system

Abstract

The system according to this embodiment aims to provide quick and balanced meal ideas by adjusting dietary recommendations based on individual health indicators. [Solution] The system according to the embodiment comprises a monitoring unit, an adjustment unit, an analysis unit, and a provision unit. The monitoring unit monitors health indicators. The adjustment unit adjusts dietary recommendations based on the data monitored by the monitoring unit. The analysis unit scans meals and performs nutritional analysis. The provision unit provides quick and balanced meal ideas.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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 in 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 conventional technology, dietary recommendations and adjustments based on individual health indicators have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to adjust dietary recommendations based on individual health indicators and provide quick and balanced meal ideas.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, an adjustment unit, an analysis unit, and a serving unit. The monitoring unit monitors health indicators. The adjustment unit adjusts dietary recommendations based on the data monitored by the monitoring unit. The analysis unit scans meals and performs nutritional analysis. The serving unit provides quick and balanced meal ideas. [Effects of the Invention]

[0007] The system according to this embodiment can adjust dietary recommendations based on individual health indicators and provide quick and balanced meal ideas. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls 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 SmartMeal AI system, according to an embodiment of the present invention, is an innovative personalized virtual nutritionist system that provides customized meal plans and real-time nutritional guidance to optimize the user's health. The SmartMeal AI system seamlessly integrates with fitness trackers and wearable devices, continuously monitoring key health indicators while tailoring meal recommendations based on individual goals, preferences, and lifestyles. Users can instantly scan meals for nutritional analysis, track progress, and receive alerts when necessary nutritional adjustments are needed. Ideal for busy individuals, the SmartMeal AI system provides quick and balanced meal ideas suited to busy schedules. By assisting users in data-driven meal choices, the SmartMeal AI system promotes overall health, supports weight management and muscle building, and provides specific nutritional guidance to maintain an individual's healthy lifestyle. This enables the SmartMeal AI system to optimize the user's health and provide meal plans and real-time nutritional guidance tailored to individual goals.

[0029] The SmartMeal AI system according to this embodiment comprises a monitoring unit, an adjustment unit, an analysis unit, and a provision unit. The monitoring unit monitors health indicators. Health indicators include, but are not limited to, heart rate, blood pressure, and blood glucose levels. The monitoring unit acquires data from, for example, fitness trackers and wearable devices and monitors these health indicators in real time. The adjustment unit adjusts meal recommendations based on the data monitored by the monitoring unit. Meal recommendations include, for example, calorie restriction and nutritional balance. The adjustment unit adjusts meal recommendations to match the user's health goals. The analysis unit scans meals and performs nutritional analysis. Nutritional analysis includes, for example, the analysis tools used and the analysis items. The analysis unit performs nutritional analysis by, for example, scanning meals used by the user. The provision unit provides quick and balanced meal ideas. Meal ideas include, for example, recipes and ingredient combinations. The provision unit provides quick and balanced meal ideas tailored to the user's lifestyle. As a result, the SmartMeal AI system according to the embodiment can monitor health indicators, adjust meal recommendations based on data, perform nutritional analysis, and provide ideas for balanced meals.

[0030] The monitoring unit monitors health indicators. These indicators include, but are not limited to, heart rate, blood pressure, and blood glucose levels. The monitoring unit acquires data from, for example, fitness trackers and wearable devices and monitors these health indicators in real time. Specifically, fitness trackers and wearable devices are worn on the user's body and continuously measure data such as heart rate, blood pressure, and blood glucose levels. These devices transmit data to smartphones or cloud servers via Bluetooth® or Wi-Fi, and the monitoring unit receives it. The monitoring unit analyzes the received data in real time and detects abnormal values ​​and sudden fluctuations. For example, if the heart rate suddenly increases or the blood glucose level suddenly drops, the monitoring unit immediately issues an alert and notifies the user. Furthermore, the monitoring unit can also accumulate historical data and analyze long-term health trends. This allows for early detection of changes in the user's health status and the implementation of appropriate measures. The monitoring unit centrally manages the user's health data and can also collaborate with medical institutions and specialists as needed. For example, users can provide their doctors with the results of regular health checkups and receive expert advice. This allows the monitoring department to support users' health management and contribute to maintaining their health.

[0031] The adjustment unit adjusts dietary recommendations based on data monitored by the monitoring unit. Dietary recommendations include, but are not limited to, calorie restriction and nutritional balance. The adjustment unit adjusts dietary recommendations to match the user's health goals, for example. Specifically, it analyzes the user's health indicator data and creates a meal plan that suits their current health status and goals. For example, it recommends a low-carbohydrate diet for users with high blood sugar levels and a diet that does not strain the heart for users with high heart rates. The adjustment unit uses AI to analyze user data and generate an optimal meal plan. Based on past data and statistical information, the AI ​​proposes a meal plan that is best suited to the user's health status and lifestyle. Furthermore, the adjustment unit collects user feedback and continuously improves the accuracy of the meal plan. For example, the user reports the results of following the meal plan, and the adjustment unit revises the meal plan based on that data. This allows the adjustment unit to provide flexible dietary recommendations tailored to the user's health status and support their health maintenance.

[0032] The analysis department scans meals and performs nutritional analysis. Nutritional analysis includes, but is not limited to, the analysis tools and analysis items used. For example, the analysis department performs nutritional analysis when a user scans a meal. Specifically, the user takes a picture of their meal using their smartphone camera and sends it to the analysis department. The analysis department uses image recognition technology to analyze the contents of the meal and identify the nutritional components of each ingredient. For example, it analyzes and provides to the user the nutritional components such as calories, protein, fat, carbohydrates, vitamins, and minerals contained in the meal. Furthermore, the analysis department can accumulate the user's meal history and evaluate their long-term nutritional balance. This allows the user to understand their diet in detail and use this information to improve their nutritional balance. The analysis department uses AI to quickly and accurately analyze the nutritional components of meals and provides feedback to the user. This allows the user to review their daily diet and maintain a healthy eating lifestyle.

[0033] The service provider offers quick and balanced meal ideas. These ideas include, but are not limited to, recipes and ingredient combinations. For example, the service provider offers quick and balanced meal ideas tailored to the user's lifestyle. Specifically, AI generates an optimal meal plan based on the user's health indicator data and eating history. For example, it suggests healthy recipes that can be prepared quickly for busy users, and suggests ingredient combinations that supplement nutrients for users who are deficient in certain nutrients. The service provider also takes into account the user's preferences and allergy information to provide individually customized meal plans. Furthermore, the service provider collects user feedback and continuously improves the accuracy of the meal plans. For example, users report the results of trying the provided recipes, and the service provider revises the recipes based on that data. This allows the service provider to provide flexible meal ideas that are tailored to the user's lifestyle and health condition, supporting their health maintenance.

[0034] The SmartMeal AI system according to this embodiment includes a tracking unit that tracks the user's progress. The tracking unit tracks, for example, changes in the user's weight, increases or decreases in exercise volume, etc. The tracking unit acquires data from, for example, fitness trackers or wearable devices and tracks the user's progress in real time. Some or all of the above-described processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's progress data into the AI ​​and have the AI ​​perform progress analysis. This allows for more effective health management by tracking the user's progress.

[0035] The SmartMeal AI system according to this embodiment includes an alert unit that issues an alert when necessary nutritional adjustments are required. The alert unit issues an alert when nutritional adjustments are required, such as supplementing vitamins or minerals or adjusting calories. The alert unit analyzes the user's health indicator data and issues an alert when necessary nutritional adjustments are required. Some or all of the above-described processing in the alert unit may be performed using AI, or not using AI. For example, the alert unit can input the user's health indicator data into the AI ​​and have the AI ​​determine the need for nutritional adjustments. This allows the user to manage their nutrition appropriately by issuing an alert when necessary nutritional adjustments are required.

[0036] The SmartMeal AI system according to this embodiment includes an integration unit that integrates with a fitness tracker or wearable device. The integration unit, for example, synchronizes data from the fitness tracker or wearable device. The integration unit sets, for example, the data synchronization method and the protocol to be used. Some or all of the above-described processes in the integration unit may be performed using AI, or not using AI. For example, the integration unit can input data acquired from the fitness tracker or wearable device into the AI ​​and have the AI ​​perform the data synchronization. This enables real-time monitoring of health indicators by integrating with the fitness tracker or wearable device.

[0037] The analysis unit performs nutritional analysis by scanning meals with the user. For example, the analysis unit scans the meal with a scanner and saves it as image data. Then, the analysis unit converts the image data into text data using OCR technology. The analysis unit can also take a picture of the meal using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app automatically corrects the image and performs character recognition. The analysis unit can also write the contents of the meal by hand with a dedicated digital pen, and the digital pen's movements are converted into digital data in real time. For example, the movement of the pen is detected by a sensor and saved as text data. This allows users to understand the contents of their meals in detail by scanning them and performing nutritional analysis. Nutritional analysis includes, but is not limited to, the analysis tools and analysis items used. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input scanned meal data into a generative AI and have the generative AI perform the nutritional analysis.

