system

Abstract

The system according to this embodiment aims to provide personalized health advice to individual users. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a monitoring unit. The collection unit collects data on the user's daily lifestyle habits. The analysis unit analyzes the data collected by the collection unit. The provision unit provides advice based on the analysis results obtained by the analysis unit. The monitoring unit monitors the user's progress and adjusts the plan.

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 the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is difficult to provide health advice optimized for individual users, and there is room for improvement.

[0005] The system according to the embodiment aims to provide health advice optimized for individual users.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a monitoring unit. The collection unit collects daily lifestyle data of users. The analysis unit analyzes the data collected by the collection unit. The provision unit provides advice based on the analysis results obtained by the analysis unit. The monitoring unit monitors the progress of the user and adjusts the plan. [Effects of the Invention]

[0007] The system according to this embodiment can provide health advice optimized for individual users. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 personalized health coaching system according to an embodiment of the present invention is an advanced personalized health coaching service that caters to the individual user. This personalized health coaching system utilizes the latest AI technology to analyze in detail the user's daily lifestyle data, such as meals, exercise, and sleep. Based on this analysis, it provides advice optimized for each individual's health condition and goals. For example, it evaluates nutritional balance from meal records and provides specific meal suggestions to supplement any deficient nutrients. From exercise records, it creates a training plan tailored to the user's physical fitness and goals, and provides step-by-step guidance on effective exercise methods. Furthermore, it takes into account factors such as sleep quality and daytime activity levels to provide comprehensive advice to optimize the user's overall lifestyle rhythm. The AI ​​constantly monitors the user's progress and flexibly adjusts the plan towards achieving goals. This service allows users to receive detailed, expert-like advice 24 / 7, enabling more effective and sustainable health management. For example, it collects the user's daily lifestyle data, such as meals, exercise, and sleep. For instance, the user uses an app to record their meals, inputting the type and quantity of food. Exercise records include the type, duration, and intensity of exercise performed by the user. Furthermore, for sleep tracking, a device is used to record the user's sleep duration and quality. Next, the collected data is analyzed in detail by AI. From the food log, the AI ​​evaluates nutritional balance and identifies any nutrient deficiencies. For example, if a user is not consuming enough vegetables, the AI ​​will provide specific meal suggestions to compensate for the deficiency. From the exercise log, the AI ​​creates a training plan tailored to the user's fitness level and goals. For example, if a user wants to improve their muscle strength, the AI ​​will guide them step-by-step on effective strength training methods. In addition, from the sleep log, the AI ​​takes into account the user's sleep quality and daytime activity level to provide comprehensive advice to optimize their overall lifestyle. The AI ​​constantly monitors the user's progress and flexibly adjusts the plan to help them achieve their goals. For example, if a user is falling behind on their goals, the AI ​​will revise the plan and suggest more realistic goal setting and approaches.Furthermore, once a user achieves a goal, the service sets a new goal as the next step, supporting continuous health management. This service allows users to receive detailed, expert-like advice 24 / 7. For example, if a user is unsure about their food choices, the AI ​​provides appropriate meal suggestions in real time. Also, if a user is unsure about their form during exercise, the AI ​​provides immediate feedback and guidance on correct form. In addition, if sleep quality is poor, the AI ​​identifies the cause and suggests solutions. In this way, the AI-powered personalized health coaching service effectively and sustainably supports users' health management. Users can develop healthy lifestyle habits without feeling overwhelmed by self-management, as they receive detailed, expert-like advice. Thus, the personalized health coaching system effectively and sustainably supports users' health management.

[0029] The personalized health coaching system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a monitoring unit. The collection unit collects the user's daily lifestyle data. The collection unit includes, for example, a method for the user to input meal details using an app. The collection unit includes, for example, the user inputting meal details as text. The collection unit also includes a method for the user to take a photograph of meal details. The collection unit includes, for example, the user taking a photograph of meal details and uploading it to the app. Furthermore, the collection unit includes a method for the user to input meal details by voice. The collection unit includes, for example, the user inputting meal details by voice and the app automatically converting it to text. The collection unit includes a method for automatically collecting exercise data using a wearable device. The collection unit includes, for example, recording the user's steps using a pedometer. The collection unit includes, for example, recording the user's heart rate using a heart rate monitor. The collection unit includes, for example, recording the user's exercise route using GPS tracking. The analysis unit analyzes the data collected by the collection unit in detail. The analysis unit includes, for example, evaluating nutritional balance from meal records and identifying deficient nutrients. The analysis department, for example, calculates calories and evaluates the user's calorie intake. The analysis department, for example, compares nutrient intake to recommended intake levels and identifies deficient nutrients. The analysis department creates a training plan tailored to the user's fitness level and goals from exercise records. The analysis department, for example, evaluates the type, frequency, and intensity of the user's exercise and creates a training plan. The analysis department, for example, suggests effective strength training methods based on the user's exercise data. The analysis department, for example, creates a training plan to improve endurance based on the user's exercise data. The service department provides individual advice based on the analysis results obtained by the analysis department. The service department, for example, takes into account the user's sleep quality and daytime activity level and provides comprehensive advice to optimize the overall lifestyle rhythm. The service department, for example, evaluates the user's sleep duration and suggests an appropriate sleep duration. The service department, for example, evaluates the user's daytime activity level and suggests an appropriate activity level. The service department, for example, provides specific advice to optimize the user's lifestyle rhythm. The monitoring department monitors the user's progress and adjusts the plan accordingly.The monitoring unit, for example, collects user progress data in real time and adjusts the plan. If the user is behind schedule towards their goal, the monitoring unit reviews the plan and suggests more realistic goal setting and approaches. If the user achieves their goal, the monitoring unit sets a new goal as the next step and supports continuous health management. Thus, the personalized health coaching system according to this embodiment achieves effective health management by collecting and analyzing the user's daily lifestyle data in detail, providing individual advice, monitoring progress, and adjusting the plan.

