system

Abstract

The system according to this embodiment aims to accurately understand the contents of a person's diet and provide advice on health management. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, an estimation unit, and a provision unit. The reception unit uploads a photo of a meal. The analysis unit analyzes the photo uploaded by the reception unit and identifies the type and quantity of food. The estimation unit estimates the calories and nutrients based on the type and quantity of food identified by the analysis unit. The provision unit provides health management advice based on the calories and nutrients estimated by the estimation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to accurately grasp the content of meals and utilize it for health management.

[0005] The system according to the embodiment aims to accurately grasp the content of meals and provide advice on health management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, an estimation unit, and a provision unit. The reception unit uploads a photo of a meal. The analysis unit analyzes the photo uploaded by the reception unit and identifies the type and quantity of food. The estimation unit estimates the calories and nutrients based on the type and quantity of food identified by the analysis unit. The provision unit provides health management advice based on the calories and nutrients estimated by the estimation unit. [Effects of the Invention]

[0007] The system according to this embodiment can accurately grasp the contents of a person's diet and provide advice on health management. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The health management system according to an embodiment of the present invention is a system that provides health management advice by having an AI analyze and visualize the estimated calories and deficient nutrients simply by uploading a photo of what the user has eaten. The health management system works by having the user take a photo of what they have eaten with a device such as a smartphone and upload it to the application. Next, the AI ​​analyzes the uploaded photo and identifies the type and amount of food. Based on the type and amount of food, the AI ​​estimates the calories and nutrient content. Furthermore, the AI ​​considers the user's past meal data and physical condition information to identify deficient nutrients and excess calories and generates health management advice. This advice is provided to the user through the application. For example, the user takes a photo of what they have eaten with their smartphone and uploads it to the application. In this case, the user does not need to do anything special; they just need to take a photo. For example, they take a photo of the bread and salad they ate for breakfast and upload it to the application. This information is input to the AI. Next, the AI ​​analyzes the uploaded photo. The AI ​​uses image recognition technology to identify the type and amount of food in the photo. For example, it identifies the type of bread and the ingredients of the salad and measures the amount of each. This allows the system to estimate calorie and nutrient content based on the type and quantity of food consumed. Furthermore, the AI ​​considers the user's past dietary data and physical characteristics to identify nutrient deficiencies and excess calories. For example, if past data reveals a vitamin B deficiency, the AI ​​checks if vitamin B is present in the day's meals and generates advice if it is. For instance, it might advise, "Eat foods containing vitamin B for breakfast." This advice is provided to the user through the application. Users can view the AI-generated advice simply by opening the application. For example, specific advice such as, "Today's calorie intake is 2000kcal, and the nutrient you are deficient in is vitamin B. Eat a salad for breakfast," might be displayed. This system allows users to easily manage their health.By simply taking and uploading photos of what they eat, users can have AI analyze calories and nutrients and receive health management advice, eliminating the need for complex calculations or data entry. Furthermore, the AI ​​considers the user's physical characteristics and past data to generate personalized advice, enabling optimal health management for each individual. For example, it might advise users deficient in vitamin B to consume foods containing vitamin B, or encourage exercise for users with excessive calorie intake. This allows the health management system to automatically analyze a user's diet and provide health management advice.

[0029] The health management system according to this embodiment comprises a reception unit, an analysis unit, an estimation unit, and a provision unit. The reception unit receives photos of meals uploaded by users. The reception unit allows users to take photos of meals using devices such as smartphones or tablets and upload them to the application. The reception unit inputs the uploaded photos into the AI. The analysis unit uses the AI ​​to analyze the uploaded photos and identify the type and quantity of food. The analysis unit uses image recognition technology to identify the type and quantity of food in the photos. The analysis unit can classify the type of food and measure the quantity using deep learning technology. The analysis unit can refer to a pre-trained database to identify the type of food. The estimation unit estimates the calorie and nutrient content based on the type and quantity of food identified by the analysis unit. The estimation unit can use an algorithm to calculate the calorie and nutrient content based on the type and quantity of food. The estimation unit can obtain the calorie and nutrient content for each type of food from the database and calculate it according to the quantity. The provision unit provides health management advice based on the calories and nutrients estimated by the estimation unit. The provision unit can generate advice by considering, for example, the user's past dietary data and physical characteristics. The provision unit can analyze, for example, the user's past dietary data to identify deficient nutrients and excess calories. The provision unit can provide individually optimized advice by considering, for example, the user's physical characteristics. The provision unit provides advice to the user through the application. The provision unit can send advice to the user using, for example, the application's notification function. The provision unit can display the advice on, for example, the application's dashboard. As a result, the health management system according to the embodiment can automatically analyze the user's diet and provide health management advice.

[0030] The reception desk receives photos of meals uploaded by users. Users can take photos of their meals using devices such as smartphones or tablets and upload them to the application. Specifically, users install a dedicated application and utilize its camera function to take photos of their meals. The application automatically uploads the captured photos to a cloud server, which the reception desk receives. The reception desk inputs the uploaded photos into an AI. The AI ​​uses image recognition technology to perform pre-processing to identify the type and quantity of food in the photos. For example, the AI ​​adjusts the resolution of the photos and performs filtering to remove noise. It can also use segmentation technology to identify the area of ​​the photo occupied by food. This allows the reception desk to efficiently process the photos uploaded by users and prepare them for transmission to the analysis department. Furthermore, the reception desk protects data using encryption technology to ensure secure data transmission from the user's device. For example, it uses the SSL / TLS protocol to prevent eavesdropping and tampering by third parties during data transmission. This ensures that the reception desk reliably receives photos of meals while protecting user privacy.

[0031] The analysis unit uses AI to analyze uploaded photos and identify the type and quantity of food. For example, it uses image recognition technology to identify the type and quantity of food in a photo. Specifically, it uses an image classification model based on deep learning technology to classify the type of food. For instance, it uses a convolutional neural network (CNN) to extract food features and compare them with a pre-trained database to identify the type of food. Furthermore, to measure the quantity of food, it can use algorithms based on the number of pixels in the image and the size of the tableware. For example, it can calculate the area occupied by the food based on the standard size of the tableware and estimate the quantity. The analysis unit can also refer to a pre-trained database to identify the type of food. This database contains thousands of images and characteristics of different types of food, allowing the AI ​​to identify the type of food with high accuracy. Additionally, the analysis unit can refer to the user's past meal data to quickly identify the same food if it is uploaded again. This enables the analysis unit to quickly and accurately analyze uploaded photos and identify the type and quantity of food.