[0038] The adjustment unit adjusts dietary recommendations to match the user's health goals. For example, the adjustment unit adjusts calorie restrictions and nutritional balance based on the user's health goals. For example, the adjustment unit adjusts dietary recommendations to match the user's health goals. Some or all of the above processing in the adjustment unit may be performed using AI, or not using AI. For example, the adjustment unit can input the user's health goal data into the AI ​​and have the AI ​​adjust the dietary recommendations. This makes it possible to manage meals according to individual goals by adjusting dietary recommendations to match the user's health goals.

[0039] The monitoring unit analyzes the user's past health data and selects the optimal monitoring method. For example, the monitoring unit focuses monitoring on specific time periods based on the user's past health data. For example, the monitoring unit analyzes the user's past health data and adjusts the monitoring frequency for specific health indicators. For example, the monitoring unit refers to the user's past health data and selects a monitoring method appropriate to specific events or situations. This allows the optimal monitoring method to be selected by analyzing the user's past health data. Past health data includes, but is not limited to, past diagnostic results and fitness tracker data. Some or all of the above processes in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input the user's past health data into AI and have the AI ​​select the optimal monitoring method.

[0040] The monitoring unit filters health indicators based on the user's current activity level and lifestyle. For example, the monitoring unit adjusts the frequency of monitoring health indicators based on the user's current activity level. For example, the monitoring unit prioritizes monitoring certain health indicators according to the user's lifestyle. For example, the monitoring unit filters health indicator monitoring data based on the user's activity level and lifestyle, collecting only important data. This allows for the collection of only important data by filtering health indicators based on the user's activity level and lifestyle. Activity levels include, but are not limited to, steps taken, exercise time, and calories burned. Lifestyles include, but are not limited to, meal frequency, sleep duration, and smoking habits. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input the user's activity level and lifestyle data into AI and have the AI ​​perform the filtering.

[0041] The monitoring unit, when monitoring health indicators, prioritizes monitoring highly relevant indicators by considering the user's geographical location. For example, if the user is at high altitude, the monitoring unit prioritizes monitoring oxygen concentration and heart rate. If the user is in an urban area, the monitoring unit prioritizes monitoring air quality and noise levels. If the user is exercising, the monitoring unit prioritizes monitoring exercise intensity and calorie consumption. This allows for the prioritization of monitoring highly relevant health indicators by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's geographical location data into AI and have the AI ​​select highly relevant indicators.

[0042] The monitoring unit analyzes the user's social media activity and monitors relevant health indicators when monitoring health indicators. For example, the monitoring unit estimates stress levels from the user's social media activity and monitors stress-related health indicators. For example, the monitoring unit estimates sleep patterns from the user's social media activity and monitors sleep-related health indicators. For example, the monitoring unit estimates eating patterns from the user's social media activity and monitors eating-related health indicators. In this way, relevant health indicators can be monitored by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and comments. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input the user's social media activity data into AI and have the AI ​​select relevant health indicators.

[0043] The adjustment unit adjusts the level of detail of dietary recommendations based on the user's health goals. For example, if the user's goal is weight loss, the adjustment unit provides detailed recommendations for calorie restriction. If the user's goal is muscle gain, the adjustment unit provides detailed recommendations for protein intake. If the user's goal is health maintenance, the adjustment unit provides recommendations for a balanced diet. By adjusting the level of detail of recommendations based on the user's health goals, dietary management tailored to individual goals becomes possible. Health goals include, but are not limited to, weight loss, muscle gain, and normalization of blood pressure. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input the user's health goal data into AI and have AI perform the adjustment of the level of detail of recommendations.

[0044] The adjustment unit applies different recommendation algorithms depending on the user's dietary history when recommending meals. For example, the adjustment unit recommends the optimal meal based on the user's past meal data. For example, if the user's dietary history indicates a deficiency in a particular nutrient, the adjustment unit recommends a meal to supplement that nutrient. For example, the adjustment unit analyzes the user's dietary history and makes recommendations to increase meal variety. By applying different recommendation algorithms depending on the user's dietary history, more appropriate dietary management becomes possible. Dietary history includes, but is not limited to, meal records and calorie intake. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input the user's dietary history data into AI and have the AI ​​perform the application of the recommendation algorithm.

[0045] The adjustment unit determines the priority of meal recommendations based on when the user submits their meals. For example, if the user submits breakfast, the adjustment unit prioritizes recommendations suitable for breakfast. If the user submits lunch, the adjustment unit prioritizes recommendations suitable for lunch. If the user submits dinner, the adjustment unit prioritizes recommendations suitable for dinner. This allows for more appropriate meal management by determining the priority of recommendations based on when the user submits their meals. The timing of meal submission includes, but is not limited to, the time the meal was recorded and the submission deadline. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input the user's meal submission timing data into the AI ​​and have the AI ​​determine the priority of recommendations.

[0046] The adjustment unit adjusts the order of recommendations based on the relevance of the user's meals when making meal recommendations. For example, the adjustment unit prioritizes recommending meals that are highly relevant to meals the user has eaten in the past. For example, if the user's meal history indicates a deficiency in a particular nutrient, the adjustment unit prioritizes recommending meals that supplement that nutrient. For example, the adjustment unit analyzes the user's meal history and prioritizes recommendations to increase meal variety. This allows for more appropriate dietary management by adjusting the order of recommendations based on the relevance of the user's meals. Meal relevance includes, but is not limited to, examples such as nutrient balance and ingredient combinations. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input the user's meal relevance data into AI and have the AI ​​perform the adjustment of the recommendation order.

[0047] The analysis unit adjusts the level of detail of the nutritional analysis based on the user's diet. For example, the analysis unit performs a detailed nutritional analysis based on the contents of the meals consumed by the user. For example, if the analysis unit finds that a particular nutrient is deficient in the user's diet, it performs a detailed analysis of that nutrient. For example, the analysis unit performs a balanced nutritional analysis by referring to the user's diet. This allows for more appropriate nutritional management by adjusting the level of detail of the analysis based on the user's diet. The contents of the diet include, but are not limited to, the types of ingredients and nutrient content. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's diet data into AI and have the AI ​​adjust the level of detail of the analysis.

[0048] The analysis unit applies different analysis algorithms depending on the user's meal category during nutritional analysis. For example, the analysis unit applies the optimal nutritional analysis algorithm based on the category of meals consumed by the user. For example, if the analysis unit finds that a particular nutrient is deficient in the user's meal category, it will focus its analysis on that nutrient. For example, the analysis unit performs a balanced nutritional analysis by referring to the user's meal category. This allows for more appropriate nutritional management by applying different analysis algorithms depending on the user's meal category. Meal categories include, but are not limited to, breakfast, lunch, dinner, and snacks. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's meal category data into AI and have the AI ​​perform the application of the analysis algorithm.

[0049] The analysis unit prioritizes nutritional analysis based on when the user submits their meals. For example, if the user submits breakfast, the analysis unit prioritizes the nutritional analysis related to breakfast. If the user submits lunch, the analysis unit prioritizes the nutritional analysis related to lunch. If the user submits dinner, the analysis unit prioritizes the nutritional analysis related to dinner. By prioritizing analysis based on when the user submits their meals, more appropriate nutritional management becomes possible. The timing of meal submission includes, but is not limited to, the time the meal was recorded and the submission deadline. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's meal submission timing data into AI and have the AI ​​perform the determination of analysis priorities.

[0050] The analysis unit adjusts the order of analysis based on the relevance of the user's meals during nutritional analysis. For example, the analysis unit prioritizes analyzing meals that are highly relevant to meals the user has eaten in the past. For example, if the analysis unit finds that a particular nutrient is deficient based on the user's dietary history, it will focus on analyzing that nutrient. For example, the analysis unit analyzes the user's dietary history and prioritizes analyses that increase the variety of meals. By adjusting the order of analysis based on the relevance of the user's meals, more appropriate nutritional management becomes possible. Dietary relevance includes, but is not limited to, the balance of nutrients and combinations of ingredients. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's dietary relevance data into AI and have the AI ​​perform the adjustment of the order of analysis.

[0051] The service provider adjusts the level of detail in meal ideas based on the user's health goals. For example, if the user's goal is weight loss, the service provider will provide detailed calorie-restricted meal ideas. If the user's goal is muscle gain, the service provider will provide detailed protein intake meal ideas. If the user's goal is health maintenance, the service provider will provide balanced meal ideas. By adjusting the level of detail based on the user's health goals, more appropriate dietary management becomes possible. Health goals include, but are not limited to, weight loss, muscle gain, and normalization of blood pressure. 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 health goal data into AI and have the AI ​​adjust the level of detail of the suggestions.

