotion using text analysis technology. The analysis unit, for example, analyzes text data input by the user and estimates the emotion. This allows the length of the analysis to be adjusted according to the user's emotion, making it possible to provide more appropriate analysis results.

[0103] During analysis, the analysis unit can determine the priority of analysis based on when the health data was collected. The analysis unit, for example, uses AI to evaluate when the health data was collected. The analysis unit, for example, prioritizes analysis of recently collected data. The analysis unit can also prioritize analysis of data collected during a specific time period. For example, the analysis unit prioritizes analysis of data collected at night. The analysis unit can also prioritize analysis of data from important time periods based on the user's lifestyle rhythm. For example, the analysis unit prioritizes analysis of data from time periods when the user is most active. In this way, by determining the priority of analysis based on when the health data was collected, important data can be analyzed preferentially.

[0104] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the health data. The analysis unit, for example, uses AI to evaluate the relevance of the health data. The analysis unit can adjust the order of analysis, for example, taking into account the relevance between heart rate and stress level. The analysis unit can also adjust the order of analysis, for example, taking into account the relevance between step count and calorie consumption. The analysis unit can also prioritize analyzing highly relevant data based on, for example, step count data and calorie consumption data. The analysis unit can also prioritize analyzing highly relevant data according to the user's health condition. For example, if the user's health condition is deteriorating, the analysis unit prioritizes analyzing highly relevant data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data.

[0105] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, uses a questionnaire to evaluate the user's level of expertise. The analysis unit, for example, adjusts the use of technical terms based on the user's questionnaire results. The analysis unit can also adjust the use of technical terms based on the user's past feedback. The analysis unit, for example, adjusts the use of technical terms based on the user's past comments and ratings. The analysis unit can also adjust the use of technical terms based on the user's behavioral data. The analysis unit, for example, adjusts the use of technical terms based on the user's past behavioral data. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide analysis results that are easy to understand.

[0106] The ingredient analysis unit can estimate the user's emotions and adjust the criteria for ingredient analysis based on the estimated user emotions. The ingredient analysis unit, for example, uses facial expression recognition technology to estimate the user's emotions. The ingredient analysis unit, for example, uses a camera to analyze the user's facial expressions and estimate emotions. The ingredient analysis unit can also estimate the user's emotions using voice analysis technology. The ingredient analysis unit, for example, uses a microphone to analyze the tone and speed of the user's voice and estimate emotions. The ingredient analysis unit can also estimate the user's emotions using text analysis technology. The ingredient analysis unit, for example, analyzes text data entered by the user and estimates emotions. This allows for more appropriate ingredient analysis by adjusting the criteria for ingredient analysis according to the user's emotions.

[0107] The ingredient analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between ingredients when analyzing ingredients. The ingredient analysis unit, for example, uses AI to evaluate the interrelationships between ingredients. The ingredient analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between nutrients in ingredients, for example. The ingredient analysis unit can also improve the accuracy of the analysis by taking into account the interrelationships between the flavors of ingredients. The ingredient analysis unit can, for example, suggest an optimal combination of ingredients based on the interrelationships between the flavors of ingredients. The ingredient analysis unit can also improve the accuracy of the analysis by taking into account the interrelationships between the cooking methods of ingredients. The ingredient analysis unit can, for example, suggest an optimal cooking method based on the interrelationships between the cooking methods of ingredients. In this way, the accuracy of the analysis is improved by taking into account the interrelationships between ingredients.

[0108] When analyzing ingredients, the ingredient analysis unit can take into account attribute information of the ingredient provider. The ingredient analysis unit, for example, uses AI to evaluate attribute information of the ingredient provider. For example, if the provider practices organic farming, the ingredient analysis unit can take that information into account when performing the analysis. Furthermore, if the provider is limited to a specific region, the ingredient analysis unit can also take that information into account when performing the analysis. For example, the ingredient analysis unit can take into account regional characteristics based on regional information about the provider when performing the analysis. Furthermore, if the provider meets specific quality standards, the ingredient analysis unit can also take that information into account when performing the analysis. For example, the ingredient analysis unit selects highly reliable ingredients based on quality standard information about the provider. This enables more reliable analysis by taking into account the attribute information of the provider.