[0030] The data collection unit collects data on the user's daily lifestyle habits. The data collection unit includes, for example, a method for the user to input meal details using an app. The data collection unit includes, for example, a method for the user to input meal details as text. The data collection unit also includes a method for the user to take photos of meal details. The data collection unit includes, for example, a method for the user to take photos of meal details and upload them to the app. Furthermore, the data collection unit includes a method for the user to input meal details as voice. The data collection unit includes, for example, a method for the user to input meal details as voice and the app automatically converts it to text. The data collection unit includes a method for automatically collecting exercise data using a wearable device. The data collection unit includes, for example, a method for the user to record the user's steps using a pedometer. The data collection unit includes, for example, a method for the user to record the user's heart rate using a heart rate monitor. The data collection unit includes, for example, a method for the user to record the user's exercise route using GPS tracking. The data collection unit provides a variety of methods for the user to record their daily lifestyle habits. For example, when the user inputs meal details as text, the app automatically recognizes the type and amount of food and retrieves and records calorie and nutrient information from a database. Furthermore, when recording meal contents through photography, image recognition technology is used to automatically identify food items and similarly obtain calorie and nutrient information. In the case of voice input, speech recognition technology is used to convert the user's speech into text and perform the same processing. This allows users to easily record their meal contents. In addition, when collecting exercise data using wearable devices, sensors such as pedometers, heart rate monitors, and GPS tracking are used to record the user's exercise volume and route in detail. This data is transmitted to the app in real time and stored in a central database. The collection unit centrally manages this data and can cooperate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and provisioning departments. Also, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the collection unit can collect data efficiently and effectively and improve the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department in detail. For example, the analysis department evaluates nutritional balance from meal records and identifies deficient nutrients. For example, the analysis department calculates calories and evaluates the user's calorie intake. For example, the analysis department compares nutrient intake to recommended intake levels and identifies deficient nutrients. The analysis department creates training plans tailored to the user's fitness level and goals from exercise records. For example, the analysis department evaluates the type, frequency, and intensity of the user's exercise and creates training plans. For example, the analysis department suggests effective strength training methods based on the user's exercise data. For example, the analysis department creates training plans to improve endurance based on the user's exercise data. The analysis department analyzes the collected data in detail and comprehensively evaluates the user's health status and lifestyle. For example, from meal records, it evaluates the nutrient balance of each meal and identifies deficient nutrients such as vitamins, minerals, and protein. This makes it clear which nutrients the user should supplement. In addition, by calculating calories and comparing the user's calorie intake and expenditure, it can provide specific advice for weight management and dieting. The analysis of exercise records evaluates the type, frequency, and intensity of the user's exercise and creates a training plan tailored to their individual goals. For example, for users who prioritize strength training, it suggests effective strength training methods, sets, and reps, while for users who want to improve their endurance, it provides a plan for aerobic exercise such as running or cycling. Furthermore, the analysis department can also perform trend analysis and risk assessment for long-term health management based on user data. For example, it can predict health risks in specific seasons or life events based on past data and suggest preventive measures. This allows the analysis department to comprehensively evaluate the user's health status and provide specific advice tailored to their individual needs.

[0032] The service provider provides individualized advice based on the analysis results obtained by the analysis department. For example, the service provider provides comprehensive advice to optimize the user's overall lifestyle, taking into account the user's sleep quality and daytime activity level. For example, the service provider evaluates the user's sleep duration and suggests an appropriate amount of sleep. For example, the service provider evaluates the user's daytime activity level and suggests an appropriate amount of activity. For example, the service provider provides specific advice to optimize the user's lifestyle. Based on the data provided by the analysis department, the service provider provides specific and practical advice to the user. For example, by analyzing the user's sleep data and evaluating the quality and duration of sleep, it suggests appropriate sleep duration and methods to improve the sleep environment. This allows the user to obtain higher quality sleep and improve their daytime activity level and concentration. It also optimizes the user's lifestyle by evaluating daytime activity levels and suggesting an appropriate balance of exercise and rest. In order to provide advice tailored to the user's individual needs and goals, the service provider can collect user feedback and continuously improve the advice. For example, by receiving feedback on the results of the user's actions following the advice provided, the service provider evaluates the effectiveness of the advice and makes adjustments as needed. Furthermore, the service provider also provides support to help users maintain their motivation. For example, they visualize progress toward achieving goals and provide rewards and encouraging messages according to the level of achievement. This helps users to continue their health management efforts. The service provider also provides comprehensive advice to optimize the user's overall lifestyle and supports users in leading a healthy life.

[0033] The monitoring department monitors user progress and adjusts plans accordingly. For example, the monitoring department collects user progress data in real time and adjusts plans based on that data. If a user is behind schedule, the monitoring department revises the plan and suggests more realistic goal setting and approaches. If a user achieves their goal, the monitoring department sets a new goal as the next step to support continued health management. The monitoring department monitors user progress in real time and adjusts plans as needed. For example, if a user is behind schedule towards their set goal, the monitoring department analyzes the cause and suggests a new approach to achieving the goal. This may include resetting goals, revising training plans, or improving dietary habits. Furthermore, if a user achieves their goal, the monitoring department sets a new goal as the next step to support continued health management. The monitoring department can provide more effective approaches by collecting user feedback and evaluating the effectiveness of the plan. For example, by receiving feedback on the results of users following the provided plan, the monitoring department evaluates its effectiveness and makes adjustments as needed. The monitoring department also supports users in maintaining motivation. For example, it visualizes progress toward goal achievement and provides rewards and encouraging messages based on the level of achievement. This increases users' motivation to continuously engage in health management. Furthermore, the monitoring unit can flexibly adjust the plan in response to changes in the user's health status and lifestyle. For example, if a user develops a new health problem or their living environment changes, the monitoring unit will provide a plan tailored to the situation and support the user's health management. In this way, the monitoring unit can continuously monitor the user's progress and provide appropriate plans, thereby achieving effective health management.

[0034] The data collection unit includes a method for users to input meal details using an app. For example, the user inputs meal details as text. For example, the user takes a photo of their meal and uploads it to the app. For example, the user inputs meal details by voice, and the app automatically converts it to text. This makes it easier to collect meal data by allowing users to input meal details using an app. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the meal details entered by the user into an AI, which can then analyze the meal details and collect data.

[0035] The data collection unit includes a method for automatically collecting exercise data using a wearable device. For example, the data collection unit records the user's steps using a pedometer. For example, the data collection unit records the user's heart rate using a heart rate monitor. For example, the data collection unit records the user's exercise route using GPS tracking. This makes the collection of exercise data more efficient by automatically collecting exercise data using a wearable device. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input exercise data acquired from a wearable device into an AI, which can then analyze and collect the exercise data.

[0036] The analysis unit evaluates nutritional balance from meal records and identifies deficient nutrients. The analysis unit, for example, calculates calories and evaluates the user's calorie intake. The analysis unit, for example, compares nutrient intake to recommended intake levels and identifies deficient nutrients. This enables appropriate nutritional management by evaluating nutritional balance from meal records and identifying deficient nutrients. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input meal records into AI, which can evaluate nutritional balance and identify deficient nutrients.

[0037] The analysis department creates a training plan tailored to the user's fitness level and goals based on exercise records. For example, the analysis department evaluates the type, frequency, and intensity of the user's exercise and creates a training plan. For example, the analysis department suggests effective strength training methods based on the user's exercise data. For example, the analysis department creates a training plan to improve endurance based on the user's exercise data. This enables effective exercise guidance by creating a training plan tailored to the user's fitness level and goals from exercise records. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input exercise records into AI, which can then create a training plan tailored to the user's fitness level and goals.

[0038] The service provider offers advice to improve the user's overall lifestyle based on their sleep quality and daytime activity levels. For example, the service provider evaluates the user's sleep duration and suggests an appropriate amount of sleep. For example, the service provider evaluates the user's daytime activity levels and suggests an appropriate amount of activity. For example, the service provider provides specific advice to optimize the user's lifestyle. This allows for effective health management by providing comprehensive advice to optimize the user's overall lifestyle, taking into account their sleep quality and daytime activity levels. 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 sleep data into an AI, which can then evaluate the sleep quality and suggest improvement measures.

[0039] The monitoring unit collects user progress data in real time and adjusts the plan. For example, if a user is behind schedule, the monitoring unit reviews the plan and suggests more realistic goal setting and approaches. If a user achieves their goal, the monitoring unit sets a new goal as the next step and supports continuous health management. This allows for flexible responses toward goal achievement by collecting user progress data in real time and adjusting the plan. Some or all of the above processes in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input user progress data into AI, which can evaluate the progress and adjust the plan.