[0052] The service provider applies different service algorithms depending on the user's eating history when providing meal ideas. For example, the service provider provides optimal meal ideas based on data of meals the user has consumed in the past. For example, if the service provider finds that a specific nutrient is deficient based on the user's eating history, it provides meal ideas to supplement that nutrient. For example, the service provider analyzes the user's eating history and provides meal ideas to increase the variety of meals. By applying different service algorithms depending on the user's eating history, more appropriate dietary management becomes possible. Meal history includes, but is not limited to, meal records and calorie intake. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's eating history data into AI and have the AI ​​perform the application of the service algorithm.

[0053] The service provider prioritizes meal ideas based on when the user submits their meals. For example, if a user submits breakfast, the service provider will prioritize providing meal ideas suitable for breakfast. If a user submits lunch, the service provider will prioritize providing meal ideas suitable for lunch. If a user submits dinner, the service provider will prioritize providing meal ideas suitable for dinner. This allows for more appropriate meal management by prioritizing the provision of ideas based on when the user submits their meals. The submission time of meals includes, but is not limited to, the time the meal was recorded and the submission deadline. 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 the user's meal submission time data into AI and have the AI ​​determine the priority of the suggestions.

[0054] The service provider adjusts the order in which meal ideas are presented based on the relevance of the user's diet. For example, the service provider prioritizes providing meal ideas that are highly relevant to meals the user has eaten in the past. For example, if the service provider finds that a particular nutrient is deficient based on the user's dietary history, it prioritizes providing meal ideas that supplement that nutrient. For example, the service provider analyzes the user's dietary history and prioritizes providing meal ideas that increase the variety of meals. By adjusting the order of presentation based on the relevance of the user's diet, more appropriate dietary management becomes possible. Relevance of meals includes, but is not limited to, the balance of nutrients and combinations of ingredients. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's dietary relevance data into AI and have the AI ​​perform the adjustment of the presentation order.

[0055] The tracking unit selects the optimal tracking method by referring to the user's past health data when tracking progress. For example, the tracking unit focuses tracking on specific time periods based on the user's past health data. For example, the tracking unit analyzes the user's past health data and adjusts the tracking frequency for specific health indicators. For example, the tracking unit refers to the user's past health data and selects a tracking method appropriate to specific events or situations. This allows the optimal tracking method to be selected by referring to the user's past health data. Past health data includes, but is not limited to, past diagnostic results and fitness tracker data. Some or all of the above processing in the tracking unit may be performed using, for example, AI, or not using AI. For example, the tracking unit can input the user's past health data into AI and have the AI ​​select the optimal tracking method.

[0056] The tracking unit selects the optimal tracking method when tracking progress, taking into account the user's geographical location information. For example, if the user is at high altitude, the tracking unit prioritizes tracking oxygen concentration and heart rate. For example, if the user is in an urban area, the tracking unit prioritizes tracking air quality and noise levels. For example, if the user is exercising, the tracking unit prioritizes tracking exercise intensity and calorie consumption. This allows the system to select the optimal tracking method by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the tracking unit may be performed using, for example, AI, or not using AI. For example, the tracking unit can input the user's geographical location data into AI and have the AI ​​select the optimal tracking method.

[0057] The alert unit selects the most appropriate alert content by referring to the user's past health data when issuing an alert. For example, the alert unit may issue alerts more frequently during specific time periods based on the user's past health data. For example, the alert unit may analyze the user's past health data and adjust the alert content for specific health indicators. For example, the alert unit may refer to the user's past health data and select alert content appropriate to specific events or situations. This allows the system to select the most appropriate alert content by referring to the user's past health data. Past health data includes, but is not limited to, past diagnostic results and fitness tracker data. Some or all of the above-described processes in the alert unit may be performed using, for example, AI, or not. For example, the alert unit may input the user's past health data into an AI and have the AI ​​select the most appropriate alert content.

[0058] The alert unit selects the most appropriate alert content when issuing an alert, taking into account the user's geographical location. For example, if the user is at high altitude, the alert unit prioritizes issuing alerts regarding oxygen concentration and heart rate. If the user is in an urban area, the alert unit prioritizes issuing alerts regarding air quality and noise levels. If the user is exercising, the alert unit prioritizes issuing alerts regarding exercise intensity and calorie consumption. This allows the system to select the most appropriate alert content by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the alert unit may be performed using, for example, AI, or not. For example, the alert unit can input the user's geographical location data into an AI and have the AI ​​select the most appropriate alert content.

[0059] The integration unit selects the optimal integration method by referring to the user's past device usage history during integration. For example, the integration unit integrates data with a focus on specific time periods based on the user's past device usage history. For example, the integration unit analyzes the user's past device usage history and adjusts the integration method for specific health indicators. For example, the integration unit refers to the user's past device usage history and selects an integration method according to specific events or situations. This allows the optimal integration method to be selected by referring to the user's past device usage history. Past device usage history includes, but is not limited to, device usage time and frequency. Some or all of the above processes in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can input the user's past device usage history data into AI and have the AI ​​select the optimal integration method.

[0060] The integration unit selects the optimal integration method during integration, taking into account the user's device information. For example, if the user is using a smartphone, the integration unit provides a data integration method optimized for smartphones. For example, if the user is using a tablet, the integration unit provides a data integration method optimized for tablets. For example, if the user is using a smartwatch, the integration unit provides a data integration method optimized for smartwatches. This allows the optimal integration method to be selected by taking into account the user's device information. Device information includes, but is not limited to, the type of device, its functions, and its usage. Some or all of the above processing in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can input the user's device information data into AI and have AI select the optimal integration method.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The monitoring unit can also analyze the user's past health data and select the optimal monitoring method. For example, it can focus monitoring on specific time periods based on the user's past health data. It can analyze the user's past health data and adjust the monitoring frequency for specific health indicators. It can refer to the user's past health data and select a monitoring method appropriate to specific events or situations. This allows for the selection of the optimal monitoring method by analyzing the user's past health data. Past health data includes, but is not limited to, past diagnostic results and fitness tracker data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not. For example, the monitoring unit can input the user's past health data into AI and have the AI ​​select the optimal monitoring method.

[0063] The integration unit can also select the optimal integration method by considering the user's device information during integration. For example, if the user is using a smartphone, it can provide a data integration method optimized for smartphones. If the user is using a tablet, it can provide a data integration method optimized for tablets. If the user is using a smartwatch, it can provide a data integration method optimized for smartwatches. This allows for the selection of the optimal integration method by considering the user's device information. Device information includes, but is not limited to, the type of device, its functions, and its usage. Some or all of the above-described processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's device information data into AI and have AI select the optimal integration method.

[0064] The tracking unit can also select the optimal tracking method by referring to the user's past health data when tracking progress. For example, it can focus tracking on specific time periods based on the user's past health data. It can analyze the user's past health data and adjust the tracking frequency for specific health indicators. It can refer to the user's past health data and select a tracking method appropriate for specific events or situations. This allows the optimal tracking method to be selected by referring to the user's past health data. Past health data includes, but is not limited to, past diagnostic results and fitness tracker data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input the user's past health data into AI and have the AI ​​select the optimal tracking method.

[0065] The service provider can also adjust the level of detail provided when offering meal ideas based on the user's health goals. For example, if the user's goal is weight loss, detailed calorie-restricted meal ideas can be provided. If the user's goal is muscle gain, detailed protein intake meal ideas can be provided. If the user's goal is maintaining health, balanced meal ideas can be provided. By adjusting the level of detail based on the user's health goals, more appropriate dietary management becomes possible. Health goals include, but are not limited to, weight loss, muscle gain, and normalization of blood pressure. Some or all of the processing described above 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 goal data into AI and have the AI ​​adjust the level of detail provided.

[0066] The monitoring unit can also filter health indicators based on the user's current activity level and lifestyle. For example, it can adjust the frequency of health indicator monitoring based on the user's current activity level. It can prioritize monitoring of specific health indicators according to the user's lifestyle. It can filter health indicator monitoring data based on the user's activity level and lifestyle to collect only important data. This allows for the collection of only important data by filtering health indicators based on the user's activity level and lifestyle. Activity levels include, but are not limited to, steps taken, exercise time, and calories burned. Lifestyles include, but are not limited to, meal frequency, sleep duration, and smoking habits. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input the user's activity level and lifestyle data into AI and have the AI ​​perform the filtering.