[0109] When analyzing ingredients, the ingredient analysis unit can weight the analysis based on the consumption frequency of the ingredients. The ingredient analysis unit, for example, uses AI to evaluate the consumption frequency of ingredients. The ingredient analysis unit, for example, sets a high analysis weight for ingredients that the user consumes frequently. The ingredient analysis unit can also set a low analysis weight for ingredients that the user consumes less often. The ingredient analysis unit, for example, prioritizes the analysis of important ingredients based on the user's past consumption frequency data. The ingredient analysis unit can also adjust the analysis weight for seasonal ingredients. For example, the ingredient analysis unit prioritizes the analysis of seasonal ingredients. In this way, by weighting the analysis based on the consumption frequency, important ingredients can be prioritized in the analysis.

[0110] The ingredient analysis unit can estimate the user's emotions and adjust the order in which the ingredient analysis results are displayed based on the estimated user's emotions. The ingredient analysis unit, for example, uses facial expression recognition technology to estimate the user's emotions. The ingredient analysis unit, for example, uses a camera to analyze the user's facial expressions and estimate the emotions. The ingredient analysis unit can also estimate the user's emotions using voice analysis technology. The ingredient analysis unit, for example, uses a microphone to analyze the tone and speed of the user's voice and estimate the emotions. The ingredient analysis unit can also estimate the user's emotions using text analysis technology. The ingredient analysis unit, for example, analyzes text data entered by the user and estimates the emotions. This allows the display order to be adjusted according to the user's emotions, making it possible to provide more appropriate ingredient information.

[0111] The ingredient analysis unit can take into account the geographical distribution of ingredients when analyzing ingredients. For example, the ingredient analysis unit uses AI to evaluate the geographical distribution of ingredients. For example, the ingredient analysis unit prioritizes the analysis of locally produced ingredients. The ingredient analysis unit can also analyze imported ingredients by taking into account transportation time and storage conditions. For example, the ingredient analysis unit evaluates the quality based on transportation time data for imported ingredients. The ingredient analysis unit can also analyze ingredients that are only available in a specific region by taking into account the characteristics of that region. For example, the ingredient analysis unit can propose the optimal way to use ingredients based on the characteristics of regional specialties. This makes it possible to perform analysis according to regional characteristics by taking geographical distribution into account.

[0112] When analyzing ingredients, the ingredient analysis unit can improve the accuracy of the analysis by referring to literature related to ingredients. The ingredient analysis unit, for example, uses AI to refer to literature related to ingredients. The ingredient analysis unit, for example, performs its analysis by referring to the latest research papers on the nutritional value of ingredients. The ingredient analysis unit can also perform its analysis by referring to literature on the health benefits of ingredients. The ingredient analysis unit, for example, selects optimal ingredients based on literature on the health benefits of ingredients. The ingredient analysis unit can also perform its analysis by referring to literature on cooking methods for ingredients. The ingredient analysis unit, for example, suggests optimal cooking methods based on literature on cooking methods for ingredients. In this way, by referring to related literature, the accuracy of the analysis is improved.

[0113] The ingredient analysis unit can take into account the market value of ingredients when analyzing ingredients. The ingredient analysis unit, for example, uses AI to evaluate the market value of ingredients. For expensive ingredients, the ingredient analysis unit, for example, performs analysis taking cost performance into account. For inexpensive ingredients, the ingredient analysis unit can also perform analysis emphasizing nutritional value and health benefits. For example, the ingredient analysis unit can suggest the optimal way to use inexpensive ingredients based on the nutritional value. The ingredient analysis unit can also suggest optimal ingredients taking into account fluctuations in market value. For example, the ingredient analysis unit selects ingredients with high cost performance based on data on fluctuations in market value. In this way, ingredients with high cost performance can be suggested by taking market value into account.