[0032] The estimation unit estimates the calorie and nutrient content based on the type and quantity of food identified by the analysis unit. The estimation unit can, for example, use algorithms to calculate calorie and nutrient content based on the type and quantity of food. Specifically, it retrieves the calorie and nutrient content for each type of food from a database and calculates it according to the quantity. For example, if the calorie and nutrient content per 100 grams is registered in the database, the total calorie and nutrient content is calculated based on the quantity identified by the analysis unit. The estimation unit can, for example, retrieve the calorie and nutrient content for each type of food from a database and calculate it according to the quantity. Furthermore, the estimation unit can adjust the estimated calorie and nutrient values ​​according to the user's individual health condition and goals. For example, if the user is on a diet, the estimation unit provides advice to limit calorie intake. Also, if there is a deficiency in a particular nutrient, it can suggest meals to supplement that nutrient. This allows the estimation unit to provide specific information useful for the user's health management. Furthermore, the estimation unit can analyze the user's eating patterns and nutrient intake trends based on past data and provide advice for long-term health management. This allows the estimation unit to comprehensively understand the user's health condition and provide appropriate advice.

[0033] The service provider provides health management advice based on calories and nutrients estimated by the estimation unit. The service provider can generate advice by considering, for example, the user's past dietary data and physical characteristics. Specifically, it analyzes the user's dietary history to identify nutrient deficiencies or excesses. For example, based on the past week's dietary data, if vitamin or mineral intake is insufficient, it can suggest meals to supplement those nutrients. If calorie intake is excessive, it can suggest meals to reduce calorie intake. The service provider can also provide individually optimized advice by considering the user's physical characteristics. For example, if a user has allergies, it can suggest meals that do not contain those allergens. Furthermore, if a user has specific health goals (e.g., muscle building or weight loss), it can suggest meals tailored to those goals. The service provider delivers advice to the user through the application. For example, it can send advice to the user using the application's notification function. Users can receive notifications on their smartphones to view advice in real time. Advice can also be displayed on the application's dashboard, allowing users to check it at any time. This enables the service provider to support the user's health management and provide specific advice for a healthier lifestyle. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it can provide a function that allows users to rate their satisfaction with the advice provided, and revise the advice based on that rating. This allows the service provider to always provide users with the best possible advice and maximize the effectiveness of their health management.

[0034] The service provider can generate advice by considering the user's past dietary data and physical condition information. For example, the service provider can analyze the user's past dietary data to identify deficient nutrients and excess calories. For example, the service provider can provide individually optimized advice by considering the user's physical condition information. This ensures that individually optimized advice is provided by considering the user's past dietary data and physical condition information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past dietary data into AI, which can then identify deficient nutrients and excess calories and generate advice.

[0035] The analysis unit can identify the type and quantity of food using image recognition technology. For example, the analysis unit can classify the type of food in a photograph and measure its quantity using deep learning technology. For example, the analysis unit can refer to a pre-trained database to identify the type of food. This allows for accurate identification of the type and quantity of food using image recognition technology. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input a photograph into an AI, which can then identify the type and quantity of food.

[0036] The estimation unit can estimate the calorie and nutrient content based on the type and quantity of food. For example, the estimation unit uses an algorithm to calculate the calorie and nutrient content based on the type and quantity of food. For example, the estimation unit can obtain the calorie and nutrient content for each type of food from a database and calculate it according to the quantity. This allows for accurate estimation of the calorie and nutrient content based on the type and quantity of food. Some or all of the above-described processes in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input the type and quantity of food into the AI, and the AI ​​can estimate the calorie and nutrient content.

[0037] The service provider can provide advice to users through the application. For example, the service provider can send advice to users using the application's notification function. For example, the service provider can display advice on the application's dashboard. This allows the service provider to provide advice to users through the application. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have AI generate advice and provide it to users through the application.

[0038] The service provider can provide advice in a diary style. For example, the service provider can provide a diary-style interface with fields for recording daily meals and comments. For example, the service provider can provide advice based on the user recording their daily meals. This makes it easier for users to continuously manage their health by providing advice in a diary style. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have AI design the diary-style interface and provide it to the user.

[0039] The reception desk can analyze a user's past food photo upload history and select the optimal upload method. For example, the reception desk can prioritize suggesting upload methods that the user has frequently used in the past (e.g., voice input or text input). For example, if a user tends to upload during a specific time period, the reception desk can send a notification during that time period. For example, the reception desk can analyze the quality of photos the user has uploaded in the past and suggest the optimal shooting method. In this way, by analyzing past upload history, the reception desk can provide the user with the most suitable upload method. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past upload history into the AI, which can then select the optimal upload method.

[0040] The reception system can filter uploaded photos based on the user's current eating patterns and health status. For example, if a user is on a diet, the reception system can prioritize uploading photos of low-calorie meals. If a user needs to consume a specific nutrient, the reception system can prioritize uploading photos of meals containing that nutrient. If a user has allergies, the reception system can filter out photos of meals containing allergens. This allows for the uploading of appropriate photos by filtering based on the user's current eating patterns and health status. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can input the user's current eating patterns and health status into the AI, which can then perform the filtering.

[0041] The reception system can prioritize uploading highly relevant food photos by considering the user's geographical location when they upload photos. For example, if the user is in a specific region, the reception system can prioritize uploading food photos that include local specialties from that region. For example, if the user is traveling, the reception system can prioritize uploading food photos from their travel destination. For example, if the user is at home, the reception system can prioritize uploading photos of home-cooked meals. This allows for the prioritization of highly relevant food photos by considering geographical location. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input the user's geographical location information into the AI, which can then select highly relevant food photos.

[0042] The reception desk can analyze a user's social media activity when they upload photos and upload relevant food photos. For example, the reception desk can prioritize uploading food photos that the user has shared on social media. For example, the reception desk can upload food photos that the user has "liked" on social media. For example, the reception desk can upload photos that the user follows on social media. In this way, relevant food photos can be uploaded by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input the user's social media activity data into AI, and the AI ​​can select relevant food photos.

[0043] The analysis unit can apply different analysis algorithms based on the type and quantity of food consumed during the analysis. For example, in the case of a high-calorie meal, the analysis unit can apply an algorithm specialized in calorie calculation. For example, in the case of a nutritionally balanced meal, the analysis unit can apply an algorithm that performs a detailed analysis of nutrients. For example, in the case of a meal containing a large amount of a particular nutrient, the analysis unit can apply an algorithm that performs a specialized analysis of that nutrient. By applying different analysis algorithms based on the type and quantity of food consumed, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the type and quantity of food into the AI, which can then select an appropriate analysis algorithm and perform the analysis.

[0044] The analysis unit can customize its analysis method according to the time of day and season of the meal. For example, in the case of breakfast, the analysis unit can focus on energy intake. For example, in winter, the analysis unit can focus on vitamin D intake. For example, in summer, the analysis unit can focus on hydration. By customizing the analysis method according to the time of day and season of the meal, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information on the time of day and season of the meal into the AI, which can then select an appropriate analysis method and perform the analysis.

