system

Abstract

The system according to this embodiment aims to provide an optimal diet plan tailored to each individual's physical characteristics and lifestyle, track progress, and provide interactive feedback. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, a tracking unit, and a feedback unit. The collection unit collects data on the user's physical characteristics and lifestyle. The generation unit analyzes the data collected by the collection unit and generates an optimal diet plan for each individual user. The tracking unit tracks the user's progress based on the diet plan generated by the generation unit. The feedback unit provides interactive feedback based on the progress tracked by the tracking unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to provide a diet plan optimized for individual constitutions and lifestyles, and there is a lack of feedback for progress tracking and motivation maintenance.

[0005] The system according to the embodiment aims to provide a diet plan optimized for individual constitutions and lifestyles, track progress, and provide interactive feedback.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a data generation unit, a data tracking unit, and a data feedback unit. The data collection unit collects data on the user's physical characteristics and lifestyle. The data generation unit analyzes the data collected by the data collection unit and generates an optimal diet plan for each individual user. The data tracking unit tracks the user's progress based on the diet plan generated by the data generation unit. The data feedback unit provides interactive feedback based on the progress tracked by the data tracking unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide a diet plan that is optimal for each individual's physical constitution and lifestyle, track progress, and provide interactive feedback. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) An AI agent according to an embodiment of the present invention is a system that provides a customized diet plan tailored to an individual's physical characteristics and lifestyle. This system collects data on the user's physical characteristics and lifestyle, and a generating AI analyzes this data to generate an optimal diet plan. Furthermore, it supports healthy weight management by tracking the user's progress based on the generated diet plan and providing interactive feedback. For example, it collects data on the user's physical characteristics and lifestyle. In this process, it collects detailed data such as the user's diet, exercise habits, and sleep patterns. For example, it collects data such as the calories the user consumes daily, the frequency of exercise, and the amount of sleep. This allows for an understanding of the user's lifestyle. Next, the generating AI analyzes the collected data. Based on the collected data, the generating AI analyzes the user's physical characteristics and lifestyle and generates an optimal diet plan for each individual user. For example, if a user is consuming high-calorie meals, the generating AI will suggest a meal plan with reduced calories. Also, for users with little exercise, it will suggest a plan that incorporates moderate exercise. This allows for the provision of a customized diet plan tailored to the user's needs. Furthermore, it tracks the user's progress based on the generated diet plan. The generating AI regularly collects data such as the user's weight and body fat percentage to track their progress. For example, it can check how close the user is to their target weight and adjust the plan as needed. This allows the user to understand their progress in real time. It also provides interactive feedback. The generating AI provides encouraging messages and advice based on the user's progress. For example, if the user is close to their target weight, the generating AI will send a message such as, "Congratulations! You're getting closer to your goal." If progress has stalled, it will send an encouraging message such as, "Let's keep going a little longer." This helps maintain the user's motivation and supports healthy weight management.This allows the AI ​​agent to provide customized diet plans tailored to individual body types and lifestyles, and to support healthy weight management by providing interactive feedback for tracking progress and maintaining motivation.

[0029] The AI ​​agent according to this embodiment comprises a collection unit, a generation unit, a tracking unit, and a feedback unit. The collection unit collects data related to the user's physical characteristics and lifestyle. For example, the collection unit collects data such as the user's diet, exercise habits, and sleep patterns. For example, the collection unit collects data such as the user's daily calorie intake, exercise frequency, and sleep duration. For example, the collection unit records the user's diet to understand calorie intake and nutrient balance. For example, the collection unit records the user's exercise habits to understand the type, frequency, and intensity of exercise. For example, the collection unit records the user's sleep patterns to understand sleep duration and sleep quality. The generation unit analyzes the data collected by the collection unit and generates an optimal diet plan for each individual user. For example, the generation unit analyzes the user's physical characteristics and lifestyle based on the collected data. For example, if the user is consuming high-calorie meals, the generation unit proposes a calorie-reduced meal plan. For example, if the user has little exercise, the generation unit proposes a plan that incorporates moderate exercise. The generation unit, for example, proposes a nutritionally balanced meal plan tailored to the user's constitution. The generation unit, for example, proposes an exercise plan tailored to the user's lifestyle. The generation unit, for example, proposes a diet plan tailored to the user's target weight. The tracking unit tracks the user's progress based on the diet plan generated by the generation unit. The tracking unit, for example, periodically collects data such as the user's weight and body fat percentage. The tracking unit, for example, checks how close the user is to their target weight. The tracking unit, for example, records changes in the user's weight to track progress. The tracking unit, for example, records changes in the user's body fat percentage to track progress. The tracking unit, for example, records the user's exercise achievement to track progress. The feedback unit provides interactive feedback based on the progress tracked by the tracking unit. The feedback unit, for example, provides encouraging messages and advice according to the user's progress. The feedback unit, for example, sends an encouraging message when the user is approaching their target weight. The feedback unit, for example, sends an encouraging message when the user's progress has stagnated.The feedback unit provides, for example, advice on diet and exercise based on the user's progress. The feedback unit also sends messages to maintain motivation based on the user's progress. The feedback unit also suggests adjusting the plan based on the user's progress. As a result, the AI ​​agent according to the embodiment can provide a customized diet plan tailored to the user's physical characteristics and lifestyle, and offer interactive feedback for tracking progress and maintaining motivation.