[0040] The service provider sets new goals as the next step when the user achieves their initial goal, supporting continuous health management. For example, the service provider sets new goals as the next step when the user achieves their initial goal, supporting continuous health management. For example, the service provider sets new goals based on user feedback. For example, the service provider analyzes the user's past data and sets appropriate goals as the next step. This enables continuous health management by setting new goals as the next step when the user achieves their initial goal, supporting continuous health management. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user feedback into AI, which can then set new goals.

[0041] The data collection unit analyzes the user's past data collection history and selects the optimal collection method. For example, the data collection unit prioritizes suggesting data collection methods that the user has frequently used in the past. For example, the data collection unit selects the most efficient collection method from the user's past data collection history. For example, the data collection unit analyzes the user's past data collection history and optimizes the collection frequency. This enables efficient data collection by analyzing the user's past data collection history and selecting the optimal collection method. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into AI, which can then select the optimal collection method.

[0042] The data collection unit filters data based on the user's current health status and lifestyle. For example, if the user is tired, the data collection unit performs simplified data collection. For example, if the user is healthy, the data collection unit performs detailed data collection. For example, the data collection unit adjusts the type of data collected according to the user's lifestyle. This allows for appropriate data collection by filtering the data based on the user's current health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's health status and lifestyle into the AI, which can then filter the data.

[0043] The data collection unit prioritizes collecting highly relevant data, taking into account the user's geographical location. For example, if the user is in a specific region, the data collection unit prioritizes collecting health data related to that region. For example, if the user is traveling, the data collection unit prioritizes collecting health information related to the travel destination. For example, if the user is at home, the data collection unit prioritizes collecting lifestyle data related to the user's home. This enables appropriate data collection by prioritizing the collection of highly relevant data, taking into account the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into the AI, which can then prioritize the collection of highly relevant data.

[0044] The data collection unit analyzes the user's social media activity and collects relevant data during data collection. For example, the data collection unit collects information about meals shared by the user on social media. For example, the data collection unit collects information about exercise shared by the user on social media. For example, the data collection unit collects information about sleep shared by the user on social media. This allows for the collection of a wider variety of data by analyzing the user's social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI, which can then collect relevant data.

[0045] The analysis unit adjusts the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit performs a simplified analysis on less important data. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0046] The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a nutritional balance analysis algorithm to dietary data. For example, the analysis unit applies a training effect analysis algorithm to exercise data. For example, the analysis unit applies a sleep quality analysis algorithm to sleep data. By applying different analysis algorithms depending on the data category, appropriate data analysis becomes possible. 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 data category into the AI, and the AI ​​can apply an appropriate analysis algorithm.

[0047] The analysis department determines the priority of analysis based on the data collection period. For example, the analysis department prioritizes the analysis of the most recent data. For example, the analysis department analyzes the most recent data while referring to past data. For example, the analysis department adjusts the priority of analysis according to the data collection period. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the data collection period into the AI, and the AI ​​can determine the priority of analysis.

[0048] The analysis unit adjusts the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit prioritizes analyzing highly relevant data. For example, the analysis unit postpones analyzing less relevant data. The analysis unit adjusts the order of analysis according to the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of analysis.

[0049] The service provider adjusts the level of detail of the advice based on the user's health condition when providing advice. For example, if the user is healthy, the service provider provides detailed advice. For example, if the user is unwell, the service provider provides simplified advice. The service provider adjusts the level of detail of the advice according to the user's health condition. This allows the service provider to provide appropriate advice by adjusting the level of detail based on the user's health condition. 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 condition data into the AI, which can then adjust the level of detail of the advice.

[0050] The service provider applies different advice algorithms depending on the user's goals when providing advice. For example, if the user wants to lose weight, the service provider applies an advice algorithm specifically for weight loss. For example, if the user wants to improve muscle strength, the service provider applies an advice algorithm specifically for muscle training. For example, if the user wants to maintain their health, the service provider applies a balanced advice algorithm. By applying different advice algorithms according to the user's goals, the service provider can provide effective advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's goal data into the AI, and the AI ​​can apply an appropriate advice algorithm.

[0051] The service provider determines the priority of advice based on the user's daily rhythm when providing advice. For example, the service provider provides simplified advice during busy times for the user. For example, the service provider provides detailed advice during relaxed times for the user. The service provider adjusts the priority of advice according to the user's daily rhythm for the user. This allows the service provider to provide advice at the appropriate time by determining the priority of advice based on the user's daily rhythm. 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 daily rhythm data into AI, and the AI ​​can determine the priority of advice.

[0052] The service provider adjusts the order of advice by referring to the user's past advice history when providing advice. The service provider determines the optimal order of advice based on the advice the user has received in the past, for example. The service provider proposes an effective order of advice based on the user's past advice history, for example. The service provider analyzes the user's past advice history and optimizes the order of advice. This allows for the provision of effective advice by adjusting the order of advice by referring to the user's past advice history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past advice history into AI, which can then adjust the order of advice.

[0053] The monitoring unit analyzes the user's past progress data during monitoring to select the optimal monitoring method. For example, the monitoring unit selects the optimal monitoring method based on the user's past progress data. For example, the monitoring unit proposes an effective monitoring method based on the user's past progress data. For example, the monitoring unit analyzes the user's past progress data and optimizes the monitoring frequency. This enables effective monitoring by analyzing the user's past progress data and selecting the optimal monitoring method. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past progress data into AI, which can then select the optimal monitoring method.

[0054] The monitoring unit customizes the monitoring methods based on the user's current living situation during monitoring. For example, if the user is busy, the monitoring unit provides simplified monitoring methods. For example, if the user is relaxed, the monitoring unit provides detailed monitoring methods. The monitoring unit adjusts the monitoring methods according to the user's living situation. This allows for appropriate monitoring by customizing the monitoring methods based on the user's current living situation. 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 living situation data into the AI, which can then customize the monitoring methods.

[0055] The monitoring unit selects the optimal monitoring method when monitoring, taking into account the user's geographical location information. For example, if the user is in a specific region, the monitoring unit prioritizes monitoring health data related to that region. For example, if the user is traveling, the monitoring unit prioritizes monitoring health information related to the travel destination. For example, if the user is at home, the monitoring unit prioritizes monitoring lifestyle data at home. This enables appropriate monitoring by selecting the optimal monitoring method considering the user's geographical location information. 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 the user's geographical location information into the AI, which can then select the optimal monitoring method.

[0056] The monitoring unit analyzes the user's social media activity during monitoring and proposes monitoring methods. For example, the monitoring unit monitors meal information shared by the user on social media. For example, the monitoring unit monitors exercise information shared by the user on social media. For example, the monitoring unit monitors sleep information shared by the user on social media. By analyzing the user's social media activity and proposing monitoring methods, appropriate monitoring becomes possible. 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 the user's social media activity data into AI, and the AI ​​can propose monitoring methods.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, it can prioritize suggesting data collection methods that the user has frequently used in the past. It can also select the most efficient collection method based on the user's past data collection history. Furthermore, it can analyze the user's past data collection history and optimize the collection frequency. As a result, by analyzing the user's past data collection history and selecting the optimal collection method, efficient data collection becomes possible.