[0067] The adjustment unit can also apply different recommendation algorithms depending on the user's dietary history when recommending meals. For example, it can recommend the optimal meal based on the user's past meal data. If the user's dietary history indicates a deficiency in a particular nutrient, it can recommend meals that supplement that nutrient. It can also analyze the user's dietary history and make recommendations to increase the variety of meals. By applying different recommendation algorithms depending on the user's dietary history, more appropriate dietary management becomes possible. Dietary history includes, but is not limited to, meal records and calorie intake. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's dietary history data into AI and have AI perform the application of recommendation algorithms.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The monitoring unit monitors health indicators. These indicators include, for example, heart rate, blood pressure, and blood glucose levels. The monitoring unit acquires data from fitness trackers and wearable devices and monitors these health indicators in real time. Step 2: The adjustment unit adjusts the dietary recommendations based on the data monitored by the monitoring unit. These recommendations include calorie restriction and nutritional balance. The adjustment unit adjusts the dietary recommendations to match the user's health goals. Step 3: The analysis unit scans the meal and performs a nutritional analysis. This includes the analysis tools and analysis items to be used. The analysis unit performs the nutritional analysis after the user scans the meal. Step 4: The service provider offers quick and balanced meal ideas. These ideas include recipes and ingredient combinations. The service provider offers quick and balanced meal ideas tailored to the user's lifestyle.

[0070] (Example of form 2) The SmartMeal AI system, according to an embodiment of the present invention, is an innovative personalized virtual nutritionist system that provides customized meal plans and real-time nutritional guidance to optimize the user's health. The SmartMeal AI system seamlessly integrates with fitness trackers and wearable devices, continuously monitoring key health indicators while tailoring meal recommendations based on individual goals, preferences, and lifestyles. Users can instantly scan meals for nutritional analysis, track progress, and receive alerts when necessary nutritional adjustments are needed. Ideal for busy individuals, the SmartMeal AI system provides quick and balanced meal ideas suited to busy schedules. By assisting users in data-driven meal choices, the SmartMeal AI system promotes overall health, supports weight management and muscle building, and provides specific nutritional guidance to maintain an individual's healthy lifestyle. This enables the SmartMeal AI system to optimize the user's health and provide meal plans and real-time nutritional guidance tailored to individual goals.

[0071] The SmartMeal AI system according to this embodiment comprises a monitoring unit, an adjustment unit, an analysis unit, and a provision unit. The monitoring unit monitors health indicators. Health indicators include, but are not limited to, heart rate, blood pressure, and blood glucose levels. The monitoring unit acquires data from, for example, fitness trackers and wearable devices and monitors these health indicators in real time. The adjustment unit adjusts meal recommendations based on the data monitored by the monitoring unit. Meal recommendations include, for example, calorie restriction and nutritional balance. The adjustment unit adjusts meal recommendations to match the user's health goals. The analysis unit scans meals and performs nutritional analysis. Nutritional analysis includes, for example, the analysis tools used and the analysis items. The analysis unit performs nutritional analysis by, for example, scanning meals used by the user. The provision unit provides quick and balanced meal ideas. Meal ideas include, for example, recipes and ingredient combinations. The provision unit provides quick and balanced meal ideas tailored to the user's lifestyle. As a result, the SmartMeal AI system according to the embodiment can monitor health indicators, adjust meal recommendations based on data, perform nutritional analysis, and provide ideas for balanced meals.

[0072] The monitoring unit monitors health indicators. These indicators include, but are not limited to, heart rate, blood pressure, and blood glucose levels. The monitoring unit acquires data from, for example, fitness trackers and wearable devices and monitors these health indicators in real time. Specifically, fitness trackers and wearable devices are worn on the user's body and continuously measure data such as heart rate, blood pressure, and blood glucose levels. These devices transmit data to smartphones or cloud servers via Bluetooth or Wi-Fi, and the monitoring unit receives it. The monitoring unit analyzes the received data in real time and detects abnormal values ​​and sudden fluctuations. For example, if the heart rate suddenly increases or blood glucose levels suddenly drop, the monitoring unit immediately issues an alert and notifies the user. Furthermore, the monitoring unit can also accumulate historical data and analyze long-term health trends. This allows for early detection of changes in the user's health status and the implementation of appropriate measures. The monitoring unit centrally manages the user's health data and can also collaborate with medical institutions and specialists as needed. For example, it can provide the results of regular health checkups to a doctor and receive expert advice. This allows the monitoring unit to support users' health management and contribute to maintaining their health.

[0073] The adjustment unit adjusts dietary recommendations based on data monitored by the monitoring unit. Dietary recommendations include, but are not limited to, calorie restriction and nutritional balance. The adjustment unit adjusts dietary recommendations to match the user's health goals, for example. Specifically, it analyzes the user's health indicator data and creates a meal plan that suits their current health status and goals. For example, it recommends a low-carbohydrate diet for users with high blood sugar levels and a diet that does not strain the heart for users with high heart rates. The adjustment unit uses AI to analyze user data and generate an optimal meal plan. Based on past data and statistical information, the AI ​​proposes a meal plan that is best suited to the user's health status and lifestyle. Furthermore, the adjustment unit collects user feedback and continuously improves the accuracy of the meal plan. For example, the user reports the results of following the meal plan, and the adjustment unit revises the meal plan based on that data. This allows the adjustment unit to provide flexible dietary recommendations tailored to the user's health status and support their health maintenance.

[0074] The analysis department scans meals and performs nutritional analysis. Nutritional analysis includes, but is not limited to, the analysis tools and analysis items used. For example, the analysis department performs nutritional analysis when a user scans a meal. Specifically, the user takes a picture of their meal using their smartphone camera and sends it to the analysis department. The analysis department uses image recognition technology to analyze the contents of the meal and identify the nutritional components of each ingredient. For example, it analyzes and provides to the user the nutritional components such as calories, protein, fat, carbohydrates, vitamins, and minerals contained in the meal. Furthermore, the analysis department can accumulate the user's meal history and evaluate their long-term nutritional balance. This allows the user to understand their diet in detail and use this information to improve their nutritional balance. The analysis department uses AI to quickly and accurately analyze the nutritional components of meals and provides feedback to the user. This allows the user to review their daily diet and maintain a healthy eating lifestyle.

[0075] The service provider offers quick and balanced meal ideas. These ideas include, but are not limited to, recipes and ingredient combinations. For example, the service provider offers quick and balanced meal ideas tailored to the user's lifestyle. Specifically, AI generates an optimal meal plan based on the user's health indicator data and eating history. For example, it suggests healthy recipes that can be prepared quickly for busy users, and suggests ingredient combinations that supplement nutrients for users who are deficient in certain nutrients. The service provider also takes into account the user's preferences and allergy information to provide individually customized meal plans. Furthermore, the service provider collects user feedback and continuously improves the accuracy of the meal plans. For example, users report the results of trying the provided recipes, and the service provider revises the recipes based on that data. This allows the service provider to provide flexible meal ideas that are tailored to the user's lifestyle and health condition, supporting their health maintenance.

[0076] The SmartMeal AI system according to this embodiment includes a tracking unit that tracks the user's progress. The tracking unit tracks, for example, changes in the user's weight, increases or decreases in exercise volume, etc. The tracking unit acquires data from, for example, fitness trackers or wearable devices and tracks the user's progress in real time. Some or all of the above-described processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's progress data into the AI ​​and have the AI ​​perform progress analysis. This allows for more effective health management by tracking the user's progress.

[0077] The SmartMeal AI system according to this embodiment includes an alert unit that issues an alert when necessary nutritional adjustments are required. The alert unit issues an alert when nutritional adjustments are required, such as supplementing vitamins or minerals or adjusting calories. The alert unit analyzes the user's health indicator data and issues an alert when necessary nutritional adjustments are required. Some or all of the above-described processing in the alert unit may be performed using AI, or not using AI. For example, the alert unit can input the user's health indicator data into the AI ​​and have the AI ​​determine the need for nutritional adjustments. This allows the user to manage their nutrition appropriately by issuing an alert when necessary nutritional adjustments are required.

[0078] The SmartMeal AI system according to this embodiment includes an integration unit that integrates with a fitness tracker or wearable device. The integration unit, for example, synchronizes data from the fitness tracker or wearable device. The integration unit sets, for example, the data synchronization method and the protocol to be used. Some or all of the above-described processes in the integration unit may be performed using AI, or not using AI. For example, the integration unit can input data acquired from the fitness tracker or wearable device into the AI ​​and have the AI ​​perform the data synchronization. This enables real-time monitoring of health indicators by integrating with the fitness tracker or wearable device.

[0079] The analysis unit performs nutritional analysis by scanning meals with the user. For example, the analysis unit scans the meal with a scanner and saves it as image data. Then, the analysis unit converts the image data into text data using OCR technology. The analysis unit can also take a picture of the meal using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app automatically corrects the image and performs character recognition. The analysis unit can also write the contents of the meal by hand with a dedicated digital pen, and the digital pen's movements are converted into digital data in real time. For example, the movement of the pen is detected by a sensor and saved as text data. This allows users to understand the contents of their meals in detail by scanning them and performing nutritional analysis. Nutritional analysis includes, but is not limited to, the analysis tools and analysis items used. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or not using generative AI. For example, the analysis unit can input scanned meal data into a generative AI and have the generative AI perform the nutritional analysis.