[0045] The analysis unit can perform analysis while considering the geographical distribution of meals. For example, if the user is in a specific region, the analysis unit can perform analysis while considering the food culture of that region. For example, if the user is traveling, the analysis unit can perform analysis while considering the meals of the travel destination. For example, if the user is at home, the analysis unit can perform analysis while considering home cooking. By considering geographical distribution, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical distribution data into AI, which can then select an appropriate analysis method and perform the analysis.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on diet during the analysis process. For example, the analysis unit can perform analysis by referring to the latest nutritional research. For example, the analysis unit can perform analysis by referring to past data on diet. For example, the analysis unit can perform analysis by referring to specialized books on diet. This improves the accuracy of the analysis by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into AI, which can then select an appropriate analysis method and perform the analysis.

[0047] The estimation unit can apply different estimation algorithms based on the type and quantity of food during estimation. For example, in the case of a high-calorie meal, the estimation unit can apply an algorithm specialized in calorie calculation. For example, in the case of a nutritionally balanced meal, the estimation unit can apply an algorithm that performs detailed estimation of nutrients. For example, in the case of a meal containing a large amount of a particular nutrient, the estimation unit can apply an algorithm that performs estimation specialized for that nutrient. By applying different estimation algorithms based on the type and quantity of food, the accuracy of the estimation is improved. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input the type and quantity of food into the AI, which can then select an appropriate estimation algorithm and perform the estimation.

[0048] The estimation unit can customize its estimation method according to the time of meal and the season. For example, in the case of breakfast, the estimation unit can focus on energy intake. For example, in winter, the estimation unit can focus on vitamin D intake. For example, in summer, the estimation unit can focus on hydration. By customizing the estimation method according to the time of meal and the season, more appropriate estimation results can be provided. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input information on the time of meal and the season into the AI, which can select an appropriate estimation method and perform the estimation.

[0049] The estimation unit can perform estimations while considering the geographical distribution of meals. For example, if the user is in a specific region, the estimation unit can perform estimations while considering the food culture of that region. For example, if the user is traveling, the estimation unit can perform estimations while considering the meals of the travel destination. For example, if the user is at home, the estimation unit can perform estimations while considering home cooking. By considering geographical distribution, more appropriate estimation results can be provided. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input the user's geographical distribution data into AI, which can then select an appropriate estimation method and perform the estimation.

[0050] The estimation unit can improve the accuracy of its estimations by referring to relevant literature on diet during the estimation process. For example, the estimation unit can perform estimations by referring to the latest nutritional research. For example, the estimation unit can perform estimations by referring to past data on diet. For example, the estimation unit can perform estimations by referring to specialized books on diet. This improves the accuracy of the estimation by referring to relevant literature. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input relevant literature data into AI, which can then select an appropriate estimation method and perform the estimation.

[0051] The service provider can adjust the level of detail of the advice given by considering the user's past dietary data and physical condition information. For example, if the user has a history of vitamin B deficiency, the service provider can specifically suggest foods containing vitamin B. For example, if the user has a history of excessive calorie intake, the service provider can suggest low-calorie meals. For example, if the user has a specific allergy, the service provider can suggest meals that do not contain that allergen. This allows for the provision of more detailed advice by considering past dietary data and physical condition information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past dietary data and physical condition information into AI, which can then generate appropriate advice.

[0052] The service provider can customize advice based on the user's current health status and goals when providing advice. For example, if the user is on a diet, the service provider can suggest a low-calorie meal. For example, if the user is aiming to build muscle, the service provider can suggest a high-protein meal. For example, if the user has a specific health problem, the service provider can suggest a meal that addresses that problem. By customizing the advice based on the user's current health status and goals, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's health status and goals into the AI, which can then generate appropriate advice.

[0053] The service provider can provide optimal advice by considering the user's geographical location when providing advice. For example, if the user is in a specific region, the service provider can suggest meals that include local specialties. For example, if the user is traveling, the service provider can suggest meals at the travel destination. For example, if the user is at home, the service provider can suggest home-cooked meals. By considering geographical location, the service provider can provide more appropriate advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location into AI, and the AI ​​can generate appropriate advice.

[0054] The service provider can provide relevant advice by analyzing the user's social media activity when providing advice. For example, the service provider can provide advice based on meals the user has shared on social media. For example, the service provider can provide advice based on meals the user has "liked" on social media. For example, the service provider can provide advice by referring to information on cooking accounts the user follows. In this way, relevant advice can be provided by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into AI, and the AI ​​can generate appropriate advice.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] A health management system can acquire a user's exercise data and combine it with dietary data to provide comprehensive health advice. For example, if a user is using a smartwatch or fitness tracker, exercise data is acquired from that device. This exercise data includes steps taken, calories burned, and exercise time. The analysis unit can analyze this exercise data in combination with dietary data to calculate the user's overall calorie balance. For example, it can compare the calories a user burns and consumes in a day to check for excessive calorie intake. Based on this data, the service provider can advise the user to increase their exercise or suggest meals to supplement specific nutrients. This allows users to manage their health while balancing diet and exercise.

[0057] A health management system can acquire a user's sleep data and combine it with dietary data to provide comprehensive health advice. For example, if a user uses a smartwatch or sleep tracker, sleep data is acquired from that device. This sleep data includes sleep duration, sleep quality, and the ratio of deep to light sleep. The analysis unit can analyze this sleep data in combination with dietary data to assess the user's overall health status. For example, if a user is experiencing persistent sleep deprivation, the system can provide dietary suggestions to improve sleep quality and advise the user to consume foods with relaxing effects. This allows users to manage their health while balancing diet and sleep.

[0058] A health management system can monitor a user's fluid intake and provide comprehensive health advice by combining it with dietary data. For example, if a user is using an application to record their fluid intake, the system can obtain fluid intake data from that application. This fluid intake data includes the amount and timing of fluid intake throughout the day. The analysis unit can analyze this fluid intake data in combination with dietary data to evaluate the user's overall fluid balance. For example, if fluid intake is insufficient, the system can advise the user to hydrate more or suggest consuming foods high in water. This allows users to manage their health while balancing their diet and fluid intake.

[0059] The health management system can provide dietary advice while taking into account the user's allergy information. For example, if a user has an allergy to a specific food, that information can be registered in the system. The analysis unit can identify foods containing allergens when analyzing food photos uploaded by the user. If foods containing allergens are detected, the provision unit can advise the user to avoid those foods. Furthermore, the provision unit can also suggest alternative foods that do not contain the allergens. This allows users to manage their diet while taking allergies into consideration.