[0030] The data collection unit collects data about the user's physical characteristics and lifestyle. Specifically, it collects detailed data such as the user's daily diet, exercise habits, and sleep patterns. For example, it records the user's meals to understand the balance of calories and nutrients they consume daily. This includes information such as the type, quantity, and timing of the food consumed. Furthermore, it records the type, frequency, and intensity of exercise the user performs. For example, if the user engages in jogging, strength training, or yoga, detailed information is collected. Regarding sleep patterns, it records the user's sleep duration and sleep quality. This includes information such as when the user goes to bed and wakes up, the number of times they woke up during the night, and the depth of their sleep. The data collection unit centrally manages this data, enabling a detailed understanding of the user's physical characteristics and lifestyle. In addition, the data collection unit can integrate with the user's devices and applications to automatically collect data. For example, it can record meals through a smartphone app or collect exercise data using a fitness tracker. This allows users to collect data without effort, providing more accurate information.

[0031] The generation unit analyzes the data collected by the collection unit to generate an optimal diet plan for each individual user. Specifically, it analyzes the user's physical characteristics and lifestyle in detail based on the collected data. For example, if a user is consuming high-calorie meals, the generation unit will propose a calorie-reduced meal plan. This includes low-calorie foods and balanced meal menus. For users with little exercise, the generation unit will propose a plan that incorporates moderate exercise. This may include light jogging, strength training, or stretching a few times a week. Furthermore, it will propose a nutritionally balanced meal plan tailored to the user's physical characteristics. For example, if a user is deficient in a particular nutrient, it will propose a menu that includes foods that supplement that nutrient. It will also propose an exercise plan tailored to the user's lifestyle. For example, it will propose a short, effective exercise menu for busy users and a longer exercise menu for users with more time. Finally, it will propose a diet plan tailored to the user's target weight. For example, it will adjust calorie restrictions and exercise volume to reach the target weight. Based on this data, the generation unit generates an optimal diet plan for the user and supports the user in achieving their goals in a healthy way.

[0032] The tracking unit tracks the user's progress based on the diet plan generated by the generation unit. Specifically, it regularly collects data such as the user's weight and body fat percentage to understand their progress. For example, it records changes in weight to see how close the user is to their target weight. It also records changes in body fat percentage to understand changes in the user's body composition. Furthermore, it records the user's exercise achievement to check whether they are exercising as planned. For example, it records how much of the set exercise menu the user has achieved to understand their progress. The tracking unit centrally manages this data, allowing for a detailed understanding of the user's progress. In addition, the tracking unit can integrate with the user's devices and applications to automatically collect data. For example, it can record weight and body fat percentage through a smartphone app or collect exercise data using a fitness tracker. This allows users to collect data without effort and provides more accurate information. The tracking unit provides detailed support for users to achieve their goals by understanding their progress.

[0033] The feedback unit provides interactive feedback based on the progress tracked by the tracking unit. Specifically, it provides encouraging messages and advice according to the user's progress. For example, when a user approaches their target weight, it sends an encouraging message to maintain their motivation. Also, if a user's progress has stalled, it sends an encouraging message to rekindle their motivation. Furthermore, it provides advice on diet and exercise according to the user's progress. For example, it suggests improvements to their diet or additional exercise routines to support the user in effectively working towards their goal. It also suggests adjusting the plan according to the user's progress. For example, if the target weight is reached, it sets a new goal and proposes a plan to achieve it. Through this interactive feedback, the feedback unit maintains the user's motivation and supports them in achieving their goals. In addition, the feedback unit can collect user feedback and continuously improve the accuracy and effectiveness of the feedback it provides. For example, it can review and improve the content of the feedback based on user opinions and requests. This allows the feedback unit to provide effective support to users and maintain their motivation towards achieving their goals.

[0034] The data collection unit can collect data such as the user's diet, exercise habits, and sleep patterns. For example, the data collection unit can record the user's diet to understand calorie intake and nutrient balance. For example, the data collection unit can record the user's exercise habits to understand the type, frequency, and intensity of exercise. For example, the data collection unit can record the user's sleep patterns to understand sleep duration and quality. By collecting detailed lifestyle data of the user, a more accurate diet plan can be generated. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, to record the user's diet, the data collection unit can provide an interface for the user to input their diet, input the entered data into a generating AI, and the generating AI can analyze the data to understand calorie intake and nutrient balance.