[0059] The data collection unit can filter data based on the user's current health status and lifestyle. For example, if the user is tired, simplified data collection can be performed. Conversely, if the user is healthy, detailed data collection can be performed. Furthermore, the type of data collected can be adjusted according to the user's lifestyle. This allows for appropriate data collection by filtering data based on the user's current health status and lifestyle.

[0060] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, if a user is in a specific region, it can prioritize the collection of health data related to that region. Similarly, if a user is traveling, it can prioritize the collection of health information related to their travel destination. Furthermore, if a user is at home, it can prioritize the collection of lifestyle data related to their home environment. This allows for appropriate data collection by prioritizing the collection of highly relevant data while considering the user's geographical location.

[0061] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, it can collect information about meals shared by users on social media. It can also collect information about exercise shared by users on social media. Furthermore, it can collect information about sleep shared by users on social media. This allows for the collection of a wider variety of data by analyzing users' social media activity and collecting relevant data.

[0062] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform detailed analysis on important data, and simplified analysis on less important data. Furthermore, it can determine the priority of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail based on the importance of the data.

[0063] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, a nutritional balance analysis algorithm can be applied to dietary data. Similarly, a training effect analysis algorithm can be applied to exercise data. Furthermore, a sleep quality analysis algorithm can be applied to sleep data. This allows for appropriate data analysis by applying different analysis algorithms depending on the data category.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The data collection unit collects the user's daily lifestyle data. The data collection unit includes, for example, a method for the user to input meal details using an app. The user can input meal details by text, taking a photo, or voice input. The data collection unit also automatically collects exercise data using wearable devices. For example, it records steps with a pedometer, heart rate with a heart rate monitor, and exercise route with GPS tracking. Step 2: The analysis unit analyzes the data collected by the data collection unit in detail. For example, it evaluates nutritional balance from meal records and identifies deficient nutrients. It calculates calories and evaluates the user's calorie intake. It creates a training plan tailored to the user's fitness level and goals from exercise records. It evaluates the type, frequency, and intensity of the user's exercise and proposes an effective strength training or endurance training plan. Step 3: The service provider provides individualized advice based on the analysis results obtained by the analysis provider. For example, they provide comprehensive advice to optimize the user's overall lifestyle, taking into account the user's sleep quality and daytime activity level. They evaluate the user's sleep duration and suggest an appropriate amount of sleep. They evaluate the user's daytime activity level and suggest an appropriate activity level. They provide specific advice to optimize the lifestyle. Step 4: The monitoring team monitors user progress and adjusts the plan. For example, they collect user progress data in real time and adjust the plan. If a user is behind schedule towards their goal, they revise the plan and suggest more realistic goal setting and approaches. If a user achieves their goal, they set a new goal as the next step and support ongoing health management.

[0066] (Example of form 2) The personalized health coaching system according to an embodiment of the present invention is an advanced personalized health coaching service that caters to the individual user. This personalized health coaching system utilizes the latest AI technology to analyze in detail the user's daily lifestyle data, such as meals, exercise, and sleep. Based on this analysis, it provides advice optimized for each individual's health condition and goals. For example, it evaluates nutritional balance from meal records and provides specific meal suggestions to supplement any deficient nutrients. From exercise records, it creates a training plan tailored to the user's physical fitness and goals, and provides step-by-step guidance on effective exercise methods. Furthermore, it takes into account factors such as sleep quality and daytime activity levels to provide comprehensive advice to optimize the user's overall lifestyle rhythm. The AI ​​constantly monitors the user's progress and flexibly adjusts the plan towards achieving goals. This service allows users to receive detailed, expert-like advice 24 / 7, enabling more effective and sustainable health management. For example, it collects the user's daily lifestyle data, such as meals, exercise, and sleep. For instance, the user uses an app to record their meals, inputting the type and quantity of food. Exercise records include the type, duration, and intensity of exercise performed by the user. Furthermore, for sleep tracking, a device is used to record the user's sleep duration and quality. Next, the collected data is analyzed in detail by AI. From the food log, the AI ​​evaluates nutritional balance and identifies any nutrient deficiencies. For example, if a user is not consuming enough vegetables, the AI ​​will provide specific meal suggestions to compensate for the deficiency. From the exercise log, the AI ​​creates a training plan tailored to the user's fitness level and goals. For example, if a user wants to improve their muscle strength, the AI ​​will guide them step-by-step on effective strength training methods. In addition, from the sleep log, the AI ​​takes into account the user's sleep quality and daytime activity level to provide comprehensive advice to optimize their overall lifestyle. The AI ​​constantly monitors the user's progress and flexibly adjusts the plan to help them achieve their goals. For example, if a user is falling behind on their goals, the AI ​​will revise the plan and suggest more realistic goal setting and approaches.Furthermore, once a user achieves a goal, the service sets a new goal as the next step, supporting continuous health management. This service allows users to receive detailed, expert-like advice 24 / 7. For example, if a user is unsure about their food choices, the AI ​​provides appropriate meal suggestions in real time. Also, if a user is unsure about their form during exercise, the AI ​​provides immediate feedback and guidance on correct form. In addition, if sleep quality is poor, the AI ​​identifies the cause and suggests solutions. In this way, the AI-powered personalized health coaching service effectively and sustainably supports users' health management. Users can develop healthy lifestyle habits without feeling overwhelmed by self-management, as they receive detailed, expert-like advice. Thus, the personalized health coaching system effectively and sustainably supports users' health management.

[0067] The personalized health coaching system according to the embodiment comprises a collection unit, an analysis unit, a provision unit, and a monitoring unit. The collection unit collects the user's daily lifestyle data. The collection unit includes, for example, a method for the user to input meal details using an app. The collection unit includes, for example, the user inputting meal details as text. The collection unit also includes a method for the user to take a photograph of meal details. The collection unit includes, for example, the user taking a photograph of meal details and uploading it to the app. Furthermore, the collection unit includes a method for the user to input meal details by voice. The collection unit includes, for example, the user inputting meal details by voice and the app automatically converting it to text. The collection unit includes a method for automatically collecting exercise data using a wearable device. The collection unit includes, for example, recording the user's steps using a pedometer. The collection unit includes, for example, recording the user's heart rate using a heart rate monitor. The collection unit includes, for example, recording the user's exercise route using GPS tracking. The analysis unit analyzes the data collected by the collection unit in detail. The analysis unit includes, for example, evaluating nutritional balance from meal records and identifying deficient nutrients. The analysis department, for example, calculates calories and evaluates the user's calorie intake. The analysis department, for example, compares nutrient intake to recommended intake levels and identifies deficient nutrients. The analysis department creates a training plan tailored to the user's fitness level and goals from exercise records. The analysis department, for example, evaluates the type, frequency, and intensity of the user's exercise and creates a training plan. The analysis department, for example, suggests effective strength training methods based on the user's exercise data. The analysis department, for example, creates a training plan to improve endurance based on the user's exercise data. The service department provides individual advice based on the analysis results obtained by the analysis department. The service department, for example, takes into account the user's sleep quality and daytime activity level and provides comprehensive advice to optimize the overall lifestyle rhythm. The service department, for example, evaluates the user's sleep duration and suggests an appropriate sleep duration. The service department, for example, evaluates the user's daytime activity level and suggests an appropriate activity level. The service department, for example, provides specific advice to optimize the user's lifestyle rhythm. The monitoring department monitors the user's progress and adjusts the plan accordingly.The monitoring unit, for example, collects user progress data in real time and adjusts the plan. If the user is behind schedule towards their goal, the monitoring unit reviews the plan and suggests more realistic goal setting and approaches. If the user achieves their goal, the monitoring unit sets a new goal as the next step and supports continuous health management. Thus, the personalized health coaching system according to this embodiment achieves effective health management by collecting and analyzing the user's daily lifestyle data in detail, providing individual advice, monitoring progress, and adjusting the plan.