[0060] A health management system can learn a user's eating patterns and provide individually optimized dietary advice. For example, it can analyze the user's past uploaded meal data to learn their eating tendencies and preferences. Based on this data, the analysis unit can identify the user's preferred foods and eating patterns. The provision unit can then provide dietary advice tailored to the user's preferences. For example, if a user likes a particular food, the system can suggest healthy recipes that include that food. This allows users to manage their health while enjoying meals that suit their preferences.

[0061] A health management system can compare a user's dietary data with other users to assess their relative health status. For example, it can aggregate dietary data from users of the same age group and gender to calculate average calorie intake and nutrient balance. The analysis unit can compare the user's dietary data with these averages to assess how healthy the user's diet is. The service provider can provide the user with feedback indicating their relative health status and advise on areas for improvement. For example, it might offer specific advice such as, "Your calorie intake is higher than average, so try increasing your exercise or reducing your calorie intake." This allows users to understand how healthy their diet is compared to other users and identify areas for improvement.

[0062] A health management system can predict future health risks and provide preventative advice based on a user's dietary data. For example, the analysis unit analyzes the user's dietary data and predicts future health risks if there is a deficiency or excess of certain nutrients. Based on these predictions, the service provider can provide preventative advice to the user. For example, it might provide specific advice such as, "Your current eating patterns may increase your risk of osteoporosis due to vitamin D deficiency in the future. Try to consume foods rich in vitamin D." This allows users to manage their diet to prevent future health risks.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk receives a photo of the meal from the user. For example, the user can take a photo of their meal using a device such as a smartphone or tablet and upload it to the application. The reception desk then inputs the uploaded photo into the AI. Step 2: The analysis unit uses AI to analyze the uploaded photos and identify the type and quantity of food. For example, it uses image recognition technology or deep learning technology to identify the type and quantity of food in the photos. The analysis unit can refer to a pre-trained database. Step 3: The estimation unit estimates the calorie and nutrient content based on the type and quantity of food identified by the analysis unit. For example, an algorithm can be used to calculate the calorie and nutrient content based on the type and quantity of food. The estimation unit can retrieve the calorie and nutrient content for each type of food from a database and calculate it according to the quantity. Step 4: The service provider provides health management advice based on the calories and nutrients estimated by the estimation unit. For example, it can generate advice by considering the user's past dietary data and physical characteristics. The service provider delivers the advice to the user through the application. For example, it can send advice to the user using the application's notification function.

[0065] (Example of form 2) The health management system according to an embodiment of the present invention is a system that provides health management advice by having an AI analyze and visualize the estimated calories and deficient nutrients simply by uploading a photo of what the user has eaten. The health management system works by having the user take a photo of what they have eaten with a device such as a smartphone and upload it to the application. Next, the AI ​​analyzes the uploaded photo and identifies the type and amount of food. Based on the type and amount of food, the AI ​​estimates the calories and nutrient content. Furthermore, the AI ​​considers the user's past meal data and physical condition information to identify deficient nutrients and excess calories and generates health management advice. This advice is provided to the user through the application. For example, the user takes a photo of what they have eaten with their smartphone and uploads it to the application. In this case, the user does not need to do anything special; they just need to take a photo. For example, they take a photo of the bread and salad they ate for breakfast and upload it to the application. This information is input to the AI. Next, the AI ​​analyzes the uploaded photo. The AI ​​uses image recognition technology to identify the type and amount of food in the photo. For example, it identifies the type of bread and the ingredients of the salad and measures the amount of each. This allows the system to estimate calorie and nutrient content based on the type and quantity of food consumed. Furthermore, the AI ​​considers the user's past dietary data and physical characteristics to identify nutrient deficiencies and excess calories. For example, if past data reveals a vitamin B deficiency, the AI ​​checks if vitamin B is present in the day's meals and generates advice if it is. For instance, it might advise, "Eat foods containing vitamin B for breakfast." This advice is provided to the user through the application. Users can view the AI-generated advice simply by opening the application. For example, specific advice such as, "Today's calorie intake is 2000kcal, and the nutrient you are deficient in is vitamin B. Eat a salad for breakfast," might be displayed. This system allows users to easily manage their health.By simply taking and uploading photos of what they eat, users can have AI analyze calories and nutrients and receive health management advice, eliminating the need for complex calculations or data entry. Furthermore, the AI ​​considers the user's physical characteristics and past data to generate personalized advice, enabling optimal health management for each individual. For example, it might advise users deficient in vitamin B to consume foods containing vitamin B, or encourage exercise for users with excessive calorie intake. This allows the health management system to automatically analyze a user's diet and provide health management advice.

[0066] The health management system according to this embodiment comprises a reception unit, an analysis unit, an estimation unit, and a provision unit. The reception unit receives photos of meals uploaded by users. The reception unit allows users to take photos of meals using devices such as smartphones or tablets and upload them to the application. The reception unit inputs the uploaded photos into the AI. The analysis unit uses the AI ​​to analyze the uploaded photos and identify the type and quantity of food. The analysis unit uses image recognition technology to identify the type and quantity of food in the photos. The analysis unit can classify the type of food and measure the quantity using deep learning technology. The analysis unit can refer to a pre-trained database to identify the type of food. The estimation unit estimates the calorie and nutrient content based on the type and quantity of food identified by the analysis unit. The estimation unit can use an algorithm to calculate the calorie and nutrient content based on the type and quantity of food. The estimation unit can obtain the calorie and nutrient content for each type of food from the database and calculate it according to the quantity. The provision unit provides health management advice based on the calories and nutrients estimated by the estimation unit. The provision unit can generate advice by considering, for example, the user's past dietary data and physical characteristics. The provision unit can analyze, for example, the user's past dietary data to identify deficient nutrients and excess calories. The provision unit can provide individually optimized advice by considering, for example, the user's physical characteristics. The provision unit provides advice to the user through the application. The provision unit can send advice to the user using, for example, the application's notification function. The provision unit can display the advice on, for example, the application's dashboard. As a result, the health management system according to the embodiment can automatically analyze the user's diet and provide health management advice.

[0067] The reception desk receives photos of meals uploaded by users. Users can take photos of their meals using devices such as smartphones or tablets and upload them to the application. Specifically, users install a dedicated application and utilize its camera function to take photos of their meals. The application automatically uploads the captured photos to a cloud server, which the reception desk receives. The reception desk inputs the uploaded photos into an AI. The AI ​​uses image recognition technology to perform pre-processing to identify the type and quantity of food in the photos. For example, the AI ​​adjusts the resolution of the photos and performs filtering to remove noise. It can also use segmentation technology to identify the area of ​​the photo occupied by food. This allows the reception desk to efficiently process the photos uploaded by users and prepare them for transmission to the analysis department. Furthermore, the reception desk protects data using encryption technology to ensure secure data transmission from the user's device. For example, it uses the SSL / TLS protocol to prevent eavesdropping and tampering by third parties during data transmission. This ensures that the reception desk reliably receives photos of meals while protecting user privacy.