[0035] The generation unit can analyze the user's physical characteristics and lifestyle based on the collected data and generate calorie-restricted meal plans and plans that incorporate moderate exercise. For example, the generation unit analyzes the user's physical characteristics and lifestyle based on the collected data. For example, if the user is consuming high-calorie meals, the generation unit proposes a calorie-restricted meal plan. For example, if the user has little exercise, the generation unit proposes a plan that incorporates moderate exercise. For example, the generation unit proposes a nutritionally balanced meal plan tailored to the user's physical characteristics. For example, the generation unit proposes an exercise plan tailored to the user's lifestyle. For example, the generation unit proposes a diet plan tailored to the user's target weight. This makes it possible to provide an optimal diet plan based on the user's physical characteristics and lifestyle. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the collected data into a generation AI, which can analyze the data and generate an optimal diet plan for the user.

[0036] The tracking unit can periodically collect data such as the user's weight and body fat percentage to track their progress. For example, the tracking unit can record changes in the user's weight to understand their progress. For example, the tracking unit can record changes in the user's body fat percentage to understand their progress. For example, the tracking unit can record the user's exercise achievement to understand their progress. This allows the effectiveness of the diet plan to be checked by regularly tracking the user's progress, and the plan can be adjusted as needed. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's weight and body fat percentage data into a generating AI, which can then analyze the data to track progress.

[0037] The feedback unit can provide encouraging messages and advice according to the user's progress. For example, the feedback unit sends an encouraging message when the user approaches their target weight. For example, the feedback unit sends an encouraging message when the user's progress has stalled. For example, the feedback unit provides advice on diet and exercise according to the user's progress. For example, the feedback unit sends messages to maintain motivation according to the user's progress. For example, the feedback unit suggests adjusting the plan according to the user's progress. This allows for the maintenance of user motivation and support for healthy weight management by providing interactive feedback according to the user's progress. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user progress data into a generating AI, which can analyze the data and provide appropriate feedback.

[0038] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can automatically collect data that the user has frequently entered in the past. For example, the data collection unit can prioritize suggesting collection methods that the user has used in the past (manual, voice, etc.). For example, the data collection unit can suggest the optimal collection method for a specific time period based on the user's past data collection history. In this way, by analyzing the user's past data collection history, the optimal collection method can be selected and data can be collected efficiently. 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 past data collection history into a generating AI, which can then analyze the data and select the optimal collection method.

[0039] The data collection unit can filter data based on the user's current health status and stress level during data collection. For example, if the user is tired, the data collection unit will perform simplified data collection. For example, if the user is healthy, the data collection unit will perform detailed data collection. For example, if the user is stressed, the data collection unit will prioritize collecting data that helps reduce stress. This allows for the collection of more appropriate data by filtering the data according to the user's health status and stress level. 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 stress level into a generating AI, which can then analyze and filter the data.

[0040] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit will prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit will prioritize the collection of data related to home. By prioritizing the collection of highly relevant data based on the user's geographical location information, more useful data can be collected. 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 a generating AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can automatically collect details of meals shared by the user on social media. For example, the data collection unit can automatically collect exercise records shared by the user on social media. For example, the data collection unit can automatically collect health information shared by the user on social media. This allows for the efficient collection of relevant data by analyzing the user's social media activity. 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 the user's social media activity data into a generating AI, which can then analyze the data and collect relevant data.

[0042] The generation unit can adjust the level of detail in a diet plan based on the user's health goals when generating the plan. For example, if the user wants to lose weight in a short period of time, the generation unit will provide a detailed plan. For example, if the user wants to lose weight over a long period of time, the generation unit will provide a concise plan. For example, if the user aims to maintain their health, the generation unit will provide a balanced plan. By adjusting the level of detail in the plan according to the user's health goals, the generation unit can provide the optimal plan for the user. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's health goal data into a generation AI, and the generation AI can analyze the data and adjust the level of detail in the plan.

[0043] The generation unit can apply different plan generation algorithms depending on the user's dietary preferences and allergy information when generating a diet plan. For example, if the user is a vegetarian, the generation unit will provide a vegetarian plan. If the user has a specific allergy, the generation unit will provide a plan that avoids that allergy. If the user has a specific dietary preference, the generation unit will provide a plan tailored to that preference. By applying a plan generation algorithm according to the user's dietary preferences and allergy information, it is possible to provide a safe and effective plan for the user. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's dietary preferences and allergy information into a generation AI, which can then analyze the data and apply a different plan generation algorithm.