[0114] The menu creation unit can estimate the user's emotion and adjust the way the menu is presented based on the estimated user's emotion. The menu creation unit, for example, uses facial expression recognition technology to estimate the user's emotion. The menu creation unit, for example, uses a camera to analyze the user's facial expression and estimate the emotion. The menu creation unit can also estimate the user's emotion using voice analysis technology. The menu creation unit, for example, uses a microphone to analyze the tone and speed of the user's voice and estimate the emotion. The menu creation unit can also estimate the user's emotion using text analysis technology. The menu creation unit, for example, analyzes text data entered by the user and estimates the emotion. This makes it possible to provide a menu that is easier to understand by adjusting the way the menu is presented based on the user's emotion.

[0115] When creating a menu, the menu creation unit can adjust the level of detail of the menu based on the importance of the health data. The menu creation unit uses AI, for example, to evaluate the importance of the health data. The menu creation unit creates a menu that reflects data with high importance in detail, based on heart rate data, for example. The menu creation unit can also create a menu that simply reflects data with low importance, based on step count data. The menu creation unit creates a menu that takes into account the user's amount of exercise, based on step count data, for example. The menu creation unit can also create a menu that reflects data with high importance in detail, based on calorie consumption data. The menu creation unit creates a menu that takes into account the user's energy consumption, based on calorie consumption data, for example. In this way, by adjusting the level of detail of the menu based on the importance of the health data, it is possible to provide a menu that reflects important data.

[0116] When creating a menu, the menu creation unit can apply different menu creation algorithms depending on the category of ingredients. The menu creation unit uses AI, for example, to classify ingredient categories. For example, the menu creation unit applies a menu creation algorithm that emphasizes nutritional balance to the vegetable category. The menu creation unit can also apply a menu creation algorithm that emphasizes protein intake to the meat category. For example, the menu creation unit creates a menu that emphasizes protein intake based on ingredients in the meat category. The menu creation unit can also apply a menu creation algorithm that emphasizes omega-3 fatty acid intake to the seafood category. For example, the menu creation unit creates a menu that emphasizes omega-3 fatty acid intake based on ingredients in the seafood category. In this way, by applying an algorithm depending on the ingredient category, more appropriate menus can be created.

[0117] When creating a menu, the menu creation unit can improve the accuracy of the menu by referring to the user's past menu results. The menu creation unit, for example, uses AI to analyze the user's past menu results. The menu creation unit, for example, corrects the current menu based on the past menu results. The menu creation unit can also extract specific patterns from the past menu results and reflect them in the current menu. The menu creation unit, for example, creates a menu that takes into account the user's preferences and health condition based on the past menu results. The menu creation unit can also optimize the menu creation algorithm based on the past menu results. For example, the menu creation unit adjusts the parameters of the algorithm based on the past menu results. In this way, the accuracy of the menu is improved by referring to the past menu results.

[0118] The menu creation unit can estimate the user's emotion and adjust the length of the menu based on the estimated user's emotion. The menu creation unit, for example, uses facial expression recognition technology to estimate the user's emotion. The menu creation unit, for example, uses a camera to analyze the user's facial expression and estimate the emotion. The menu creation unit can also estimate the user's emotion using voice analysis technology. The menu creation unit, for example, uses a microphone to analyze the tone and speed of the user's voice and estimate the emotion. The menu creation unit can also estimate the user's emotion using text analysis technology. The menu creation unit, for example, analyzes text data input by the user and estimates the emotion. This allows the length of the menu to be adjusted according to the user's emotion, thereby providing a more appropriate menu.

[0119] When creating a menu, the menu creation unit can determine the priority of the menu based on when the ingredients were collected. The menu creation unit, for example, uses AI to evaluate when the ingredients were collected. The menu creation unit, for example, creates a menu that prioritizes the use of recently collected ingredients. The menu creation unit can also create a menu that prioritizes the use of ingredients that are close to their expiration date. The menu creation unit, for example, creates an optimal menu based on ingredients that are close to their expiration date. The menu creation unit can also create a menu that is suitable for important time periods based on the user's lifestyle. For example, the menu creation unit creates a menu that is suitable for time periods when the user is most active. In this way, by determining the priority of the menu based on when the ingredients were collected, it is possible to provide a menu that uses the most optimal ingredients.