[0080] The adjustment unit adjusts dietary recommendations to match the user's health goals. For example, the adjustment unit adjusts calorie restrictions and nutritional balance based on the user's health goals. For example, the adjustment unit adjusts dietary recommendations to match the user's health goals. Some or all of the above processing in the adjustment unit may be performed using AI, or not using AI. For example, the adjustment unit can input the user's health goal data into the AI ​​and have the AI ​​adjust the dietary recommendations. This makes it possible to manage meals according to individual goals by adjusting dietary recommendations to match the user's health goals.

[0081] The monitoring unit estimates the user's emotions and adjusts the monitoring frequency of health indicators based on the estimated emotions. For example, if the user is stressed, the monitoring unit increases the monitoring frequency of health indicators and collects more detailed data. For example, if the user is relaxed, the monitoring unit decreases the monitoring frequency of health indicators and collects only the minimum necessary data. For example, if the user is tired, the monitoring unit adjusts the monitoring frequency of health indicators to reduce the user's burden. This allows for more appropriate health management by adjusting the monitoring frequency of health indicators based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The monitoring unit analyzes the user's past health data and selects the optimal monitoring method. For example, the monitoring unit focuses monitoring on specific time periods based on the user's past health data. For example, the monitoring unit analyzes the user's past health data and adjusts the monitoring frequency for specific health indicators. For example, the monitoring unit refers to the user's past health data and selects a monitoring method appropriate to specific events or situations. This allows the optimal monitoring method to be selected by analyzing the user's past health data. Past health data includes, but is not limited to, past diagnostic results and fitness tracker data. Some or all of the above processes in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input the user's past health data into AI and have the AI ​​select the optimal monitoring method.

[0083] The monitoring unit filters health indicators based on the user's current activity level and lifestyle. For example, the monitoring unit adjusts the frequency of monitoring health indicators based on the user's current activity level. For example, the monitoring unit prioritizes monitoring certain health indicators according to the user's lifestyle. For example, the monitoring unit filters health indicator monitoring data based on the user's activity level and lifestyle, collecting only important data. This allows for the collection of only important data by filtering health indicators based on the user's activity level and lifestyle. Activity levels include, but are not limited to, steps taken, exercise time, and calories burned. Lifestyles include, but are not limited to, meal frequency, sleep duration, and smoking habits. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input the user's activity level and lifestyle data into AI and have the AI ​​perform the filtering.

[0084] The monitoring unit estimates the user's emotions and determines the priority of health indicators to monitor based on the estimated user emotions. For example, if the user is stressed, the monitoring unit prioritizes monitoring stress-related health indicators. For example, if the user is relaxed, the monitoring unit prioritizes monitoring relaxation-related health indicators. For example, if the user is tired, the monitoring unit prioritizes monitoring fatigue-related health indicators. This enables more appropriate health management by prioritizing health indicators based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The monitoring unit, when monitoring health indicators, prioritizes monitoring highly relevant indicators by considering the user's geographical location. For example, if the user is at high altitude, the monitoring unit prioritizes monitoring oxygen concentration and heart rate. If the user is in an urban area, the monitoring unit prioritizes monitoring air quality and noise levels. If the user is exercising, the monitoring unit prioritizes monitoring exercise intensity and calorie consumption. This allows for the prioritization of monitoring highly relevant health indicators by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can input the user's geographical location data into AI and have the AI ​​select highly relevant indicators.

[0086] The monitoring unit analyzes the user's social media activity and monitors relevant health indicators when monitoring health indicators. For example, the monitoring unit estimates stress levels from the user's social media activity and monitors stress-related health indicators. For example, the monitoring unit estimates sleep patterns from the user's social media activity and monitors sleep-related health indicators. For example, the monitoring unit estimates eating patterns from the user's social media activity and monitors eating-related health indicators. In this way, relevant health indicators can be monitored by analyzing the user's social media activity. Social media activity includes, but is not limited to, posts, the number of likes, and comments. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input the user's social media activity data into AI and have the AI ​​select relevant health indicators.

[0087] The adjustment unit estimates the user's emotions and adjusts meal recommendations based on the estimated emotions. For example, if the user is stressed, the adjustment unit recommends a meal containing ingredients that have a relaxing effect. For example, if the user is relaxed, the adjustment unit recommends a nutritionally balanced meal. For example, if the user is tired, the adjustment unit recommends a meal suitable for energy replenishment. This allows for more appropriate dietary management by adjusting meal recommendations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The adjustment unit adjusts the level of detail of dietary recommendations based on the user's health goals. For example, if the user's goal is weight loss, the adjustment unit provides detailed recommendations for calorie restriction. If the user's goal is muscle gain, the adjustment unit provides detailed recommendations for protein intake. If the user's goal is health maintenance, the adjustment unit provides recommendations for a balanced diet. By adjusting the level of detail of recommendations based on the user's health goals, dietary management tailored to individual goals becomes possible. Health goals include, but are not limited to, weight loss, muscle gain, and normalization of blood pressure. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input the user's health goal data into AI and have AI perform the adjustment of the level of detail of recommendations.

[0089] The adjustment unit applies different recommendation algorithms depending on the user's dietary history when recommending meals. For example, the adjustment unit recommends the optimal meal based on the user's past meal data. For example, if the user's dietary history indicates a deficiency in a particular nutrient, the adjustment unit recommends a meal to supplement that nutrient. For example, the adjustment unit analyzes the user's dietary history and makes recommendations to increase meal variety. By applying different recommendation algorithms depending on the user's dietary history, more appropriate dietary management becomes possible. Dietary history includes, but is not limited to, meal records and calorie intake. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input the user's dietary history data into AI and have the AI ​​perform the application of the recommendation algorithm.

[0090] The adjustment unit estimates the user's emotions and adjusts the length of recommendations based on the estimated emotions. For example, if the user is in a hurry, the adjustment unit provides short, concise recommendations. If the user is relaxed, the adjustment unit provides longer recommendations with detailed explanations. If the user is excited, the adjustment unit provides recommendations with visually stimulating effects. By adjusting the length of recommendations based on the user's emotions, more appropriate meal management becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the adjustment unit may be performed using AI, or not using AI. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The adjustment unit determines the priority of meal recommendations based on when the user submits their meals. For example, if the user submits breakfast, the adjustment unit prioritizes recommendations suitable for breakfast. If the user submits lunch, the adjustment unit prioritizes recommendations suitable for lunch. If the user submits dinner, the adjustment unit prioritizes recommendations suitable for dinner. This allows for more appropriate meal management by determining the priority of recommendations based on when the user submits their meals. The timing of meal submission includes, but is not limited to, the time the meal was recorded and the submission deadline. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input the user's meal submission timing data into the AI ​​and have the AI ​​determine the priority of recommendations.

[0092] The adjustment unit adjusts the order of recommendations based on the relevance of the user's meals when making meal recommendations. For example, the adjustment unit prioritizes recommending meals that are highly relevant to meals the user has eaten in the past. For example, if the user's meal history indicates a deficiency in a particular nutrient, the adjustment unit prioritizes recommending meals that supplement that nutrient. For example, the adjustment unit analyzes the user's meal history and prioritizes recommendations to increase meal variety. This allows for more appropriate dietary management by adjusting the order of recommendations based on the relevance of the user's meals. Meal relevance includes, but is not limited to, examples such as nutrient balance and ingredient combinations. Some or all of the above processing in the adjustment unit may be performed using, for example, AI, or not using AI. For example, the adjustment unit can input the user's meal relevance data into AI and have the AI ​​perform the adjustment of the recommendation order.

[0093] The analysis unit estimates the user's emotions and adjusts the nutritional analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit will focus on analyzing nutrients that are effective in reducing stress. For example, if the user is relaxed, the analysis unit will analyze the overall nutritional balance. For example, if the user is tired, the analysis unit will focus on analyzing nutrients suitable for energy replenishment. This allows for more appropriate nutritional management by adjusting the nutritional analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The analysis unit adjusts the level of detail of the nutritional analysis based on the user's diet. For example, the analysis unit performs a detailed nutritional analysis based on the contents of the meals consumed by the user. For example, if the analysis unit finds that a particular nutrient is deficient in the user's diet, it performs a detailed analysis of that nutrient. For example, the analysis unit performs a balanced nutritional analysis by referring to the user's diet. This allows for more appropriate nutritional management by adjusting the level of detail of the analysis based on the user's diet. The contents of the diet include, but are not limited to, the types of ingredients and nutrient content. Some or all of the above processes in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's diet data into AI and have the AI ​​adjust the level of detail of the analysis.