[0068] The analysis unit uses AI to analyze uploaded photos and identify the type and quantity of food. For example, it uses image recognition technology to identify the type and quantity of food in a photo. Specifically, it uses an image classification model based on deep learning technology to classify the type of food. For instance, it uses a convolutional neural network (CNN) to extract food features and compare them with a pre-trained database to identify the type of food. Furthermore, to measure the quantity of food, it can use algorithms based on the number of pixels in the image and the size of the tableware. For example, it can calculate the area occupied by the food based on the standard size of the tableware and estimate the quantity. The analysis unit can also refer to a pre-trained database to identify the type of food. This database contains thousands of images and characteristics of different types of food, allowing the AI ​​to identify the type of food with high accuracy. Additionally, the analysis unit can refer to the user's past meal data to quickly identify the same food if it is uploaded again. This enables the analysis unit to quickly and accurately analyze uploaded photos and identify the type and quantity of food.

[0069] The estimation unit estimates the calorie and nutrient content based on the type and quantity of food identified by the analysis unit. The estimation unit can, for example, use algorithms to calculate calorie and nutrient content based on the type and quantity of food. Specifically, it retrieves the calorie and nutrient content for each type of food from a database and calculates it according to the quantity. For example, if the calorie and nutrient content per 100 grams is registered in the database, the total calorie and nutrient content is calculated based on the quantity identified by the analysis unit. The estimation unit can, for example, retrieve the calorie and nutrient content for each type of food from a database and calculate it according to the quantity. Furthermore, the estimation unit can adjust the estimated calorie and nutrient values ​​according to the user's individual health condition and goals. For example, if the user is on a diet, the estimation unit provides advice to limit calorie intake. Also, if there is a deficiency in a particular nutrient, it can suggest meals to supplement that nutrient. This allows the estimation unit to provide specific information useful for the user's health management. Furthermore, the estimation unit can analyze the user's eating patterns and nutrient intake trends based on past data and provide advice for long-term health management. This allows the estimation unit to comprehensively understand the user's health condition and provide appropriate advice.

[0070] The service provider provides health management advice based on calories and nutrients estimated by the estimation unit. The service provider can generate advice by considering, for example, the user's past dietary data and physical characteristics. Specifically, it analyzes the user's dietary history to identify nutrient deficiencies or excesses. For example, based on the past week's dietary data, if vitamin or mineral intake is insufficient, it can suggest meals to supplement those nutrients. If calorie intake is excessive, it can suggest meals to reduce calorie intake. The service provider can also provide individually optimized advice by considering the user's physical characteristics. For example, if a user has allergies, it can suggest meals that do not contain those allergens. Furthermore, if a user has specific health goals (e.g., muscle building or weight loss), it can suggest meals tailored to those goals. The service provider delivers advice to the user through the application. For example, it can send advice to the user using the application's notification function. Users can receive notifications on their smartphones to view advice in real time. Advice can also be displayed on the application's dashboard, allowing users to check it at any time. This enables the service provider to support the user's health management and provide specific advice for a healthier lifestyle. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the advice. For example, it can provide a function that allows users to rate their satisfaction with the advice provided, and revise the advice based on that rating. This allows the service provider to always provide users with the best possible advice and maximize the effectiveness of their health management.

[0071] The service provider can generate advice by considering the user's past dietary data and physical condition information. For example, the service provider can analyze the user's past dietary data to identify deficient nutrients and excess calories. For example, the service provider can provide individually optimized advice by considering the user's physical condition information. This ensures that individually optimized advice is provided by considering the user's past dietary data and physical condition information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past dietary data into AI, which can then identify deficient nutrients and excess calories and generate advice.

[0072] The analysis unit can identify the type and quantity of food using image recognition technology. For example, the analysis unit can classify the type of food in a photograph and measure its quantity using deep learning technology. For example, the analysis unit can refer to a pre-trained database to identify the type of food. This allows for accurate identification of the type and quantity of food using image recognition technology. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input a photograph into an AI, which can then identify the type and quantity of food.

[0073] The estimation unit can estimate the calorie and nutrient content based on the type and quantity of food. For example, the estimation unit uses an algorithm to calculate the calorie and nutrient content based on the type and quantity of food. For example, the estimation unit can obtain the calorie and nutrient content for each type of food from a database and calculate it according to the quantity. This allows for accurate estimation of the calorie and nutrient content based on the type and quantity of food. Some or all of the above-described processes in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input the type and quantity of food into the AI, and the AI ​​can estimate the calorie and nutrient content.

[0074] The service provider can provide advice to users through the application. For example, the service provider can send advice to users using the application's notification function. For example, the service provider can display advice on the application's dashboard. This allows the service provider to provide advice to users through the application. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can have AI generate advice and provide it to users through the application.

[0075] The service provider can provide advice in a diary style. For example, the service provider can provide a diary-style interface with fields for recording daily meals and comments. For example, the service provider can provide advice based on the user recording their daily meals. This makes it easier for users to continuously manage their health by providing advice in a diary style. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can have AI design the diary-style interface and provide it to the user.

[0076] The reception desk can estimate the user's emotions and adjust the timing of photo uploads based on the estimated emotions. For example, if the user is feeling stressed, the reception desk may encourage them to upload photos during a time when they can relax. If the user is busy, the reception desk may provide an interface that allows for quick uploads. If the user is relaxed, the reception desk may provide an option to enter detailed information. This reduces the user's burden by adjusting the timing of photo uploads according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into an AI, which can estimate the emotions and adjust the upload timing.

[0077] The reception desk can analyze a user's past food photo upload history and select the optimal upload method. For example, the reception desk can prioritize suggesting upload methods that the user has frequently used in the past (e.g., voice input or text input). For example, if a user tends to upload during a specific time period, the reception desk can send a notification during that time period. For example, the reception desk can analyze the quality of photos the user has uploaded in the past and suggest the optimal shooting method. In this way, by analyzing past upload history, the reception desk can provide the user with the most suitable upload method. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's past upload history into the AI, which can then select the optimal upload method.

[0078] The reception system can filter uploaded photos based on the user's current eating patterns and health status. For example, if a user is on a diet, the reception system can prioritize uploading photos of low-calorie meals. If a user needs to consume a specific nutrient, the reception system can prioritize uploading photos of meals containing that nutrient. If a user has allergies, the reception system can filter out photos of meals containing allergens. This allows for the uploading of appropriate photos by filtering based on the user's current eating patterns and health status. Some or all of the above processing in the reception system may be performed using AI, for example, or not. For example, the reception system can input the user's current eating patterns and health status into the AI, which can then perform the filtering.