[0120] When creating a menu, the menu creation unit can adjust the order of the menu based on the relevance of ingredients. The menu creation unit, for example, uses AI to evaluate the relevance of ingredients. The menu creation unit, for example, adjusts the order of the menu taking into account the interrelationships between nutrients. The menu creation unit can also adjust the order of the menu taking into account the interrelationships between flavors. The menu creation unit, for example, suggests an optimal combination of ingredients based on the interrelationships between flavors. The menu creation unit can also adjust the order of the menu taking into account the interrelationships between cooking methods. For example, the menu creation unit suggests an optimal cooking method based on the interrelationships between cooking methods. In this way, by adjusting the order of the menu based on the relevance of ingredients, a more balanced menu can be provided.

[0121] When creating a menu, the menu creation unit can adjust the use of technical terms in the menu according to the user's level of expertise. The menu creation unit, for example, uses a questionnaire to evaluate the user's level of expertise. The menu creation unit, for example, adjusts the use of technical terms based on the user's questionnaire results. The menu creation unit can also adjust the use of technical terms based on the user's past feedback. The menu creation unit, for example, adjusts the use of technical terms based on the user's past comments and ratings. The menu creation unit can also adjust the use of technical terms based on the user's behavioral data. The menu creation unit, for example, adjusts the use of technical terms based on the user's past behavioral data. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide a menu that is easy to understand.

[0122] The material procurement unit can estimate the user's emotions and adjust the material procurement method based on the estimated user's emotions. The material procurement unit, for example, uses facial expression recognition technology to estimate the user's emotions. The material procurement unit, for example, uses a camera to analyze the user's facial expressions and estimate the emotions. The material procurement unit can also estimate the user's emotions using voice analysis technology. The material procurement unit, for example, uses a microphone to analyze the tone and speed of the user's voice and estimate the emotions. The material procurement unit can also estimate the user's emotions using text analysis technology. The material procurement unit, for example, analyzes text data input by the user and estimates the emotions. This allows for more appropriate material procurement by adjusting the material procurement method according to the user's emotions.

[0123] When procuring materials, the material procurement department can analyze the user's past consumption behavior and select the optimal procurement method. The material procurement department, for example, uses AI to analyze the user's past consumption behavior. The material procurement department, for example, selects the most efficient procurement method based on past purchase history. The material procurement department can also select the optimal procurement method for a specific time period based on past consumption frequency. The material procurement department, for example, selects the optimal procurement timing based on past consumption frequency data. The material procurement department can also customize the procurement method based on past consumption behavior. For example, the material procurement department suggests a procurement method that suits the user's preferences based on past consumption behavior data. In this way, the optimal procurement method can be selected by analyzing past consumption behavior.

[0124] When procuring materials, the material procurement department can customize the procurement method based on the user's current living situation. For example, the material procurement department uses a questionnaire to evaluate the user's current living situation. For example, the material procurement department selects the optimal procurement method based on the user's questionnaire results. The material procurement department can also customize the procurement method based on the user's behavioral data. For example, the material procurement department suggests the optimal procurement method based on the user's past behavioral data. This allows for more efficient material procurement by customizing the procurement method based on the user's current living situation.

[0125] The material procurement department can improve the procurement method by reflecting user feedback when procuring materials. For example, the material procurement department uses text analysis technology to analyze user feedback. For example, the material procurement department improves the procurement method based on user comments and ratings. The material procurement department can also improve the procurement method based on user survey results. For example, the material procurement department analyzes user survey results and optimizes the procurement method. In this way, the procurement method can be optimized by reflecting feedback.

[0126] The material procurement unit can estimate the user's emotions and determine the priority of material procurement based on the estimated user's emotions. The material procurement unit, for example, uses facial expression recognition technology to estimate the user's emotions. The material procurement unit, for example, uses a camera to analyze the user's facial expressions and estimate the emotions. The material procurement unit can also estimate the user's emotions using voice analysis technology. The material procurement unit, for example, uses a microphone to analyze the tone and speed of the user's voice and estimate the emotions. The material procurement unit can also estimate the user's emotions using text analysis technology. The material procurement unit, for example, analyzes text data input by the user and estimates the emotions. This allows important materials to be procured preferentially by determining the priority of material procurement according to the user's emotions.