[0044] The generation unit can determine the priority of diet plans based on the user's lifestyle when generating them. For example, if the user is a morning person, the generation unit will provide a plan that emphasizes breakfast. For example, if the user is a night owl, the generation unit will provide a plan that emphasizes dinner. For example, if the user has an irregular lifestyle, the generation unit will provide a flexible plan. By determining the priority of plans based on the user's lifestyle, it is possible to provide a plan that is easy for the user to follow. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's lifestyle data into a generation AI, and the generation AI can analyze the data to determine the priority of plans.

[0045] The generation unit can adjust the order of the diet plan based on the user's past successes when generating the plan. For example, the generation unit can provide a new plan based on the user's past successful plans. For example, the generation unit can suggest the optimal order of the plan based on the user's past successes. For example, the generation unit can analyze the user's past successes and provide the most effective plan. In this way, by adjusting the order of the plan based on the user's past successes, it is possible to provide a plan that is effective for the user. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's past success data into a generation AI, and the generation AI can analyze the data and adjust the order of the plan.

[0046] The tracking unit can optimize the tracking algorithm by referring to the user's past progress data when tracking progress. For example, the tracking unit provides the optimal tracking algorithm based on the user's past successful progress data. For example, the tracking unit proposes the optimal tracking method from the user's past progress data. For example, the tracking unit analyzes the user's past progress data and provides the most effective tracking algorithm. In this way, the optimal tracking algorithm can be provided by referring to the user's past progress data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's past progress data into a generating AI, and the generating AI can analyze the data to optimize the tracking algorithm.

[0047] The tracking unit can adjust the tracking frequency based on changes in the user's health and lifestyle when tracking progress. For example, the tracking unit performs detailed progress tracking when the user is healthy. For example, the tracking unit performs simplified progress tracking when the user is tired. For example, the tracking unit adjusts the tracking frequency based on changes in the user's lifestyle. This allows for more appropriate progress tracking by adjusting the tracking frequency according to changes in the user's health and lifestyle. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input data on the user's health and lifestyle into a generating AI, which can then analyze the data and adjust the tracking frequency.

[0048] The tracking unit can select the optimal tracking method when tracking progress, taking into account the user's geographical location information. For example, if the user is in a specific region, the tracking unit will prioritize tracking progress related to that region. For example, if the user is traveling, the tracking unit will prioritize tracking progress related to the travel destination. For example, if the user is at home, the tracking unit will prioritize tracking progress related to home. By selecting the optimal tracking method based on the user's geographical location information, more useful progress tracking can be performed. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's geographical location information into a generating AI, which can then analyze the data and select the optimal tracking method.

[0049] The tracking unit can analyze the user's social media activity and suggest tracking methods when tracking progress. For example, the tracking unit can automatically track progress shared by the user on social media. For example, the tracking unit can suggest the optimal tracking method based on health information shared by the user on social media. For example, the tracking unit can suggest the optimal tracking method based on exercise records shared by the user on social media. In this way, by analyzing the user's social media activity, the optimal tracking method can be suggested. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's social media activity data into a generating AI, which can then analyze the data and suggest the optimal tracking method.

[0050] The feedback unit can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the feedback unit may prioritize the format of feedback that the user has received favorably in the past. For example, the feedback unit may adjust the current feedback by referring to the content of feedback the user has received in the past. For example, the feedback unit may provide feedback at the optimal timing based on the user's past feedback history. In this way, optimal feedback can be provided by referring to the user's past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past feedback history data into a generating AI, and the generating AI can analyze the data to provide optimal feedback.

[0051] The feedback unit can customize the content of feedback based on changes in the user's health status and lifestyle when providing feedback. For example, if the user is in good health, the feedback unit will provide positive feedback. For example, if the user is tired, the feedback unit will provide feedback encouraging rest. For example, if the user's lifestyle changes, the feedback unit will provide feedback corresponding to that change. This allows for the provision of more appropriate feedback by customizing the content of feedback according to changes in the user's health status and lifestyle. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input data on the user's health status and lifestyle into a generating AI, which can then analyze the data and customize the content of the feedback.

[0052] The feedback unit can provide optimal feedback by considering the user's geographical location information when providing feedback. For example, if the user is in a specific region, the feedback unit will provide feedback relevant to that region. For example, if the user is traveling, the feedback unit will provide feedback relevant to the travel destination. For example, if the user is at home, the feedback unit will provide feedback relevant to home. By providing optimal feedback based on the user's geographical location information, more useful feedback can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location information into a generating AI, and the generating AI can analyze the data to provide optimal feedback.