[0095] The analysis unit applies different analysis algorithms depending on the user's meal category during nutritional analysis. For example, the analysis unit applies the optimal nutritional analysis algorithm based on the category of meals consumed by the user. For example, if the analysis unit finds that a particular nutrient is deficient in the user's meal category, it will focus its analysis on that nutrient. For example, the analysis unit performs a balanced nutritional analysis by referring to the user's meal category. This allows for more appropriate nutritional management by applying different analysis algorithms depending on the user's meal category. Meal categories include, but are not limited to, breakfast, lunch, dinner, and snacks. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's meal category data into AI and have the AI ​​perform the application of the analysis algorithm.

[0096] The analysis unit estimates the user's emotions and adjusts the display method of the nutritional analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit displays simple and easy-to-read results. For example, if the user is relaxed, the analysis unit displays detailed nutritional analysis results. For example, if the user is tired, the analysis unit focuses on displaying results related to energy replenishment. By adjusting the display method of the nutritional analysis results based on the user's emotions, more appropriate nutritional management becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The analysis unit prioritizes nutritional analysis based on when the user submits their meals. For example, if the user submits breakfast, the analysis unit prioritizes the nutritional analysis related to breakfast. If the user submits lunch, the analysis unit prioritizes the nutritional analysis related to lunch. If the user submits dinner, the analysis unit prioritizes the nutritional analysis related to dinner. By prioritizing analysis based on when the user submits their meals, more appropriate nutritional management becomes possible. The timing of meal submission includes, but is not limited to, the time the meal was recorded and the submission deadline. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's meal submission timing data into AI and have the AI ​​perform the determination of analysis priorities.

[0098] The analysis unit adjusts the order of analysis based on the relevance of the user's meals during nutritional analysis. For example, the analysis unit prioritizes analyzing meals that are highly relevant to meals the user has eaten in the past. For example, if the analysis unit finds that a particular nutrient is deficient based on the user's dietary history, it will focus on analyzing that nutrient. For example, the analysis unit analyzes the user's dietary history and prioritizes analyses that increase the variety of meals. By adjusting the order of analysis based on the relevance of the user's meals, more appropriate nutritional management becomes possible. Dietary relevance includes, but is not limited to, the balance of nutrients and combinations of ingredients. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the user's dietary relevance data into AI and have the AI ​​perform the adjustment of the order of analysis.

[0099] The service provider estimates the user's emotions and adjusts the method of providing meal ideas based on the estimated emotions. For example, if the user is stressed, the service provider provides meal ideas that include ingredients with relaxing effects. For example, if the user is relaxed, the service provider provides nutritionally balanced meal ideas. For example, if the user is tired, the service provider provides meal ideas suitable for energy replenishment. By adjusting the method of providing meal ideas based on the user's emotions, more appropriate dietary management becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The service provider adjusts the level of detail in meal ideas based on the user's health goals. For example, if the user's goal is weight loss, the service provider will provide detailed calorie-restricted meal ideas. If the user's goal is muscle gain, the service provider will provide detailed protein intake meal ideas. If the user's goal is health maintenance, the service provider will provide balanced meal ideas. By adjusting the level of detail based on the user's health goals, more appropriate dietary management becomes possible. Health goals include, but are not limited to, weight loss, muscle gain, and normalization of blood pressure. 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 health goal data into AI and have the AI ​​adjust the level of detail of the suggestions.

[0101] The service provider applies different service algorithms depending on the user's eating history when providing meal ideas. For example, the service provider provides optimal meal ideas based on data of meals the user has consumed in the past. For example, if the service provider finds that a specific nutrient is deficient based on the user's eating history, it provides meal ideas to supplement that nutrient. For example, the service provider analyzes the user's eating history and provides meal ideas to increase the variety of meals. By applying different service algorithms depending on the user's eating history, more appropriate dietary management becomes possible. Meal history includes, but is not limited to, meal records and calorie intake. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's eating history data into AI and have the AI ​​perform the application of the service algorithm.

[0102] The service provider estimates the user's emotions and prioritizes meal ideas based on the estimated emotions. For example, if the user is stressed, the service provider prioritizes meal ideas that include ingredients with relaxing effects. For example, if the user is relaxed, the service provider prioritizes meal ideas that are nutritionally balanced. For example, if the user is tired, the service provider prioritizes meal ideas that are suitable for energy replenishment. This allows for more appropriate dietary management by prioritizing meal ideas based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, 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 service provider may be performed using AI, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The service provider prioritizes meal ideas based on when the user submits their meals. For example, if a user submits breakfast, the service provider will prioritize providing meal ideas suitable for breakfast. If a user submits lunch, the service provider will prioritize providing meal ideas suitable for lunch. If a user submits dinner, the service provider will prioritize providing meal ideas suitable for dinner. This allows for more appropriate meal management by prioritizing the provision of ideas based on when the user submits their meals. The submission time of meals includes, but is not limited to, the time the meal was recorded and the submission deadline. 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 the user's meal submission time data into AI and have the AI ​​determine the priority of the suggestions.

[0104] The service provider adjusts the order in which meal ideas are presented based on the relevance of the user's diet. For example, the service provider prioritizes providing meal ideas that are highly relevant to meals the user has eaten in the past. For example, if the service provider finds that a particular nutrient is deficient based on the user's dietary history, it prioritizes providing meal ideas that supplement that nutrient. For example, the service provider analyzes the user's dietary history and prioritizes providing meal ideas that increase the variety of meals. By adjusting the order of presentation based on the relevance of the user's diet, more appropriate dietary management becomes possible. Relevance of meals includes, but is not limited to, the balance of nutrients and combinations of ingredients. Some or all of the above processing in the service provider may be performed using, for example, AI, or not using AI. For example, the service provider can input the user's dietary relevance data into AI and have the AI ​​perform the adjustment of the presentation order.

[0105] The tracking unit estimates the user's emotions and adjusts the progress tracking method based on the estimated user emotions. For example, if the user is stressed, the tracking unit increases the frequency of progress tracking and collects more detailed data. For example, if the user is relaxed, the tracking unit decreases the frequency of progress tracking and collects only the minimum necessary data. For example, if the user is tired, the tracking unit adjusts the frequency of progress tracking to reduce the user's burden. This allows for more appropriate health management by adjusting the progress tracking method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The tracking unit selects the optimal tracking method by referring to the user's past health data when tracking progress. For example, the tracking unit focuses tracking on specific time periods based on the user's past health data. For example, the tracking unit analyzes the user's past health data and adjusts the tracking frequency for specific health indicators. For example, the tracking unit refers to the user's past health data and selects a tracking method appropriate to specific events or situations. This allows the optimal tracking method to be selected by referring to the user's past health data. Past health data includes, but is not limited to, past diagnostic results and fitness tracker data. Some or all of the above processing in the tracking unit may be performed using, for example, AI, or not using AI. For example, the tracking unit can input the user's past health data into AI and have the AI ​​select the optimal tracking method.

[0107] The tracking unit estimates the user's emotions and determines the priority of progress based on the estimated user emotions. For example, if the user is stressed, the tracking unit prioritizes tracking stress-related progress. For example, if the user is relaxed, the tracking unit prioritizes tracking relaxation-related progress. For example, if the user is tired, the tracking unit prioritizes tracking fatigue-related progress. This allows for more appropriate health management by prioritizing progress based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0108] The tracking unit selects the optimal tracking method when tracking progress, taking into account the user's geographical location information. For example, if the user is at high altitude, the tracking unit prioritizes tracking oxygen concentration and heart rate. For example, if the user is in an urban area, the tracking unit prioritizes tracking air quality and noise levels. For example, if the user is exercising, the tracking unit prioritizes tracking exercise intensity and calorie consumption. This allows the system to select the optimal tracking method by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the tracking unit may be performed using, for example, AI, or not using AI. For example, the tracking unit can input the user's geographical location data into AI and have the AI ​​select the optimal tracking method.

[0109] The alert unit estimates the user's emotions and adjusts the content of the alerts based on the estimated emotions. For example, if the user is stressed, the alert unit prioritizes issuing alerts related to stress reduction. For example, if the user is relaxed, the alert unit prioritizes issuing alerts related to overall health maintenance. For example, if the user is tired, the alert unit prioritizes issuing alerts related to energy replenishment. This allows for more appropriate health management by adjusting the content of alerts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0110] The alert unit selects the most appropriate alert content by referring to the user's past health data when issuing an alert. For example, the alert unit may issue alerts more frequently during specific time periods based on the user's past health data. For example, the alert unit may analyze the user's past health data and adjust the alert content for specific health indicators. For example, the alert unit may refer to the user's past health data and select alert content appropriate to specific events or situations. This allows the system to select the most appropriate alert content by referring to the user's past health data. Past health data includes, but is not limited to, past diagnostic results and fitness tracker data. Some or all of the above-described processes in the alert unit may be performed using, for example, AI, or not. For example, the alert unit may input the user's past health data into an AI and have the AI ​​select the most appropriate alert content.