[0068] The data collection unit collects data on the user's daily lifestyle habits. The data collection unit includes, for example, a method for the user to input meal details using an app. The data collection unit includes, for example, a method for the user to input meal details as text. The data collection unit also includes a method for the user to take photos of meal details. The data collection unit includes, for example, a method for the user to take photos of meal details and upload them to the app. Furthermore, the data collection unit includes a method for the user to input meal details as voice. The data collection unit includes, for example, a method for the user to input meal details as voice and the app automatically converts it to text. The data collection unit includes a method for automatically collecting exercise data using a wearable device. The data collection unit includes, for example, a method for the user to record the user's steps using a pedometer. The data collection unit includes, for example, a method for the user to record the user's heart rate using a heart rate monitor. The data collection unit includes, for example, a method for the user to record the user's exercise route using GPS tracking. The data collection unit provides a variety of methods for the user to record their daily lifestyle habits. For example, when the user inputs meal details as text, the app automatically recognizes the type and amount of food and retrieves and records calorie and nutrient information from a database. Furthermore, when recording meal contents through photography, image recognition technology is used to automatically identify food items and similarly obtain calorie and nutrient information. In the case of voice input, speech recognition technology is used to convert the user's speech into text and perform the same processing. This allows users to easily record their meal contents. In addition, when collecting exercise data using wearable devices, sensors such as pedometers, heart rate monitors, and GPS tracking are used to record the user's exercise volume and route in detail. This data is transmitted to the app in real time and stored in a central database. The collection unit centrally manages this data and can cooperate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis and provisioning departments. Also, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. As a result, the collection unit can collect data efficiently and effectively and improve the overall system performance.

[0069] The analysis department analyzes the data collected by the data collection department in detail. For example, the analysis department evaluates nutritional balance from meal records and identifies deficient nutrients. For example, the analysis department calculates calories and evaluates the user's calorie intake. For example, the analysis department compares nutrient intake to recommended intake levels and identifies deficient nutrients. The analysis department creates training plans tailored to the user's fitness level and goals from exercise records. For example, the analysis department evaluates the type, frequency, and intensity of the user's exercise and creates training plans. For example, the analysis department suggests effective strength training methods based on the user's exercise data. For example, the analysis department creates training plans to improve endurance based on the user's exercise data. The analysis department analyzes the collected data in detail and comprehensively evaluates the user's health status and lifestyle. For example, from meal records, it evaluates the nutrient balance of each meal and identifies deficient nutrients such as vitamins, minerals, and protein. This makes it clear which nutrients the user should supplement. In addition, by calculating calories and comparing the user's calorie intake and expenditure, it can provide specific advice for weight management and dieting. The analysis of exercise records evaluates the type, frequency, and intensity of the user's exercise and creates a training plan tailored to their individual goals. For example, for users who prioritize strength training, it suggests effective strength training methods, sets, and reps, while for users who want to improve their endurance, it provides a plan for aerobic exercise such as running or cycling. Furthermore, the analysis department can also perform trend analysis and risk assessment for long-term health management based on user data. For example, it can predict health risks in specific seasons or life events based on past data and suggest preventive measures. This allows the analysis department to comprehensively evaluate the user's health status and provide specific advice tailored to their individual needs.

[0070] The service provider provides individualized advice based on the analysis results obtained by the analysis department. For example, the service provider provides comprehensive advice to optimize the user's overall lifestyle, taking into account the user's sleep quality and daytime activity level. For example, the service provider evaluates the user's sleep duration and suggests an appropriate amount of sleep. For example, the service provider evaluates the user's daytime activity level and suggests an appropriate amount of activity. For example, the service provider provides specific advice to optimize the user's lifestyle. Based on the data provided by the analysis department, the service provider provides specific and practical advice to the user. For example, by analyzing the user's sleep data and evaluating the quality and duration of sleep, it suggests appropriate sleep duration and methods to improve the sleep environment. This allows the user to obtain higher quality sleep and improve their daytime activity level and concentration. It also optimizes the user's lifestyle by evaluating daytime activity levels and suggesting an appropriate balance of exercise and rest. In order to provide advice tailored to the user's individual needs and goals, the service provider can collect user feedback and continuously improve the advice. For example, by receiving feedback on the results of the user's actions following the advice provided, the service provider evaluates the effectiveness of the advice and makes adjustments as needed. Furthermore, the service provider also provides support to help users maintain their motivation. For example, they visualize progress toward achieving goals and provide rewards and encouraging messages according to the level of achievement. This helps users to continue their health management efforts. The service provider also provides comprehensive advice to optimize the user's overall lifestyle and supports users in leading a healthy life.

[0071] The monitoring department monitors user progress and adjusts plans accordingly. For example, the monitoring department collects user progress data in real time and adjusts plans based on that data. If a user is behind schedule, the monitoring department revises the plan and suggests more realistic goal setting and approaches. If a user achieves their goal, the monitoring department sets a new goal as the next step to support continued health management. The monitoring department monitors user progress in real time and adjusts plans as needed. For example, if a user is behind schedule towards their set goal, the monitoring department analyzes the cause and suggests a new approach to achieving the goal. This may include resetting goals, revising training plans, or improving dietary habits. Furthermore, if a user achieves their goal, the monitoring department sets a new goal as the next step to support continued health management. The monitoring department can provide more effective approaches by collecting user feedback and evaluating the effectiveness of the plan. For example, by receiving feedback on the results of users following the provided plan, the monitoring department evaluates its effectiveness and makes adjustments as needed. The monitoring department also supports users in maintaining motivation. For example, it visualizes progress toward goal achievement and provides rewards and encouraging messages based on the level of achievement. This increases users' motivation to continuously engage in health management. Furthermore, the monitoring unit can flexibly adjust the plan in response to changes in the user's health status and lifestyle. For example, if a user develops a new health problem or their living environment changes, the monitoring unit will provide a plan tailored to the situation and support the user's health management. In this way, the monitoring unit can continuously monitor the user's progress and provide appropriate plans, thereby achieving effective health management.

[0072] The data collection unit includes a method for users to input meal details using an app. For example, the user inputs meal details as text. For example, the user takes a photo of their meal and uploads it to the app. For example, the user inputs meal details by voice, and the app automatically converts it to text. This makes it easier to collect meal data by allowing users to input meal details using an app. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the meal details entered by the user into an AI, which can then analyze the meal details and collect data.