[0053] The feedback unit can analyze the user's social media activity and propose a method of providing feedback when providing feedback. For example, the feedback unit can provide optimal feedback based on the progress the user has shared on social media. For example, the feedback unit can provide optimal feedback based on the health information the user has shared on social media. For example, the feedback unit can provide optimal feedback based on the exercise records the user has shared on social media. In this way, by analyzing the user's social media activity, it is possible to propose an optimal method of feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's social media activity data into a generating AI, and the generating AI can analyze the data and propose an optimal method of feedback.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can collect data such as the user's diet, exercise habits, and sleep patterns. For example, the data collection unit can record the user's diet to understand calorie intake and nutrient balance. For example, the data collection unit can record the user's exercise habits to understand the type, frequency, and intensity of exercise. For example, the data collection unit can record the user's sleep patterns to understand sleep duration and quality. By collecting detailed lifestyle data of the user, a more accurate diet plan can be generated. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, to record the user's diet, the data collection unit can provide an interface for the user to input their diet, input the entered data into a generating AI, and the generating AI can analyze the data to understand calorie intake and nutrient balance.

[0056] The generation unit can analyze the user's physical characteristics and lifestyle based on the collected data and generate calorie-restricted meal plans and plans that incorporate moderate exercise. For example, the generation unit analyzes the user's physical characteristics and lifestyle based on the collected data. For example, if the user is consuming high-calorie meals, the generation unit proposes a calorie-restricted meal plan. For example, if the user has little exercise, the generation unit proposes a plan that incorporates moderate exercise. For example, the generation unit proposes a nutritionally balanced meal plan tailored to the user's physical characteristics. For example, the generation unit proposes an exercise plan tailored to the user's lifestyle. For example, the generation unit proposes a diet plan tailored to the user's target weight. This makes it possible to provide an optimal diet plan based on the user's physical characteristics and lifestyle. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the collected data into a generation AI, which can analyze the data and generate an optimal diet plan for the user.

[0057] The tracking unit can periodically collect data such as the user's weight and body fat percentage to track their progress. For example, the tracking unit can record changes in the user's weight to understand their progress. For example, the tracking unit can record changes in the user's body fat percentage to understand their progress. For example, the tracking unit can record the user's exercise achievement to understand their progress. This allows the effectiveness of the diet plan to be checked by regularly tracking the user's progress, and the plan can be adjusted as needed. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's weight and body fat percentage data into a generating AI, which can then analyze the data to track progress.

[0058] The feedback unit can provide encouraging messages and advice according to the user's progress. For example, the feedback unit sends an encouraging message when the user approaches their target weight. For example, the feedback unit sends an encouraging message when the user's progress has stalled. For example, the feedback unit provides advice on diet and exercise according to the user's progress. For example, the feedback unit sends messages to maintain motivation according to the user's progress. For example, the feedback unit suggests adjusting the plan according to the user's progress. This allows for the maintenance of user motivation and support for healthy weight management by providing interactive feedback according to the user's progress. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user progress data into a generating AI, which can analyze the data and provide appropriate feedback.

[0059] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can automatically collect data that the user has frequently entered in the past. For example, the data collection unit can prioritize suggesting collection methods that the user has used in the past (manual, voice, etc.). For example, the data collection unit can suggest the optimal collection method for a specific time period based on the user's past data collection history. In this way, by analyzing the user's past data collection history, the optimal collection method can be selected and data can be collected efficiently. 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 past data collection history into a generating AI, which can then analyze the data and select the optimal collection method.

[0060] The data collection unit can filter data based on the user's current health status and stress level during data collection. For example, if the user is tired, the data collection unit will perform simplified data collection. For example, if the user is healthy, the data collection unit will perform detailed data collection. For example, if the user is stressed, the data collection unit will prioritize collecting data that helps reduce stress. This allows for the collection of more appropriate data by filtering the data according to the user's health status and stress level. 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 stress level into a generating AI, which can then analyze and filter the data.

[0061] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit will prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit will prioritize the collection of data related to home. By prioritizing the collection of highly relevant data based on the user's geographical location information, more useful data can be collected. 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 a generating AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0062] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can automatically collect details of meals shared by the user on social media. For example, the data collection unit can automatically collect exercise records shared by the user on social media. For example, the data collection unit can automatically collect health information shared by the user on social media. This allows for the efficient collection of relevant data by analyzing the user's social media activity. 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 the user's social media activity data into a generating AI, which can then analyze the data and collect relevant data.

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

[0064] Step 1: The data collection unit collects data about the user's physical characteristics and lifestyle. Specifically, it collects data such as the user's diet, exercise habits, and sleep patterns, recording data such as daily calorie intake, exercise frequency, and sleep duration. Step 2: The generation unit analyzes the data collected by the collection unit and generates an optimal diet plan for each individual user. Specifically, it analyzes the user's physical characteristics and lifestyle, and proposes a calorie-reduced meal plan if the user consumes high-calorie foods, or a plan that incorporates moderate exercise if the user has little exercise habits. Step 3: The tracking unit tracks the user's progress based on the diet plan generated by the generation unit. Specifically, it periodically collects data such as the user's weight and body fat percentage to check how close they are to their target weight. Step 4: The feedback unit provides interactive feedback based on the progress tracked by the tracking unit. Specifically, it provides encouraging messages and advice according to the user's progress, and sends messages to maintain motivation if progress has stalled.