[0111] The alert unit estimates the user's emotions and determines the priority of alerts based on the estimated emotions. For example, if the user is stressed, the alert unit will prioritize issuing alerts related to stress reduction. For example, if the user is relaxed, the alert unit will prioritize issuing alerts related to overall health maintenance. For example, if the user is tired, the alert unit will prioritize issuing alerts related to energy replenishment. This allows for more appropriate health management by prioritizing alerts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0112] The alert unit selects the most appropriate alert content when issuing an alert, taking into account the user's geographical location. For example, if the user is at high altitude, the alert unit prioritizes issuing alerts regarding oxygen concentration and heart rate. If the user is in an urban area, the alert unit prioritizes issuing alerts regarding air quality and noise levels. If the user is exercising, the alert unit prioritizes issuing alerts regarding exercise intensity and calorie consumption. This allows the system to select the most appropriate alert content by considering the user's geographical location. Geographical location information includes, but is not limited to, GPS data and location services. Some or all of the processing described above in the alert unit may be performed using, for example, AI, or not. For example, the alert unit can input the user's geographical location data into an AI and have the AI ​​select the most appropriate alert content.

[0113] The integration unit estimates the user's emotions and adjusts the integration method based on the estimated user emotions. For example, if the user is stressed, the integration unit prioritizes integrating data related to stress reduction. For example, if the user is relaxed, the integration unit prioritizes integrating data related to overall health maintenance. For example, if the user is tired, the integration unit prioritizes integrating data related to energy replenishment. This allows for more appropriate data integration by adjusting the integration method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0114] The integration unit selects the optimal integration method by referring to the user's past device usage history during integration. For example, the integration unit integrates data with a focus on specific time periods based on the user's past device usage history. For example, the integration unit analyzes the user's past device usage history and adjusts the integration method for specific health indicators. For example, the integration unit refers to the user's past device usage history and selects an integration method according to specific events or situations. This allows the optimal integration method to be selected by referring to the user's past device usage history. Past device usage history includes, but is not limited to, device usage time and frequency. Some or all of the above processes in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can input the user's past device usage history data into AI and have the AI ​​select the optimal integration method.

[0115] The integration unit estimates the user's emotions and determines the integration priority based on the estimated user emotions. For example, if the user is stressed, the integration unit prioritizes integrating data related to stress reduction. For example, if the user is relaxed, the integration unit prioritizes integrating data related to overall health maintenance. For example, if the user is tired, the integration unit prioritizes integrating data related to energy replenishment. This allows for more appropriate data integration by determining the integration priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 integration unit may be performed using AI, for example, or not using AI. For example, the integration unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0116] The integration unit selects the optimal integration method during integration, taking into account the user's device information. For example, if the user is using a smartphone, the integration unit provides a data integration method optimized for smartphones. For example, if the user is using a tablet, the integration unit provides a data integration method optimized for tablets. For example, if the user is using a smartwatch, the integration unit provides a data integration method optimized for smartwatches. This allows the optimal integration method to be selected by taking into account the user's device information. Device information includes, but is not limited to, the type of device, its functions, and its usage. Some or all of the above processing in the integration unit may be performed using, for example, AI, or not using AI. For example, the integration unit can input the user's device information data into AI and have AI select the optimal integration method.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] The service provider can also estimate the user's emotions and adjust the way meal ideas are provided based on the estimated emotions. For example, if the user is stressed, it can provide meal ideas that include ingredients with relaxing effects. If the user is relaxed, it can provide nutritionally balanced meal ideas. If the user is tired, it can provide meal ideas suitable for energy replenishment. By adjusting the way meal ideas are provided based on the user's emotions, more appropriate dietary management becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0119] The monitoring unit can also analyze the user's past health data and select the optimal monitoring method. For example, it can focus monitoring on specific time periods based on the user's past health data. It can analyze the user's past health data and adjust the monitoring frequency for specific health indicators. It can refer to the user's past health data and select a monitoring method appropriate to specific events or situations. This allows for the selection of the optimal monitoring method by analyzing the user's past health data. Past health data includes, but is not limited to, past diagnostic results and fitness tracker data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not. For example, the monitoring unit can input the user's past health data into AI and have the AI ​​select the optimal monitoring method.

[0120] The adjustment unit can also estimate the user's emotions and adjust meal recommendations based on the estimated emotions. For example, if the user is stressed, it can recommend meals containing ingredients that have a relaxing effect. If the user is relaxed, it can recommend nutritionally balanced meals. If the user is tired, it can recommend meals suitable for energy replenishment. This allows for more appropriate dietary management by adjusting meal recommendations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0121] The analysis unit can also estimate the user's emotions and adjust the nutritional analysis method based on the estimated emotions. For example, if the user is stressed, the analysis can focus on nutrients that are effective in reducing stress. If the user is relaxed, the analysis can focus on the overall nutritional balance. If the user is tired, the analysis can focus on nutrients suitable for energy replenishment. This allows for more appropriate nutritional management by adjusting the nutritional analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 not using AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0122] The alert unit can also estimate the user's emotions and adjust the content of alerts based on the estimated emotions. For example, if the user is stressed, alerts related to stress reduction can be prioritized. If the user is relaxed, alerts related to overall health maintenance can be prioritized. If the user is tired, alerts related to energy replenishment can be prioritized. This allows for more appropriate health management by adjusting the content of alerts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 alert unit may be performed using AI, for example, or not using AI. For example, the alert unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0123] The integration unit can also select the optimal integration method by considering the user's device information during integration. For example, if the user is using a smartphone, it can provide a data integration method optimized for smartphones. If the user is using a tablet, it can provide a data integration method optimized for tablets. If the user is using a smartwatch, it can provide a data integration method optimized for smartwatches. This allows for the selection of the optimal integration method by considering the user's device information. Device information includes, but is not limited to, the type of device, its functions, and its usage. Some or all of the above-described processing in the integration unit may be performed using AI, for example, or without AI. For example, the integration unit can input the user's device information data into AI and have AI select the optimal integration method.

[0124] The tracking unit can also select the optimal tracking method by referring to the user's past health data when tracking progress. For example, it can focus tracking on specific time periods based on the user's past health data. It can analyze the user's past health data and adjust the tracking frequency for specific health indicators. It can refer to the user's past health data and select a tracking method appropriate for specific events or situations. This allows the optimal tracking method to be selected by referring to the user's past health data. Past health data includes, but is not limited to, past diagnostic results and fitness tracker data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input the user's past health data into AI and have the AI ​​select the optimal tracking method.

[0125] The service provider can also adjust the level of detail provided when offering meal ideas based on the user's health goals. For example, if the user's goal is weight loss, detailed calorie-restricted meal ideas can be provided. If the user's goal is muscle gain, detailed protein intake meal ideas can be provided. If the user's goal is maintaining health, balanced meal ideas can be provided. By adjusting the level of detail based on the user's health goals, more appropriate dietary management becomes possible. Health goals include, but are not limited to, weight loss, muscle gain, and normalization of blood pressure. Some or all of the processing described above 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 goal data into AI and have the AI ​​adjust the level of detail provided.

[0126] The monitoring unit can also filter health indicators based on the user's current activity level and lifestyle. For example, it can adjust the frequency of health indicator monitoring based on the user's current activity level. It can prioritize monitoring of specific health indicators according to the user's lifestyle. It can filter health indicator monitoring data based on the user's activity level and lifestyle to collect only important data. This allows for the collection of only important data by filtering health indicators based on the user's activity level and lifestyle. Activity levels include, but are not limited to, steps taken, exercise time, and calories burned. Lifestyles include, but are not limited to, meal frequency, sleep duration, and smoking habits. Some or all of the above processing in the monitoring unit may be performed using, for example, AI, or not using AI. For example, the monitoring unit can input the user's activity level and lifestyle data into AI and have the AI ​​perform the filtering.