[0073] The data collection unit includes a method for automatically collecting exercise data using a wearable device. For example, the data collection unit records the user's steps using a pedometer. For example, the data collection unit records the user's heart rate using a heart rate monitor. For example, the data collection unit records the user's exercise route using GPS tracking. This makes the collection of exercise data more efficient by automatically collecting exercise data using a wearable device. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input exercise data acquired from a wearable device into an AI, which can then analyze and collect the exercise data.

[0074] The analysis unit evaluates nutritional balance from meal records and identifies deficient nutrients. The analysis unit, for example, calculates calories and evaluates the user's calorie intake. The analysis unit, for example, compares nutrient intake to recommended intake levels and identifies deficient nutrients. This enables appropriate nutritional management by evaluating nutritional balance from meal records and identifying deficient nutrients. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input meal records into AI, which can evaluate nutritional balance and identify deficient nutrients.

[0075] The analysis department creates a training plan tailored to the user's fitness level and goals based on exercise records. For example, the analysis department evaluates the type, frequency, and intensity of the user's exercise and creates a training plan. For example, the analysis department suggests effective strength training methods based on the user's exercise data. For example, the analysis department creates a training plan to improve endurance based on the user's exercise data. This enables effective exercise guidance by creating a training plan tailored to the user's fitness level and goals from exercise records. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input exercise records into AI, which can then create a training plan tailored to the user's fitness level and goals.

[0076] The service provider offers advice to improve the user's overall lifestyle based on their sleep quality and daytime activity levels. For example, the service provider evaluates the user's sleep duration and suggests an appropriate amount of sleep. For example, the service provider evaluates the user's daytime activity levels and suggests an appropriate amount of activity. For example, the service provider provides specific advice to optimize the user's lifestyle. This allows for effective health management by providing comprehensive advice to optimize the user's overall lifestyle, taking into account their sleep quality and daytime activity levels. 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 sleep data into an AI, which can then evaluate the sleep quality and suggest improvement measures.

[0077] The monitoring unit collects user progress data in real time and adjusts the plan. For example, if a user is behind schedule, the monitoring unit reviews the plan and suggests more realistic goal setting and approaches. If a user achieves their goal, the monitoring unit sets a new goal as the next step and supports continuous health management. This allows for flexible responses toward goal achievement by collecting user progress data in real time and adjusting the plan. Some or all of the above processes in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input user progress data into AI, which can evaluate the progress and adjust the plan.

[0078] The service provider sets new goals as the next step when the user achieves their initial goal, supporting continuous health management. For example, the service provider sets new goals as the next step when the user achieves their initial goal, supporting continuous health management. For example, the service provider sets new goals based on user feedback. For example, the service provider analyzes the user's past data and sets appropriate goals as the next step. This enables continuous health management by setting new goals as the next step when the user achieves their initial goal, supporting continuous health management. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user feedback into AI, which can then set new goals.

[0079] The data collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit collects data during times when the user is relaxed. For example, if the user is relaxed, the data collection unit collects detailed data. For example, if the user is in a hurry, the data collection unit collects simplified data. By adjusting the timing of data collection based on the user's emotions, data can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into an AI, which can then adjust the timing of data collection.

[0080] The data collection unit analyzes the user's past data collection history and selects the optimal collection method. For example, the data collection unit prioritizes suggesting data collection methods that the user has frequently used in the past. For example, the data collection unit selects the most efficient collection method from the user's past data collection history. For example, the data collection unit analyzes the user's past data collection history and optimizes the collection frequency. This enables efficient data collection by analyzing the user's past data collection history and selecting the optimal collection method. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into AI, which can then select the optimal collection method.

[0081] The data collection unit filters data based on the user's current health status and lifestyle. For example, if the user is tired, the data collection unit performs simplified data collection. For example, if the user is healthy, the data collection unit performs detailed data collection. For example, the data collection unit adjusts the type of data collected according to the user's lifestyle. This allows for appropriate data collection by filtering the data based on the user's current health status and lifestyle. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the user's health status and lifestyle into the AI, which can then filter the data.

[0082] The data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit prioritizes collecting stress-related data. For example, if the user is relaxed, the data collection unit prioritizes collecting detailed health data. For example, if the user is in a hurry, the data collection unit prioritizes collecting only essential data. This ensures that important data is collected preferentially by prioritizing data collection 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can then determine the priority of data to collect.

[0083] The data collection unit prioritizes collecting highly relevant data, taking into account the user's geographical location. For example, if the user is in a specific region, the data collection unit prioritizes collecting health data related to that region. For example, if the user is traveling, the data collection unit prioritizes collecting health information related to the travel destination. For example, if the user is at home, the data collection unit prioritizes collecting lifestyle data related to the user's home. This enables appropriate data collection by prioritizing the collection of highly relevant data, taking into account the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into the AI, which can then prioritize the collection of highly relevant data.

[0084] The data collection unit analyzes the user's social media activity and collects relevant data during data collection. For example, the data collection unit collects information about meals shared by the user on social media. For example, the data collection unit collects information about exercise shared by the user on social media. For example, the data collection unit collects information about sleep shared by the user on social media. This allows for the collection of a wider variety of data by analyzing the user's social media activity and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI, which can then collect relevant data.

[0085] The analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a simple, visual analysis. For example, if the user is relaxed, the analysis unit provides a detailed analysis. For example, if the user is in a hurry, the analysis unit provides a concise analysis. By adjusting the presentation of the analysis based on the user's emotions, the analysis unit can provide results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into AI, and the AI ​​can adjust the presentation of the analysis.

[0086] The analysis unit adjusts the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data. For example, the analysis unit performs a simplified analysis on less important data. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis.

[0087] The analysis unit applies different analysis algorithms depending on the data category during analysis. For example, the analysis unit applies a nutritional balance analysis algorithm to dietary data. For example, the analysis unit applies a training effect analysis algorithm to exercise data. For example, the analysis unit applies a sleep quality analysis algorithm to sleep data. By applying different analysis algorithms depending on the data category, appropriate data analysis becomes possible. 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 data category into the AI, and the AI ​​can apply an appropriate analysis algorithm.

[0088] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, to-the-point analysis. For example, if the user is relaxed, the analysis unit provides a detailed analysis. For example, if the user is excited, the analysis unit provides a visually stimulating analysis. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide the user with an analysis result of an appropriate length. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into an AI, which can then adjust the length of the analysis.

[0089] The analysis department determines the priority of analysis based on the data collection period. For example, the analysis department prioritizes the analysis of the most recent data. For example, the analysis department analyzes the most recent data while referring to past data. For example, the analysis department adjusts the priority of analysis according to the data collection period. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection period. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input the data collection period into the AI, and the AI ​​can determine the priority of analysis.

[0090] The analysis unit adjusts the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit prioritizes analyzing highly relevant data. For example, the analysis unit postpones analyzing less relevant data. The analysis unit adjusts the order of analysis according to the relevance of the data. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of analysis.