[0127] When procuring materials, the material procurement department can select the optimal procurement method by taking into account the user's geographical location information. The material procurement department, for example, uses GPS technology to grasp the user's geographical location information. The material procurement department, for example, uses a GPS device to record the user's location information in real time. The material procurement department can also grasp the geographical location information based on the user's address information. The material procurement department selects the optimal procurement method based on the user's registered address, for example. This allows the optimal procurement method to be selected by taking into account the geographical location information.

[0128] When procuring ingredients, the ingredient procurement department can analyze the user's social media activity and suggest procurement methods. The ingredient procurement department, for example, uses text analysis technology to analyze the user's social media activity. The ingredient procurement department, for example, analyzes the content of the user's social media posts and suggests related ingredients. The ingredient procurement department can also suggest related ingredients by referring to the activity of the user's friends on social media. For example, the ingredient procurement department analyzes the social media activity of the user's friends and suggests related ingredients. This allows related ingredients to be procured efficiently by analyzing social media activity.

[0129] When procuring materials, the material procurement department can customize the procurement method by reflecting the user's past feedback. The material procurement department, for example, uses text analysis technology to analyze the user's past feedback. The material procurement department customizes the procurement method based on the user's past comments and ratings, for example. The material procurement department can also customize the procurement method based on the user's past survey results. The material procurement department, for example, analyzes the user's survey results and optimizes the procurement method. In this way, the procurement method can be optimized by reflecting the past feedback.

[0130] The recipe providing unit can estimate the user's emotions and adjust the recipe provision method based on the estimated user's emotions. The recipe providing unit, for example, uses facial expression recognition technology to estimate the user's emotions. The recipe providing unit, for example, uses a camera to analyze the user's facial expressions and estimate the emotions. The recipe providing unit can also estimate the user's emotions using voice analysis technology. The recipe providing unit, for example, uses a microphone to analyze the tone and speed of the user's voice and estimate the emotions. The recipe providing unit can also estimate the user's emotions using text analysis technology. The recipe providing unit, for example, analyzes text data entered by the user and estimates the emotions. This allows the recipe provision method to be adjusted according to the user's emotions, thereby providing a more appropriate recipe.

[0131] When providing a recipe, the recipe providing unit can select the optimal providing method by referring to the user's past cooking history. The recipe providing unit, for example, uses AI to analyze the user's past cooking history. The recipe providing unit, for example, provides recipes that match the user's preferences and cooking skills based on the past cooking history. The recipe providing unit can also extract specific patterns from the past cooking history and select the optimal providing method. The recipe providing unit, for example, provides recipes that match the user's preferences and cooking skills based on the past cooking history. The recipe providing unit can also optimize the recipe providing algorithm based on the past cooking history. The recipe providing unit, for example, adjusts the parameters of the algorithm based on the past cooking history. In this way, the optimal recipe providing method can be selected by referring to the past cooking history.

[0132] When providing a recipe, the recipe providing unit can customize the content to be provided based on the user's current cooking skills. The recipe providing unit, for example, uses a questionnaire to evaluate the user's cooking skills. The recipe providing unit, for example, provides an optimal recipe based on the user's questionnaire results. The recipe providing unit can also evaluate the cooking skills based on the user's past cooking history and customize the content to be provided. The recipe providing unit, for example, provides a recipe tailored to the user's cooking skills based on the past cooking history. In this way, by customizing the content to be provided based on the user's current cooking skills, a more appropriate recipe can be provided.

[0133] When providing a recipe, the recipe providing unit can improve the providing method by reflecting user feedback. The recipe providing unit, for example, uses text analysis technology to analyze user feedback. The recipe providing unit improves the providing method based on, for example, user comments and ratings. The recipe providing unit can also improve the providing method based on user survey results. The recipe providing unit, for example, analyzes user survey results and optimizes the providing method. In this way, the providing method can be optimized by reflecting feedback.