[0065] (Example of form 2) An AI agent according to an embodiment of the present invention is a system that provides a customized diet plan tailored to an individual's physical characteristics and lifestyle. This system collects data on the user's physical characteristics and lifestyle, and a generating AI analyzes this data to generate an optimal diet plan. Furthermore, it supports healthy weight management by tracking the user's progress based on the generated diet plan and providing interactive feedback. For example, it collects data on the user's physical characteristics and lifestyle. In this process, it collects detailed data such as the user's diet, exercise habits, and sleep patterns. For example, it collects data such as the calories the user consumes daily, the frequency of exercise, and the amount of sleep. This allows for an understanding of the user's lifestyle. Next, the generating AI analyzes the collected data. Based on the collected data, the generating AI analyzes the user's physical characteristics and lifestyle and generates an optimal diet plan for each individual user. For example, if a user is consuming high-calorie meals, the generating AI will suggest a meal plan with reduced calories. Also, for users with little exercise, it will suggest a plan that incorporates moderate exercise. This allows for the provision of a customized diet plan tailored to the user's needs. Furthermore, it tracks the user's progress based on the generated diet plan. The generating AI regularly collects data such as the user's weight and body fat percentage to track their progress. For example, it can check how close the user is to their target weight and adjust the plan as needed. This allows the user to understand their progress in real time. It also provides interactive feedback. The generating AI provides encouraging messages and advice based on the user's progress. For example, if the user is close to their target weight, the generating AI will send a message such as, "Congratulations! You're getting closer to your goal." If progress has stalled, it will send an encouraging message such as, "Let's keep going a little longer." This helps maintain the user's motivation and supports healthy weight management.This allows the AI ​​agent to provide customized diet plans tailored to individual body types and lifestyles, and to support healthy weight management by providing interactive feedback for tracking progress and maintaining motivation.

[0066] The AI ​​agent according to this embodiment comprises a collection unit, a generation unit, a tracking unit, and a feedback unit. The collection unit collects data related to the user's physical characteristics and lifestyle. For example, the collection unit collects data such as the user's diet, exercise habits, and sleep patterns. For example, the collection unit collects data such as the user's daily calorie intake, exercise frequency, and sleep duration. For example, the collection unit records the user's diet to understand calorie intake and nutrient balance. For example, the collection unit records the user's exercise habits to understand the type, frequency, and intensity of exercise. For example, the collection unit records the user's sleep patterns to understand sleep duration and sleep quality. The generation unit analyzes the data collected by the collection unit and generates an optimal diet plan for each individual user. For example, the generation unit analyzes the user's physical characteristics and lifestyle based on the collected data. For example, if the user is consuming high-calorie meals, the generation unit proposes a calorie-reduced meal plan. For example, if the user has little exercise, the generation unit proposes a plan that incorporates moderate exercise. The generation unit, for example, proposes a nutritionally balanced meal plan tailored to the user's constitution. The generation unit, for example, proposes an exercise plan tailored to the user's lifestyle. The generation unit, for example, proposes a diet plan tailored to the user's target weight. The tracking unit tracks the user's progress based on the diet plan generated by the generation unit. The tracking unit, for example, periodically collects data such as the user's weight and body fat percentage. The tracking unit, for example, checks how close the user is to their target weight. The tracking unit, for example, records changes in the user's weight to track progress. The tracking unit, for example, records changes in the user's body fat percentage to track progress. The tracking unit, for example, records the user's exercise achievement to track progress. The feedback unit provides interactive feedback based on the progress tracked by the tracking unit. The feedback unit, for example, provides encouraging messages and advice according to the user's progress. The feedback unit, for example, sends an encouraging message when the user is approaching their target weight. The feedback unit, for example, sends an encouraging message when the user's progress has stagnated.The feedback unit provides, for example, advice on diet and exercise based on the user's progress. The feedback unit also sends messages to maintain motivation based on the user's progress. The feedback unit also suggests adjusting the plan based on the user's progress. As a result, the AI ​​agent according to the embodiment can provide a customized diet plan tailored to the user's physical characteristics and lifestyle, and offer interactive feedback for tracking progress and maintaining motivation.