[0091] The service provider estimates the user's emotions and adjusts the way advice is presented based on the estimated emotions. For example, if the user is stressed, the service provider provides simple, visual advice. For example, if the user is relaxed, the service provider provides detailed advice. For example, if the user is in a hurry, the service provider provides concise advice. By adjusting the way advice is presented based on the user's emotions, the service provider can provide advice that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into AI, and the AI ​​can adjust the way advice is presented.

[0092] The service provider adjusts the level of detail of the advice based on the user's health condition when providing advice. For example, if the user is healthy, the service provider provides detailed advice. For example, if the user is unwell, the service provider provides simplified advice. The service provider adjusts the level of detail of the advice according to the user's health condition. This allows the service provider to provide appropriate advice by adjusting the level of detail based on the user's health condition. 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 condition data into the AI, which can then adjust the level of detail of the advice.

[0093] The service provider applies different advice algorithms depending on the user's goals when providing advice. For example, if the user wants to lose weight, the service provider applies an advice algorithm specifically for weight loss. For example, if the user wants to improve muscle strength, the service provider applies an advice algorithm specifically for muscle training. For example, if the user wants to maintain their health, the service provider applies a balanced advice algorithm. By applying different advice algorithms according to the user's goals, the service provider can provide effective advice. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's goal data into the AI, and the AI ​​can apply an appropriate advice algorithm.

[0094] The service provider estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the service provider provides short, concise advice. For example, if the user is relaxed, the service provider provides detailed advice. For example, if the user is excited, the service provider provides visually stimulating advice. By adjusting the length of the advice based on the user's emotions, the service provider can provide advice of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into an AI, which can then adjust the length of the advice.

[0095] The service provider determines the priority of advice based on the user's daily rhythm when providing advice. For example, the service provider provides simplified advice during busy times for the user. For example, the service provider provides detailed advice during relaxed times for the user. The service provider adjusts the priority of advice according to the user's daily rhythm for the user. This allows the service provider to provide advice at the appropriate time by determining the priority of advice based on the user's daily rhythm. 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 daily rhythm data into AI, and the AI ​​can determine the priority of advice.

[0096] The service provider adjusts the order of advice by referring to the user's past advice history when providing advice. The service provider determines the optimal order of advice based on the advice the user has received in the past, for example. The service provider proposes an effective order of advice based on the user's past advice history, for example. The service provider analyzes the user's past advice history and optimizes the order of advice. This allows for the provision of effective advice by adjusting the order of advice by referring to the user's past advice history. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past advice history into AI, which can then adjust the order of advice.

[0097] The monitoring unit estimates the user's emotions and adjusts its monitoring method based on the estimated emotions. For example, if the user is stressed, the monitoring unit reduces the monitoring frequency. For example, if the user is relaxed, the monitoring unit performs detailed monitoring. For example, if the user is in a hurry, the monitoring unit performs simplified monitoring. This allows for appropriate monitoring by adjusting the monitoring method 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, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input user emotion data into an AI, which can then adjust the monitoring method.

[0098] The monitoring unit analyzes the user's past progress data during monitoring to select the optimal monitoring method. For example, the monitoring unit selects the optimal monitoring method based on the user's past progress data. For example, the monitoring unit proposes an effective monitoring method based on the user's past progress data. For example, the monitoring unit analyzes the user's past progress data and optimizes the monitoring frequency. This enables effective monitoring by analyzing the user's past progress data and selecting the optimal monitoring method. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the user's past progress data into AI, which can then select the optimal monitoring method.

[0099] The monitoring unit customizes the monitoring methods based on the user's current living situation during monitoring. For example, if the user is busy, the monitoring unit provides simplified monitoring methods. For example, if the user is relaxed, the monitoring unit provides detailed monitoring methods. The monitoring unit adjusts the monitoring methods according to the user's living situation. This allows for appropriate monitoring by customizing the monitoring methods based on the user's current living situation. 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 living situation data into the AI, which can then customize the monitoring methods.

[0100] The monitoring unit estimates the user's emotions and determines monitoring priorities based on the estimated emotions. For example, if the user is stressed, the monitoring unit prioritizes stress-related monitoring. If the user is relaxed, the monitoring unit prioritizes monitoring detailed health data. If the user is in a hurry, the monitoring unit prioritizes monitoring only important data. This allows for priority monitoring of important data by determining monitoring priorities 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, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input user emotion data into an AI, which can then determine monitoring priorities.

[0101] The monitoring unit selects the optimal monitoring method when monitoring, taking into account the user's geographical location information. For example, if the user is in a specific region, the monitoring unit prioritizes monitoring health data related to that region. For example, if the user is traveling, the monitoring unit prioritizes monitoring health information related to the travel destination. For example, if the user is at home, the monitoring unit prioritizes monitoring lifestyle data at home. This enables appropriate monitoring by selecting the optimal monitoring method considering the user's geographical location information. 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 the user's geographical location information into the AI, which can then select the optimal monitoring method.

[0102] The monitoring unit analyzes the user's social media activity during monitoring and proposes monitoring methods. For example, the monitoring unit monitors meal information shared by the user on social media. For example, the monitoring unit monitors exercise information shared by the user on social media. For example, the monitoring unit monitors sleep information shared by the user on social media. By analyzing the user's social media activity and proposing monitoring methods, appropriate monitoring becomes possible. 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 the user's social media activity data into AI, and the AI ​​can propose monitoring methods.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, data can be collected during times when they are relaxed. If the user is relaxed, detailed data can be collected. Furthermore, if the user is in a hurry, simplified data collection can be performed. By adjusting the timing of data collection based on the user's emotions, data can be collected at a more appropriate time.

[0105] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, it can prioritize suggesting data collection methods that the user has frequently used in the past. It can also select the most efficient collection method based on the user's past data collection history. Furthermore, it can analyze the user's past data collection history and optimize the collection frequency. As a result, by analyzing the user's past data collection history and selecting the optimal collection method, efficient data collection becomes possible.

[0106] The data collection unit can filter data based on the user's current health status and lifestyle. For example, if the user is tired, simplified data collection can be performed. Conversely, if the user is healthy, detailed data collection can be performed. Furthermore, the type of data collected can be adjusted according to the user's lifestyle. This allows for appropriate data collection by filtering data based on the user's current health status and lifestyle.

[0107] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is stressed, stress-related data can be prioritized. If the user is relaxed, detailed health data can be prioritized. Furthermore, if the user is in a hurry, only essential data can be prioritized. In this way, by prioritizing the data to be collected based on the user's emotions, important data can be collected preferentially.

[0108] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, if a user is in a specific region, it can prioritize the collection of health data related to that region. Similarly, if a user is traveling, it can prioritize the collection of health information related to their travel destination. Furthermore, if a user is at home, it can prioritize the collection of lifestyle data related to their home environment. This allows for appropriate data collection by prioritizing the collection of highly relevant data while considering the user's geographical location.

[0109] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, it can collect information about meals shared by users on social media. It can also collect information about exercise shared by users on social media. Furthermore, it can collect information about sleep shared by users on social media. This allows for the collection of a wider variety of data by analyzing users' social media activity and collecting relevant data.