[0134] The recipe providing unit can estimate the user's emotions and determine the priority of recipes based on the estimated user's emotions. The recipe providing unit, for example, uses facial expression recognition technology to estimate the user's emotions. The recipe providing unit, for example, uses a camera to analyze the user's facial expressions and estimate the emotions. The recipe providing unit can also estimate the user's emotions using voice analysis technology. The recipe providing unit, for example, uses a microphone to analyze the tone and speed of the user's voice and estimate the emotions. The recipe providing unit can also estimate the user's emotions using text analysis technology. The recipe providing unit, for example, analyzes text data entered by the user and estimates the emotions. This allows important recipes to be provided preferentially by determining the priority of recipes according to the user's emotions.

[0135] When providing a recipe, the recipe providing unit can select the optimal providing method by taking into account the user's device information. For example, the recipe providing unit refers to device setting information to understand the user's device information. For example, the recipe providing unit provides a recipe that matches the screen size of the user's smartphone. The recipe providing unit can also provide a recipe optimized for the screen size of the user's tablet. For example, the recipe providing unit can provide a recipe optimized for the large screen of a tablet. The recipe providing unit can also provide a concise and highly visible recipe to the user's smartwatch. For example, the recipe providing unit can provide a recipe optimized for the small screen of a smartwatch. This makes it possible to select the optimal recipe providing method by taking into account the device information.

[0136] When providing a recipe, the recipe providing unit can make the provided content multilingual according to the user's language setting. The recipe providing unit, for example, references the language setting of the user's device. The recipe providing unit, for example, automatically sets the language of the recipe based on the language setting of the device. The recipe providing unit can also provide a language switching function when the user uses multiple languages. For example, when the user selects a specific language, the recipe providing unit provides the recipe in that language. This allows for multilingual support according to the language setting, making it possible to accommodate a larger number of users.

[0137] When providing a recipe, the recipe providing unit can analyze the user's social media activity and suggest related recipes. The recipe providing unit, for example, uses text analysis technology to analyze the user's social media activity. The recipe providing unit, for example, analyzes the content of the user's social media posts and suggests related recipes. The recipe providing unit can also suggest related recipes by referring to the activity of the user's friends on social media. For example, the recipe providing unit analyzes the social media activity of the user's friends and suggests related recipes. This allows related recipes to be efficiently suggested by analyzing social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, ingredient analysis unit, menu creation unit, ingredient procurement unit, and recipe provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect health data such as heart rate, number of steps, and calories burned using the camera 42 and microphone 38B of the smart device 14. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 to understand the user's health condition. The ingredient analysis unit can collect and analyze ingredient information in the refrigerator using the camera 42 of the smart device 14. For example, the menu creation unit creates a menu based on data obtained from the analysis unit and ingredient analysis unit by the specific processing unit 290 of the data processing device 12. For example, the ingredient procurement unit can order missing ingredients from an online store by the specific processing unit 290 of the data processing device 12. The recipe providing unit can provide instructions on how to make a menu through the control unit 46A of the smart device 14 and explain the cooking procedure in an easy-to-understand manner, for example. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, ingredient analysis unit, menu creation unit, ingredient procurement unit, and recipe provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect health data such as heart rate, step count, and calories burned using the camera 42 and microphone 238 of the smart glasses 214. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes data collected by the specific processing unit 290 of the data processing device 12 to understand the user's health condition. The ingredient analysis unit can collect and analyze ingredient information in the refrigerator using the camera 42 of the smart glasses 214. For example, the menu creation unit creates a menu based on data obtained from the analysis unit and ingredient analysis unit by the specific processing unit 290 of the data processing device 12. For example, the ingredient procurement unit can order missing ingredients from an online store by the specific processing unit 290 of the data processing device 12. The recipe provider can provide instructions on how to make a menu through the control unit 46A of the smart glasses 214, for example, and explain the cooking steps in an easy-to-understand manner. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, ingredient analysis unit, menu creation unit, ingredient procurement unit, and recipe provision unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit can collect health data such as heart rate, number of steps, and calories burned using the camera 42 and microphone 238 of the headset terminal 314. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 to understand the user's health condition. For example, the ingredient analysis unit can collect and analyze ingredient information in the refrigerator using the camera 42 of the headset terminal 314. For example, the menu creation unit creates a menu based on data obtained from the analysis unit and ingredient analysis unit by the specific processing unit 290 of the data processing device 12. For example, the ingredient procurement unit can order missing ingredients from an online store using the specific processing unit 290 of the data processing device 12. The recipe providing unit can provide instructions on how to make a menu using the control unit 46A of the headset terminal 314, and explain the cooking procedure in an easy-to-understand manner. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, ingredient analysis unit, menu creation unit, ingredient procurement unit, and recipe provision unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect health data such as heart rate, number of steps, and calories burned using the camera 42 and microphone 238 of the robot 414. The collection unit can also be realized by the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 to understand the user's health condition. The ingredient analysis unit can collect and analyze ingredient information in the refrigerator using the camera 42 of the robot 414. For example, the menu creation unit creates a menu based on data obtained from the analysis unit and ingredient analysis unit by the specific processing unit 290 of the data processing device 12. For example, the ingredient procurement unit can order missing ingredients from an online store by the specific processing unit 290 of the data processing device 12. The recipe providing unit can provide instructions on how to make a menu using the control unit 46A of the robot 414, and explain the cooking procedure in an easy-to-understand manner.