[0127] The adjustment unit can also apply different recommendation algorithms depending on the user's dietary history when recommending meals. For example, it can recommend the optimal meal based on the user's past meal data. If the user's dietary history indicates a deficiency in a particular nutrient, it can recommend meals that supplement that nutrient. It can also analyze the user's dietary history and make recommendations to increase the variety of meals. By applying different recommendation algorithms depending on the user's dietary history, more appropriate dietary management becomes possible. Dietary history includes, but is not limited to, meal records and calorie intake. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or without AI. For example, the adjustment unit can input the user's dietary history data into AI and have AI perform the application of recommendation algorithms.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The monitoring unit monitors health indicators. These indicators include, for example, heart rate, blood pressure, and blood glucose levels. The monitoring unit acquires data from fitness trackers and wearable devices and monitors these health indicators in real time. Step 2: The adjustment unit adjusts the dietary recommendations based on the data monitored by the monitoring unit. These recommendations include calorie restriction and nutritional balance. The adjustment unit adjusts the dietary recommendations to match the user's health goals. Step 3: The analysis unit scans the meal and performs a nutritional analysis. This includes the analysis tools and analysis items to be used. The analysis unit performs the nutritional analysis after the user scans the meal. Step 4: The service provider offers quick and balanced meal ideas. These ideas include recipes and ingredient combinations. The service provider offers quick and balanced meal ideas tailored to the user's lifestyle.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the monitoring unit, adjustment unit, analysis unit, provision unit, tracking unit, alert unit, and integration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit acquires data from the fitness tracker or wearable device of the smart device 14 and monitors health indicators in real time. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts dietary recommendations based on the data from the monitoring unit. The analysis unit scans meals using the camera 42 of the smart device 14 and performs nutritional analysis. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides quick and balanced meal ideas. The tracking unit acquires data from the fitness tracker or wearable device of the smart device 14 and tracks the user's progress in real time. The alert unit is implemented by the specific processing unit 290 of the data processing unit 12 and issues alerts when necessary nutritional adjustments are needed. The integration unit synchronizes data from fitness trackers and wearable devices using the communication I / F 44 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the monitoring unit, adjustment unit, analysis unit, provision unit, tracking unit, alert unit, and integration unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit acquires data from the fitness tracker or wearable device of the smart glasses 214 and monitors health indicators in real time. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts dietary recommendations based on the data from the monitoring unit. The analysis unit scans meals using the camera 42 of the smart glasses 214 and performs nutritional analysis. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides quick and balanced meal ideas. The tracking unit acquires data from the fitness tracker or wearable device of the smart glasses 214 and tracks the user's progress in real time. The alert unit is implemented by the specific processing unit 290 of the data processing unit 12 and issues alerts when necessary nutritional adjustments are needed. The integration unit synchronizes data from fitness trackers and wearable devices using the communication I / F 44 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[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 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.

[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 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.

[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 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.

[0165] Each of the multiple elements described above, including the monitoring unit, adjustment unit, analysis unit, provision unit, tracking unit, alert unit, and integration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit acquires data from the fitness tracker or wearable device of the headset terminal 314 and monitors health indicators in real time. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts dietary recommendations based on the data from the monitoring unit. The analysis unit scans meals using the camera 42 of the headset terminal 314 and performs nutritional analysis. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides quick and balanced meal ideas. The tracking unit acquires data from the fitness tracker or wearable device of the headset terminal 314 and tracks the user's progress in real time. The alert unit is implemented by the specific processing unit 290 of the data processing unit 12 and issues alerts when necessary nutritional adjustments are needed. The integration unit synchronizes data from fitness trackers and wearable devices using the communication I / F 44 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] Each of the multiple elements described above, including the monitoring unit, adjustment unit, analysis unit, provision unit, tracking unit, alert unit, and integration unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit acquires data from the robot 414's fitness tracker and wearable devices and monitors health indicators in real time. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts meal recommendations based on the data from the monitoring unit. The analysis unit scans meals using the robot 414's camera 42 and performs nutritional analysis. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides quick and balanced meal ideas. The tracking unit acquires data from the robot 414's fitness tracker and wearable devices and tracks the user's progress in real time. The alert unit is implemented by the specific processing unit 290 of the data processing unit 12 and issues alerts when necessary nutritional adjustments are needed. The integration unit synchronizes data from fitness trackers and wearable devices using the robot 414's communication I / F 44. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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."

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] (Note 1) The monitoring department, which monitors health indicators, An adjustment unit that adjusts dietary recommendations based on data monitored by the aforementioned monitoring unit, The analysis department scans meals and performs nutritional analysis, It includes a service section that provides quick and balanced meal ideas. A system characterized by the following features. (Note 2) It includes a tracking unit to track the user's progress. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features an alert unit that issues an alert when necessary nutritional adjustments are required. The system described in Appendix 1, characterized by the features described herein. (Note 4) It features an integration unit for connecting with fitness trackers and wearable devices. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Users scan their meals for nutritional analysis. The system described in Appendix 1, characterized by the features described herein. (Note 6) The adjustment unit is, Adjust dietary recommendations to match the user's health goals. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency of health indicators based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, Analyze the user's past health data to select the optimal monitoring method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, When monitoring health indicators, filtering is performed based on the user's current activity level and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, It estimates the user's emotions and determines the priority of health metrics to monitor based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, When monitoring health indicators, the system prioritizes monitoring of highly relevant indicators by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned monitoring unit, When monitoring health metrics, analyze users' social media activity and monitor relevant health metrics. The system described in Appendix 1, characterized by the features described herein. (Note 13) The adjustment unit is, It estimates the user's emotions and adjusts meal recommendations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The adjustment unit is, When providing dietary recommendations, adjust the level of detail based on the user's health goals. The system described in Appendix 1, characterized by the features described herein. (Note 15) The adjustment unit is, When recommending meals, different recommendation algorithms are applied depending on the user's eating history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The adjustment unit is, It estimates the user's sentiment and adjusts the length of the recommendation based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The adjustment unit is, When recommending meals, the system prioritizes recommendations based on when the user submitted their meals. The system described in Appendix 1, characterized by the features described herein. (Note 18) The adjustment unit is, When recommending meals, the order of recommendations is adjusted based on the relevance of the user's meals. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is The system estimates the user's emotions and adjusts the nutritional analysis method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During nutritional analysis, the level of detail of the analysis is adjusted based on the user's diet. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is During nutritional analysis, different analysis algorithms are applied depending on the user's dietary category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is The system estimates the user's emotions and adjusts how the nutritional analysis results are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is During nutritional analysis, the analysis priority is determined based on when the user submitted their meals. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is During nutritional analysis, the order of analysis is adjusted based on the relevance of the user's diet. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how meal ideas are 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 meal ideas, adjust the level of detail based on the user's health goals. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing meal ideas, different recommendation algorithms are applied depending on the user's meal history. 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 the meal ideas to offer 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 meal ideas, the priority of the suggestions is determined based on when the user submitted their meal ideas. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing meal ideas, the order in which they are presented is adjusted based on the relevance of the user's meals. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned tracking unit is We estimate the user's emotions and adjust how progress is tracked based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned tracking unit is When tracking progress, the system selects the optimal tracking method by referring to the user's past health data. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned tracking unit is It estimates the user's emotions and prioritizes progress based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned tracking unit is When tracking progress, the optimal tracking method is selected by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 35) The alert unit is, It estimates the user's emotions and adjusts the content of alerts based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The alert unit is, When issuing an alert, the system selects the most appropriate alert content by referring to the user's past health data. The system described in Appendix 3, characterized by the features described herein. (Note 37) The alert unit is, It estimates the user's emotions and determines the priority of alerts based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The alert unit is, When issuing an alert, the system selects the most appropriate alert content by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned integration unit is It estimates the user's emotions and adjusts the integration method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned integration unit is During integration, the system selects the optimal integration method by referring to the user's past device usage history. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned integration unit is It estimates user sentiment and determines integration priorities based on the estimated user sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned integration unit is During integration, the optimal integration method is selected, taking into account the user's device information. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0202] 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 monitoring department, which monitors health indicators, An adjustment unit that adjusts dietary recommendations based on data monitored by the aforementioned monitoring unit, The analysis department scans meals and performs nutritional analysis, It includes a service section that provides quick and balanced meal ideas. A system characterized by the following features.

2. It includes a tracking unit to track the user's progress. The system according to feature 1.

3. It features an alert unit that issues an alert when necessary nutritional adjustments are required. The system according to feature 1.

4. It features an integration unit for connecting with fitness trackers and wearable devices. The system according to feature 1.

5. The aforementioned analysis unit is Users scan their meals for nutritional analysis. The system according to feature 1.

6. The adjustment unit is, Adjust dietary recommendations to match the user's health goals. The system according to feature 1.

7. The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring frequency of health indicators based on the estimated user emotions. The system according to feature 1.

8. The aforementioned monitoring unit, Analyze the user's past health data to select the optimal monitoring method. The system according to feature 1.

9. The aforementioned monitoring unit, When monitoring health indicators, filtering is performed based on the user's current activity level and lifestyle. The system according to feature 1.

10. The aforementioned monitoring unit, It estimates the user's emotions and determines the priority of health metrics to monitor based on the estimated user emotions. The system according to feature 1.

Images