[0110] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is stressed, it can provide a simple, visual analysis. If the user is relaxed, it can provide a detailed analysis. Furthermore, if the user is in a hurry, it can provide a concise analysis. By adjusting the presentation of the analysis based on the user's emotions, it can provide analysis results that are easy for the user to understand.

[0111] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform detailed analysis on important data, and simplified analysis on less important data. Furthermore, it can determine the priority of the analysis according to the importance of the data. This allows for efficient data analysis by adjusting the level of detail based on the importance of the data.

[0112] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, a nutritional balance analysis algorithm can be applied to dietary data. Similarly, a training effect analysis algorithm can be applied to exercise data. Furthermore, a sleep quality analysis algorithm can be applied to sleep data. This allows for appropriate data analysis by applying different analysis algorithms depending on the data category.

[0113] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on those emotions. For example, if the user is in a hurry, it can provide a short, to-the-point analysis. If the user is relaxed, it can provide a detailed analysis. Furthermore, if the user is excited, it can provide a visually stimulating analysis. By adjusting the length of the analysis based on the user's emotions, the system can provide analysis results of an appropriate length for the user.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The data collection unit collects the user's daily lifestyle data. The data collection unit includes, for example, a method for the user to input meal details using an app. The user can input meal details by text, taking a photo, or voice input. The data collection unit also automatically collects exercise data using wearable devices. For example, it records steps with a pedometer, heart rate with a heart rate monitor, and exercise route with GPS tracking. Step 2: The analysis unit analyzes the data collected by the data collection unit in detail. For example, it evaluates nutritional balance from meal records and identifies deficient nutrients. It calculates calories and evaluates the user's calorie intake. It creates a training plan tailored to the user's fitness level and goals from exercise records. It evaluates the type, frequency, and intensity of the user's exercise and proposes an effective strength training or endurance training plan. Step 3: The service provider provides individualized advice based on the analysis results obtained by the analysis provider. For example, they provide comprehensive advice to optimize the user's overall lifestyle, taking into account the user's sleep quality and daytime activity level. They evaluate the user's sleep duration and suggest an appropriate amount of sleep. They evaluate the user's daytime activity level and suggest an appropriate activity level. They provide specific advice to optimize the lifestyle. Step 4: The monitoring team monitors user progress and adjusts the plan. For example, they collect user progress data in real time and adjust the plan. If a user is behind schedule towards their goal, they revise the plan and suggest more realistic goal setting and approaches. If a user achieves their goal, they set a new goal as the next step and support ongoing health management.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and monitoring unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects the user's daily lifestyle data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in detail by the specific processing unit 290 of the data processing unit 12. The provision unit is implemented in detail by the control unit 46A of the smart device 14. Based on the analysis results, it provides personalized advice. The monitoring unit is implemented in detail by the specific processing unit 290 of the data processing unit 12. It monitors the user's progress and adjusts the plan accordingly. 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.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and monitoring unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects the user's daily lifestyle data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data in detail. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides personalized advice based on the analysis results. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and monitors the user's progress and adjusts the plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and monitoring unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects the user's daily lifestyle data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in detail by the specific processing unit 290 of the data processing unit 12. The provision unit is implemented in detail by the control unit 46A of the headset terminal 314. The provision unit provides personalized advice based on the analysis results. The monitoring unit is implemented in detail by the specific processing unit 290 of the data processing unit 12. The monitoring unit monitors the user's progress and adjusts the plan. 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.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the data collection unit, analysis unit, provision unit, and monitoring unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data on the user's daily lifestyle using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data in detail. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides individual advice based on the analysis results. The monitoring unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and monitors the user's progress and adjusts the plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] (Note 1) A system characterized by comprising: a collection unit that collects data on the user's daily lifestyle; an analysis unit that analyzes the data collected by the collection unit; a provision unit that provides advice based on the analysis results obtained by the analysis unit; and a monitoring unit that monitors the user's progress and adjusts the plan. (Note 2) The aforementioned collection unit is The app provides a way for users to input their meal details. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is It includes a method for automatically collecting exercise data using wearable devices. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is Evaluate nutritional balance from food diaries and identify any nutrient deficiencies. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Based on exercise records, a training plan tailored to the user's fitness level and goals is created. The system described in Appendix 1, characterized by the features described herein. (Note 6) The system described in Appendix 1 is characterized by providing advice to improve the user's overall lifestyle rhythm based on the quality of their sleep and their daytime activity level. (Note 7) The aforementioned monitoring unit, Collect user progress data in real time and adjust the plan accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned supply unit is, Once a user achieves their goal, the next step involves setting a new goal to support their continued health management. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The system described in Appendix 1, characterized by analyzing the user's past data collection history and selecting an appropriate collection method. (Note 11) The aforementioned collection unit is During data collection, filtering is performed based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The system described in Appendix 1, characterized in that, during data collection, it prioritizes the collection of highly relevant data based on the user's geographical location information. (Note 14) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The system described in Appendix 1, characterized in that the level of detail of the analysis is adjusted based on the importance of the data during the analysis. (Note 17) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The system described in Appendix 1, characterized in that it determines the priority of analysis based on the timing of data collection during the analysis process. (Note 20) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The system described in Appendix 1, characterized in that it adjusts the level of detail of the advice based on the user's health condition when providing advice. (Note 23) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the user's goals. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The system described in Appendix 1, characterized in that it determines the priority of advice based on the user's daily routine when providing advice. (Note 26) The aforementioned supply unit is, When providing advice, the system adjusts the order of advice by referring to the user's past advice history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned monitoring unit, It estimates user sentiment and adjusts monitoring methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The system described in Appendix 1, characterized in that, during monitoring, it analyzes the user's past progress data to select an appropriate monitoring method. (Note 29) The aforementioned monitoring unit, During monitoring, the monitoring methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The system described in Appendix 1, characterized in that it selects an appropriate monitoring method based on the user's geographical location information during monitoring. (Note 32) The aforementioned monitoring unit, During monitoring, we analyze users' social media activity and propose monitoring methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0188] 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. A system characterized by comprising: a collection unit that collects data on the user's daily lifestyle; an analysis unit that analyzes the data collected by the collection unit; a provision unit that provides advice based on the analysis results obtained by the analysis unit; and a monitoring unit that monitors the user's progress and adjusts the plan.

2. The aforementioned collection unit is The app provides a way for users to input their meal details. The system according to feature 1.

3. The aforementioned collection unit is It includes a method for automatically collecting exercise data using wearable devices. The system according to feature 1.

4. The aforementioned analysis unit is Evaluate nutritional balance from food diaries and identify any nutrient deficiencies. The system according to feature 1.

5. The aforementioned analysis unit is Based on exercise records, a training plan tailored to the user's fitness level and goals is created. The system according to feature 1.

6. The system according to claim 1, characterized in that it provides advice to improve the user's overall lifestyle rhythm based on the quality of their sleep and their daytime activity level.

7. The aforementioned monitoring unit, Collect user progress data in real time and adjust the plan accordingly. The system according to feature 1.

8. The aforementioned supply unit is, Once a user achieves their goal, the next step involves setting a new goal to support their continued health management. The system according to feature 1.

9. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

10. The system according to claim 1, characterized by analyzing the user's past data collection history and selecting an appropriate collection method.

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