[0138] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0139] The health management support system may further include a sleep analysis unit that collects and analyzes the user's sleep data. The sleep analysis unit may record the user's sleep patterns using, for example, a wearable device. The sleep analysis unit may also evaluate the user's sleep quality and provide advice for improvement. Furthermore, the sleep analysis unit may suggest optimal exercise and meal timings based on the user's sleep data. This may support the user's comprehensive health management.

[0140] The collection unit can estimate the user's emotions and adjust the frequency of health data collection based on the estimated emotions. For example, if the user is feeling stressed, the collection unit can collect heart rate and stress level data more frequently. On the other hand, if the user is relaxed, the collection frequency can be reduced. This allows for flexible data collection according to the user's emotional state.

[0141] The analysis unit can take weather data into account when analyzing a user's health data. For example, high temperatures and humidity can affect the analysis results of the user's exercise volume and calorie consumption. It can also provide appropriate exercise and dietary advice to the user based on weather data. This allows for more accurate health management.

[0142] The collection unit can estimate the user's emotions and select the type of health data to collect based on the estimated emotions. For example, if the user is tired, the collection unit can prioritize collecting sleep data and relaxation state data. Also, if the user is active, the collection unit can prioritize collecting exercise data and calorie consumption data. This makes it possible to collect appropriate data according to the user's emotional state.

[0143] The ingredient analysis unit can analyze the user's dietary history and suggest ingredients based on past eating patterns. For example, it can suggest new menu items based on ingredients that the user has liked to eat in the past. The ingredient analysis unit can also suggest ways to improve nutritional balance based on the user's dietary history. This allows ingredients to be used in accordance with the user's preferences and health condition.

[0144] The menu creation unit can estimate the user's emotions and adjust the difficulty of the menu based on the estimated emotions. For example, if the user is tired, it can suggest a menu that is simple and easy to make. On the other hand, if the user is energetic, it can suggest a menu that requires a little effort. This makes it possible to suggest appropriate menus according to the user's emotional state.

[0145] The ingredient procurement department can analyze a user's purchasing history and suggest optimal procurement methods based on past trends in ingredients. For example, it can automatically list ingredients that a user frequently purchases, making ordering at the online store easier. Also, if a user has a preference for a particular brand or store, it can suggest procurement methods taking that information into account. This allows for efficient ingredient procurement that matches the user's purchasing trends.

[0146] The recipe provider can estimate the user's emotions and adjust the recipe explanation method based on the estimated emotions. For example, if the user is feeling stressed, it can provide a concise and easy-to-understand explanation. On the other hand, if the user is relaxed, it can provide a recipe with detailed explanations and tips. This makes it possible to provide appropriate recipes according to the user's emotional state.