[0079] The reception desk can estimate the user's emotions and determine the priority of photos to upload based on the estimated emotions. For example, if the user is excited, the reception desk may prioritize uploading photos of particularly delicious-looking food. If the user is tired, the reception desk may prioritize uploading photos of easy-to-prepare meals. If the user is relaxed, the reception desk may prioritize uploading photos of healthy meals. This allows the reception desk to upload photos that meet the user's needs by prioritizing photos according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into an AI, which can estimate the emotions and determine the priority of photos.

[0080] The reception system can prioritize uploading highly relevant food photos by considering the user's geographical location when they upload photos. For example, if the user is in a specific region, the reception system can prioritize uploading food photos that include local specialties from that region. For example, if the user is traveling, the reception system can prioritize uploading food photos from their travel destination. For example, if the user is at home, the reception system can prioritize uploading photos of home-cooked meals. This allows for the prioritization of highly relevant food photos by considering geographical location. Some or all of the above processing in the reception system may be performed using AI, or not. For example, the reception system can input the user's geographical location information into the AI, which can then select highly relevant food photos.

[0081] The reception desk can analyze a user's social media activity when they upload photos and upload relevant food photos. For example, the reception desk can prioritize uploading food photos that the user has shared on social media. For example, the reception desk can upload food photos that the user has "liked" on social media. For example, the reception desk can upload photos that the user follows on social media. In this way, relevant food photos can be uploaded by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input the user's social media activity data into AI, and the AI ​​can select relevant food photos.

[0082] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. For example, if the user is in a hurry, the analysis unit can perform a simplified analysis. For example, if the user is excited, the analysis unit can provide a visually easy-to-understand analysis result. By adjusting the accuracy of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into an AI, which can estimate the emotions and adjust the accuracy of the analysis.

[0083] The analysis unit can apply different analysis algorithms based on the type and quantity of food consumed during the analysis. For example, in the case of a high-calorie meal, the analysis unit can apply an algorithm specialized in calorie calculation. For example, in the case of a nutritionally balanced meal, the analysis unit can apply an algorithm that performs a detailed analysis of nutrients. For example, in the case of a meal containing a large amount of a particular nutrient, the analysis unit can apply an algorithm that performs a specialized analysis of that nutrient. By applying different analysis algorithms based on the type and quantity of food consumed, the accuracy of the analysis is improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the type and quantity of food into the AI, which can then select an appropriate analysis algorithm and perform the analysis.

[0084] The analysis unit can customize its analysis method according to the time of day and season of the meal. For example, in the case of breakfast, the analysis unit can focus on energy intake. For example, in winter, the analysis unit can focus on vitamin D intake. For example, in summer, the analysis unit can focus on hydration. By customizing the analysis method according to the time of day and season of the meal, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information on the time of day and season of the meal into the AI, which can then select an appropriate analysis method and perform the analysis.

[0085] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input user emotion data into an AI, the AI ​​can estimate the emotions, and the display method of the analysis results can be adjusted.

[0086] The analysis unit can perform analysis while considering the geographical distribution of meals. For example, if the user is in a specific region, the analysis unit can perform analysis while considering the food culture of that region. For example, if the user is traveling, the analysis unit can perform analysis while considering the meals of the travel destination. For example, if the user is at home, the analysis unit can perform analysis while considering home cooking. By considering geographical distribution, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical distribution data into AI, which can then select an appropriate analysis method and perform the analysis.

[0087] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on diet during the analysis process. For example, the analysis unit can perform analysis by referring to the latest nutritional research. For example, the analysis unit can perform analysis by referring to past data on diet. For example, the analysis unit can perform analysis by referring to specialized books on diet. This improves the accuracy of the analysis by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant literature data into AI, which can then select an appropriate analysis method and perform the analysis.

[0088] The estimation unit can estimate the user's emotions and adjust the calorie and nutrient estimation method based on the estimated user emotions. For example, if the user is relaxed, the estimation unit can perform a detailed calorie calculation. For example, if the user is in a hurry, the estimation unit can perform a simplified calorie calculation. For example, if the user is excited, the estimation unit can display calories in a visually easy-to-understand way. By adjusting the estimation method according to the user's emotions, more appropriate estimation results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input user emotion data into AI, which can estimate emotions and adjust the calorie and nutrient estimation method.

[0089] The estimation unit can apply different estimation algorithms based on the type and quantity of food during estimation. For example, in the case of a high-calorie meal, the estimation unit can apply an algorithm specialized in calorie calculation. For example, in the case of a nutritionally balanced meal, the estimation unit can apply an algorithm that performs detailed estimation of nutrients. For example, in the case of a meal containing a large amount of a particular nutrient, the estimation unit can apply an algorithm that performs estimation specialized for that nutrient. By applying different estimation algorithms based on the type and quantity of food, the accuracy of the estimation is improved. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input the type and quantity of food into the AI, which can then select an appropriate estimation algorithm and perform the estimation.

[0090] The estimation unit can customize its estimation method according to the time of meal and the season. For example, in the case of breakfast, the estimation unit can focus on energy intake. For example, in winter, the estimation unit can focus on vitamin D intake. For example, in summer, the estimation unit can focus on hydration. By customizing the estimation method according to the time of meal and the season, more appropriate estimation results can be provided. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input information on the time of meal and the season into the AI, which can select an appropriate estimation method and perform the estimation.

[0091] The estimation unit can estimate the user's emotions and adjust the display method of the estimation results based on the estimated user's emotions. For example, if the user is nervous, the estimation unit can provide a simple and highly visible display method. For example, if the user is relaxed, the estimation unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the estimation unit can provide a display method that gets straight to the point. By adjusting the display method of the estimation results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without using AI. For example, the estimation unit can input user emotion data into an AI, the AI ​​can estimate the emotions, and the display method of the estimation results can be adjusted.

[0092] The estimation unit can perform estimations while considering the geographical distribution of meals. For example, if the user is in a specific region, the estimation unit can perform estimations while considering the food culture of that region. For example, if the user is traveling, the estimation unit can perform estimations while considering the meals of the travel destination. For example, if the user is at home, the estimation unit can perform estimations while considering home cooking. By considering geographical distribution, more appropriate estimation results can be provided. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input the user's geographical distribution data into AI, which can then select an appropriate estimation method and perform the estimation.