[0067] The data collection unit collects data about the user's physical characteristics and lifestyle. Specifically, it collects detailed data such as the user's daily diet, exercise habits, and sleep patterns. For example, it records the user's meals to understand the balance of calories and nutrients they consume daily. This includes information such as the type, quantity, and timing of the food consumed. Furthermore, it records the type, frequency, and intensity of exercise the user performs. For example, if the user engages in jogging, strength training, or yoga, detailed information is collected. Regarding sleep patterns, it records the user's sleep duration and sleep quality. This includes information such as when the user goes to bed and wakes up, the number of times they woke up during the night, and the depth of their sleep. The data collection unit centrally manages this data, enabling a detailed understanding of the user's physical characteristics and lifestyle. In addition, the data collection unit can integrate with the user's devices and applications to automatically collect data. For example, it can record meals through a smartphone app or collect exercise data using a fitness tracker. This allows users to collect data without effort, providing more accurate information.

[0068] The generation unit analyzes the data collected by the collection unit to generate an optimal diet plan for each individual user. Specifically, it analyzes the user's physical characteristics and lifestyle in detail based on the collected data. For example, if a user is consuming high-calorie meals, the generation unit will propose a calorie-reduced meal plan. This includes low-calorie foods and balanced meal menus. For users with little exercise, the generation unit will propose a plan that incorporates moderate exercise. This may include light jogging, strength training, or stretching a few times a week. Furthermore, it will propose a nutritionally balanced meal plan tailored to the user's physical characteristics. For example, if a user is deficient in a particular nutrient, it will propose a menu that includes foods that supplement that nutrient. It will also propose an exercise plan tailored to the user's lifestyle. For example, it will propose a short, effective exercise menu for busy users and a longer exercise menu for users with more time. Finally, it will propose a diet plan tailored to the user's target weight. For example, it will adjust calorie restrictions and exercise volume to reach the target weight. Based on this data, the generation unit generates an optimal diet plan for the user and supports the user in achieving their goals in a healthy way.

[0069] The tracking unit tracks the user's progress based on the diet plan generated by the generation unit. Specifically, it regularly collects data such as the user's weight and body fat percentage to understand their progress. For example, it records changes in weight to see how close the user is to their target weight. It also records changes in body fat percentage to understand changes in the user's body composition. Furthermore, it records the user's exercise achievement to check whether they are exercising as planned. For example, it records how much of the set exercise menu the user has achieved to understand their progress. The tracking unit centrally manages this data, allowing for a detailed understanding of the user's progress. In addition, the tracking unit can integrate with the user's devices and applications to automatically collect data. For example, it can record weight and body fat percentage through a smartphone app or collect exercise data using a fitness tracker. This allows users to collect data without effort and provides more accurate information. The tracking unit provides detailed support for users to achieve their goals by understanding their progress.

[0070] The feedback unit provides interactive feedback based on the progress tracked by the tracking unit. Specifically, it provides encouraging messages and advice according to the user's progress. For example, when a user approaches their target weight, it sends an encouraging message to maintain their motivation. Also, if a user's progress has stalled, it sends an encouraging message to rekindle their motivation. Furthermore, it provides advice on diet and exercise according to the user's progress. For example, it suggests improvements to their diet or additional exercise routines to support the user in effectively working towards their goal. It also suggests adjusting the plan according to the user's progress. For example, if the target weight is reached, it sets a new goal and proposes a plan to achieve it. Through this interactive feedback, the feedback unit maintains the user's motivation and supports them in achieving their goals. In addition, the feedback unit can collect user feedback and continuously improve the accuracy and effectiveness of the feedback it provides. For example, it can review and improve the content of the feedback based on user opinions and requests. This allows the feedback unit to provide effective support to users and maintain their motivation towards achieving their goals.

[0071] The data collection unit can collect data such as the user's diet, exercise habits, and sleep patterns. For example, the data collection unit can record the user's diet to understand calorie intake and nutrient balance. For example, the data collection unit can record the user's exercise habits to understand the type, frequency, and intensity of exercise. For example, the data collection unit can record the user's sleep patterns to understand sleep duration and quality. By collecting detailed lifestyle data of the user, a more accurate diet plan can be generated. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, to record the user's diet, the data collection unit can provide an interface for the user to input their diet, input the entered data into a generating AI, and the generating AI can analyze the data to understand calorie intake and nutrient balance.

[0072] The generation unit can analyze the user's physical characteristics and lifestyle based on the collected data and generate calorie-restricted meal plans and plans that incorporate moderate exercise. For example, the generation unit analyzes the user's physical characteristics and lifestyle based on the collected data. For example, if the user is consuming high-calorie meals, the generation unit proposes a calorie-restricted meal plan. For example, if the user has little exercise, the generation unit proposes a plan that incorporates moderate exercise. For example, the generation unit proposes a nutritionally balanced meal plan tailored to the user's physical characteristics. For example, the generation unit proposes an exercise plan tailored to the user's lifestyle. For example, the generation unit proposes a diet plan tailored to the user's target weight. This makes it possible to provide an optimal diet plan based on the user's physical characteristics and lifestyle. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the collected data into a generation AI, which can analyze the data and generate an optimal diet plan for the user.