[0147] When collecting a user's health data, the collection unit can adjust the timing of data collection taking into account the user's lifestyle. For example, if the user is a nocturnal person, the collection unit can strengthen data collection at night. Also, if the user is a morning person, the collection unit can strengthen data collection in the morning. This makes it possible to collect data appropriately in line with the user's lifestyle.

[0148] The analysis unit can estimate the user's emotions and adjust the method of providing feedback on the analysis results based on the estimated emotions. For example, if the user is feeling stressed, it can emphasize positive feedback. Alternatively, if the user is relaxed, it can provide detailed analysis results. This makes it possible to provide appropriate feedback according to the user's emotional state.

[0149] The processing flow of the second embodiment will be briefly explained below.

[0150] Step 1: The collection unit collects health data such as heart rate, number of steps, and calories burned. For example, the collection unit measures heart rate using a wearable device. The collection unit can also count steps using a pedometer. The collection unit can also record the user's activity level to calculate calories burned. The collection unit also collects information about ingredients in the refrigerator. For example, the collection unit uses a camera in the refrigerator to recognize the type of ingredients, measure the amount of ingredients, and record the expiration date. Step 2: The analysis unit analyzes the data collected by the collection unit and understands the user's health condition. For example, the analysis unit uses AI to analyze heart rate fluctuations. The analysis unit can also evaluate the user's exercise volume based on step count data. The analysis unit can also evaluate the user's energy consumption based on calorie consumption data. Step 3: The ingredient analysis unit collects and analyzes information about ingredients in the refrigerator. For example, the ingredient analysis unit uses a camera inside the refrigerator to recognize the type of ingredient. The ingredient analysis unit can also measure the amount of ingredients. The ingredient analysis unit can also record the expiration date of the ingredients. Step 4: The menu creation unit creates a menu based on the data obtained by the analysis unit and the ingredient analysis unit. The menu creation unit, for example, uses AI to suggest menus that match the user's health-consciousness. The menu creation unit can also create menus taking into account the nutritional value of ingredients. The menu creation unit can also create menus taking into account the user's allergy information. Step 5: The ingredient procurement department procures the necessary ingredients based on the menu created by the menu creation department. For example, the ingredient procurement department orders missing ingredients from an online store. Alternatively, the ingredient procurement department can purchase ingredients from a local supermarket. Alternatively, the ingredient procurement department can arrange for the ingredients to be delivered. Step 6: The recipe provider provides instructions on how to prepare the menu created by the menu creator. The recipe provider displays the recipe on a smartphone or tablet, for example. The recipe provider can also provide easy-to-understand explanations of the cooking steps. The recipe provider can also provide advice in real time during cooking.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0155] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0156] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0171] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0187] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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).

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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).

[0208] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0209] 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."

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0221] 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.

[0222] [Explanation of symbols]

[0223] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects health data such as heart rate, number of steps, and calories burned; an analysis unit that analyzes the data collected by the collection unit; a collection unit that collects information about ingredients in a refrigerator; an ingredient analysis unit that analyzes the ingredient information collected by the collection unit; a menu creation unit that creates a menu based on the data obtained by the analysis unit and the ingredient analysis unit; a material procurement unit that procures necessary materials based on the menu created by the menu creation unit; a recipe providing unit that provides instructions on how to make the menu created by the menu creating unit. A system characterized by:

2. The collecting unit Collect health data such as heart rate, steps, and calories burned The system of claim 1 .

3. The analysis unit Analyze collected health data to understand the user's health condition The system of claim 1 .

4. The collecting unit Collecting information about ingredients in the refrigerator The system of claim 1 .

5. The ingredient analysis unit Analyze the collected food information and determine available ingredients The system of claim 1 .

6. The menu creation unit Create menus tailored to the user's health preferences The system of claim 1 .

7. The material procurement department Order the necessary materials from our online store The system of claim 1 .

8. The recipe providing unit Display recipes on smartphones and tablets and provide easy-to-understand cooking instructions The system of claim 1 .

Images