[0093] The estimation unit can improve the accuracy of its estimations by referring to relevant literature on diet during the estimation process. For example, the estimation unit can perform estimations by referring to the latest nutritional research. For example, the estimation unit can perform estimations by referring to past data on diet. For example, the estimation unit can perform estimations by referring to specialized books on diet. This improves the accuracy of the estimation by referring to relevant literature. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit can input relevant literature data into AI, which can then select an appropriate estimation method and perform the estimation.

[0094] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is nervous, the service provider can provide advice in a calm tone. If the user is relaxed, the service provider can provide advice in a cheerful tone. If the user is in a hurry, the service provider can provide concise and quick advice. This allows for more appropriate advice to be provided by adjusting the way advice is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into an AI, which can estimate the emotions and adjust the way advice is expressed.

[0095] The service provider can adjust the level of detail of the advice given by considering the user's past dietary data and physical condition information. For example, if the user has a history of vitamin B deficiency, the service provider can specifically suggest foods containing vitamin B. For example, if the user has a history of excessive calorie intake, the service provider can suggest low-calorie meals. For example, if the user has a specific allergy, the service provider can suggest meals that do not contain that allergen. This allows for the provision of more detailed advice by considering past dietary data and physical condition information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past dietary data and physical condition information into AI, which can then generate appropriate advice.

[0096] The service provider can customize advice based on the user's current health status and goals when providing advice. For example, if the user is on a diet, the service provider can suggest a low-calorie meal. For example, if the user is aiming to build muscle, the service provider can suggest a high-protein meal. For example, if the user has a specific health problem, the service provider can suggest a meal that addresses that problem. By customizing the advice based on the user's current health status and goals, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's health status and goals into the AI, which can then generate appropriate advice.

[0097] The service provider can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is stressed, the service provider may prioritize suggesting a relaxing meal. If the user is tired, the service provider may prioritize suggesting a meal suitable for energy replenishment. If the user is relaxed, the service provider may prioritize suggesting a healthy meal. By prioritizing advice according to the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into an AI, which can estimate the emotions and determine the priority of advice.

[0098] The service provider can provide optimal advice by considering the user's geographical location when providing advice. For example, if the user is in a specific region, the service provider can suggest meals that include local specialties. For example, if the user is traveling, the service provider can suggest meals at the travel destination. For example, if the user is at home, the service provider can suggest home-cooked meals. By considering geographical location, the service provider can provide more appropriate advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location into AI, and the AI ​​can generate appropriate advice.

[0099] The service provider can provide relevant advice by analyzing the user's social media activity when providing advice. For example, the service provider can provide advice based on meals the user has shared on social media. For example, the service provider can provide advice based on meals the user has "liked" on social media. For example, the service provider can provide advice by referring to information on cooking accounts the user follows. In this way, relevant advice can be provided by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into AI, and the AI ​​can generate appropriate advice.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] A health management system can acquire a user's exercise data and combine it with dietary data to provide comprehensive health advice. For example, if a user is using a smartwatch or fitness tracker, exercise data is acquired from that device. This exercise data includes steps taken, calories burned, and exercise time. The analysis unit can analyze this exercise data in combination with dietary data to calculate the user's overall calorie balance. For example, it can compare the calories a user burns and consumes in a day to check for excessive calorie intake. Based on this data, the service provider can advise the user to increase their exercise or suggest meals to supplement specific nutrients. This allows users to manage their health while balancing diet and exercise.

[0102] A health management system can acquire a user's sleep data and combine it with dietary data to provide comprehensive health advice. For example, if a user uses a smartwatch or sleep tracker, sleep data is acquired from that device. This sleep data includes sleep duration, sleep quality, and the ratio of deep to light sleep. The analysis unit can analyze this sleep data in combination with dietary data to assess the user's overall health status. For example, if a user is experiencing persistent sleep deprivation, the system can provide dietary suggestions to improve sleep quality and advise the user to consume foods with relaxing effects. This allows users to manage their health while balancing diet and sleep.

[0103] A health management system can monitor a user's stress level and provide comprehensive health advice by combining it with dietary data. For example, if a user is using a device with stress monitoring capabilities, stress data is acquired from that device. This stress data includes heart rate variability, skin electrical activity, and respiratory patterns. The analysis unit can analyze this stress data in combination with dietary data to assess the user's overall stress level. For example, if stress levels are high, the system can advise the user on dietary suggestions to reduce stress or on consuming foods with relaxing effects. This allows users to manage their health by combining diet and stress management.

[0104] A health management system can monitor a user's fluid intake and provide comprehensive health advice by combining it with dietary data. For example, if a user is using an application to record their fluid intake, the system can obtain fluid intake data from that application. This fluid intake data includes the amount and timing of fluid intake throughout the day. The analysis unit can analyze this fluid intake data in combination with dietary data to evaluate the user's overall fluid balance. For example, if fluid intake is insufficient, the system can advise the user to hydrate more or suggest consuming foods high in water. This allows users to manage their health while balancing their diet and fluid intake.

[0105] The health management system can provide dietary advice while taking into account the user's allergy information. For example, if a user has an allergy to a specific food, that information can be registered in the system. The analysis unit can identify foods containing allergens when analyzing food photos uploaded by the user. If foods containing allergens are detected, the provision unit can advise the user to avoid those foods. Furthermore, the provision unit can also suggest alternative foods that do not contain the allergens. This allows users to manage their diet while taking allergies into consideration.

[0106] A health management system can estimate a user's emotions and provide dietary advice based on those emotions. For example, if a user is stressed, the system can suggest foods that promote relaxation. If a user is tired, it can suggest foods suitable for energy replenishment. If a user is relaxed, it can suggest healthy meals. Emotion estimation is performed, for example, by analyzing the user's facial expressions and voice data. This allows for more appropriate health management by providing dietary advice tailored to the user's emotions.

[0107] A health management system can learn a user's eating patterns and provide individually optimized dietary advice. For example, it can analyze the user's past uploaded meal data to learn their eating tendencies and preferences. Based on this data, the analysis unit can identify the user's preferred foods and eating patterns. The provision unit can then provide dietary advice tailored to the user's preferences. For example, if a user likes a particular food, the system can suggest healthy recipes that include that food. This allows users to manage their health while enjoying meals that suit their preferences.

[0108] A health management system can compare a user's dietary data with other users to assess their relative health status. For example, it can aggregate dietary data from users of the same age group and gender to calculate average calorie intake and nutrient balance. The analysis unit can compare the user's dietary data with these averages to assess how healthy the user's diet is. The service provider can provide the user with feedback indicating their relative health status and advise on areas for improvement. For example, it might offer specific advice such as, "Your calorie intake is higher than average, so try increasing your exercise or reducing your calorie intake." This allows users to understand how healthy their diet is compared to other users and identify areas for improvement.

[0109] The health management system can estimate the user's emotions and adjust meal timing based on those emotions. For example, if the user is stressed, the system will advise them to eat during a time when they can relax. If the user is busy, it can suggest meals that can be consumed quickly. If the user is relaxed, it can suggest meals that they can enjoy at a leisurely pace. Emotion estimation is performed, for example, by analyzing the user's facial expressions and voice data. This allows for more appropriate health management by adjusting meal timing according to the user's emotions.

[0110] A health management system can predict future health risks and provide preventative advice based on a user's dietary data. For example, the analysis unit analyzes the user's dietary data and predicts future health risks if there is a deficiency or excess of certain nutrients. Based on these predictions, the service provider can provide preventative advice to the user. For example, it might provide specific advice such as, "Your current eating patterns may increase your risk of osteoporosis due to vitamin D deficiency in the future. Try to consume foods rich in vitamin D." This allows users to manage their diet to prevent future health risks.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk receives a photo of the meal from the user. For example, the user can take a photo of their meal using a device such as a smartphone or tablet and upload it to the application. The reception desk then inputs the uploaded photo into the AI. Step 2: The analysis unit uses AI to analyze the uploaded photos and identify the type and quantity of food. For example, it uses image recognition technology or deep learning technology to identify the type and quantity of food in the photos. The analysis unit can refer to a pre-trained database. Step 3: The estimation unit estimates the calorie and nutrient content based on the type and quantity of food identified by the analysis unit. For example, an algorithm can be used to calculate the calorie and nutrient content based on the type and quantity of food. The estimation unit can retrieve the calorie and nutrient content for each type of food from a database and calculate it according to the quantity. Step 4: The service provider provides health management advice based on the calories and nutrients estimated by the estimation unit. For example, it can generate advice by considering the user's past dietary data and physical characteristics. The service provider delivers the advice to the user through the application. For example, it can send advice to the user using the application's notification function.

[0113] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0116] Each of the multiple elements described above, including the reception unit, analysis unit, estimation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing the user to take a picture of a meal and upload it to the application. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the uploaded picture and identify the type and quantity of food. The estimation unit is implemented by the identification processing unit 290 of the data processing unit 12, which estimates the calorie and nutrient content based on the type and quantity of food identified by the analysis unit. The provision unit is implemented by the control unit 46A of the smart device 14, which provides health management advice based on the calories and nutrients estimated by the estimation unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 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.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the reception unit, analysis unit, estimation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to take a picture of their meal and upload it to the application. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the uploaded photo and identify the type and quantity of food. The estimation unit is implemented by the identification processing unit 290 of the data processing unit 12, which estimates the calorie and nutrient content based on the type and quantity of food identified by the analysis unit. The provision unit is implemented by the control unit 46A of the smart glasses 214, which provides health management advice based on the calories and nutrients estimated by the estimation unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the reception unit, analysis unit, estimation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to take a picture of their meal and upload it to the application. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the uploaded picture and identify the type and quantity of food. The estimation unit is implemented by the identification processing unit 290 of the data processing unit 12, which estimates the calorie and nutrient content based on the type and quantity of food identified by the analysis unit. The provision unit is implemented by the control unit 46A of the headset terminal 314, which provides health management advice based on the calories and nutrients estimated by the estimation unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the 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.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the reception unit, analysis unit, estimation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, allowing the user to take a picture of their meal and upload it to the application. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which uses AI to analyze the uploaded picture and identify the type and quantity of food. The estimation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which estimates the calorie and nutrient content based on the type and quantity of food identified by the analysis unit. The provision unit is implemented, for example, by the control unit 46A of the robot 414, which provides health management advice based on the calories and nutrients estimated by the estimation unit. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0166] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) The reception desk where you upload photos of your meal, An analysis unit analyzes the photos uploaded by the reception unit to identify the type and quantity of food, An estimation unit that estimates calories and nutrients based on the type and amount of food identified by the analysis unit, The system includes a providing unit that provides health management advice based on the calories and nutrients estimated by the estimation unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, The system generates advice based on the user's past dietary data and physical characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Image recognition technology is used to identify the type and quantity of food. The system described in Appendix 1, characterized by the features described herein. (Note 4) The estimation unit, Estimate the calorie and nutrient content based on the type and quantity of food. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Providing advice to users through the application. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Providing advice in a diary style The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of photo uploads based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system analyzes the user's past food photo upload history and selects the optimal upload method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When uploading photos, filtering is performed based on the user's current eating patterns and health status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of photos to upload based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When uploading photos, the system prioritizes uploading highly relevant food photos, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When a user uploads a photo, the system analyzes their social media activity and uploads relevant food photos. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied based on the type and amount of food consumed. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the analysis method is customized according to the time of day and season of the meal. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the geographical distribution of the food will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature on diet to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The estimation unit, The system estimates the user's emotions and adjusts the calorie and nutrient estimation methods based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The estimation unit, During estimation, different estimation algorithms are applied based on the type and amount of food consumed. The system described in Appendix 1, characterized by the features described herein. (Note 21) The estimation unit, During estimation, the estimation method is customized according to meal times and seasons. The system described in Appendix 1, characterized by the features described herein. (Note 22) The estimation unit, It estimates the user's emotions and adjusts how the estimation results are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The estimation unit, During estimation, the geographical distribution of food is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The estimation unit, During estimation, we refer to relevant literature on diet to improve the accuracy of the estimation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing advice, the level of detail in the advice is adjusted by considering the user's past dietary data and physical condition information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing advice, customize the advice based on the user's current health status and goals. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing advice, we take the user's geographical location into consideration to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing advice, we analyze the user's social media activity to provide relevant advice. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk where you upload photos of your meal, An analysis unit analyzes the photos uploaded by the reception unit to identify the type and quantity of food, An estimation unit that estimates calories and nutrients based on the type and amount of food identified by the analysis unit, The system includes a providing unit that provides health management advice based on the calories and nutrients estimated by the estimation unit. A system characterized by the following features.

2. The aforementioned supply unit is, The system generates advice based on the user's past dietary data and physical characteristics. The system according to feature 1.

3. The aforementioned analysis unit, Image recognition technology is used to identify the type and quantity of food. The system according to feature 1.

4. The estimation unit, Estimate the calorie and nutrient content based on the type and quantity of food. The system according to feature 1.

5. The aforementioned supply unit is, Providing advice to users through the application. The system according to feature 1.

6. The aforementioned supply unit is, Providing advice in a diary style The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of photo uploads based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is The system analyzes the user's past food photo upload history and selects the optimal upload method. The system according to feature 1.

9. The aforementioned reception unit is When uploading photos, filtering is performed based on the user's current eating patterns and health status. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of photos to upload based on the estimated emotions. The system according to feature 1.

Images