[0073] The tracking unit can periodically collect data such as the user's weight and body fat percentage to track their progress. For example, the tracking unit can record changes in the user's weight to understand their progress. For example, the tracking unit can record changes in the user's body fat percentage to understand their progress. For example, the tracking unit can record the user's exercise achievement to understand their progress. This allows the effectiveness of the diet plan to be checked by regularly tracking the user's progress, and the plan can be adjusted as needed. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's weight and body fat percentage data into a generating AI, which can then analyze the data to track progress.

[0074] The feedback unit can provide encouraging messages and advice according to the user's progress. For example, the feedback unit sends an encouraging message when the user approaches their target weight. For example, the feedback unit sends an encouraging message when the user's progress has stalled. For example, the feedback unit provides advice on diet and exercise according to the user's progress. For example, the feedback unit sends messages to maintain motivation according to the user's progress. For example, the feedback unit suggests adjusting the plan according to the user's progress. This allows for the maintenance of user motivation and support for healthy weight management by providing interactive feedback according to the user's progress. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user progress data into a generating AI, which can analyze the data and provide appropriate feedback.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect data during relaxed periods. For example, if the user is relaxed, the data collection unit will collect detailed data. For example, if the user is in a hurry, the data collection unit will collect simplified data. By adjusting the timing of data collection according to the user's emotions, more accurate data can be collected. 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 a generative AI, which can analyze the data and adjust the timing of data collection.

[0076] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can automatically collect data that the user has frequently entered in the past. For example, the data collection unit can prioritize suggesting collection methods that the user has used in the past (manual, voice, etc.). For example, the data collection unit can suggest the optimal collection method for a specific time period based on the user's past data collection history. In this way, by analyzing the user's past data collection history, the optimal collection method can be selected and data can be collected efficiently. 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 past data collection history into a generating AI, which can then analyze the data and select the optimal collection method.

[0077] The data collection unit can filter data based on the user's current health status and stress level during data collection. For example, if the user is tired, the data collection unit will perform simplified data collection. For example, if the user is healthy, the data collection unit will perform detailed data collection. For example, if the user is stressed, the data collection unit will prioritize collecting data that helps reduce stress. This allows for the collection of more appropriate data by filtering the data according to the user's health status and stress level. 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 stress level into a generating AI, which can then analyze and filter the data.

[0078] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data that helps reduce stress. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit will prioritize collecting simplified data. This allows for the priority collection of more important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can analyze the data and determine the priority of data to collect.

[0079] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit will prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit will prioritize the collection of data related to home. By prioritizing the collection of highly relevant data based on the user's geographical location information, more useful data can be collected. 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 a generating AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0080] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can automatically collect details of meals shared by the user on social media. For example, the data collection unit can automatically collect exercise records shared by the user on social media. For example, the data collection unit can automatically collect health information shared by the user on social media. This allows for the efficient collection of relevant data by analyzing the user's social media activity. 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 the user's social media activity data into a generating AI, which can then analyze the data and collect relevant data.

[0081] The generation unit can estimate the user's emotions and adjust the presentation of the diet plan based on the estimated emotions. For example, if the user is relaxed, the generation unit provides a plan with detailed explanations. If the user is in a hurry, the generation unit provides a concise plan. If the user is excited, the generation unit provides a visually stimulating plan. By adjusting the presentation of the diet plan according to the user's emotions, a plan that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, or not. For example, the generation unit can input user emotion data into a generative AI, which can analyze the data and adjust the presentation of the diet plan.

[0082] The generation unit can adjust the level of detail in a diet plan based on the user's health goals when generating the plan. For example, if the user wants to lose weight in a short period of time, the generation unit will provide a detailed plan. For example, if the user wants to lose weight over a long period of time, the generation unit will provide a concise plan. For example, if the user aims to maintain their health, the generation unit will provide a balanced plan. By adjusting the level of detail in the plan according to the user's health goals, the generation unit can provide the optimal plan for the user. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's health goal data into a generation AI, and the generation AI can analyze the data and adjust the level of detail in the plan.

[0083] The generation unit can apply different plan generation algorithms depending on the user's dietary preferences and allergy information when generating a diet plan. For example, if the user is a vegetarian, the generation unit will provide a vegetarian plan. If the user has a specific allergy, the generation unit will provide a plan that avoids that allergy. If the user has a specific dietary preference, the generation unit will provide a plan tailored to that preference. By applying a plan generation algorithm according to the user's dietary preferences and allergy information, it is possible to provide a safe and effective plan for the user. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's dietary preferences and allergy information into a generation AI, which can then analyze the data and apply a different plan generation algorithm.

[0084] The generation unit can estimate the user's emotions and adjust the length of the diet plan based on the estimated emotions. For example, if the user is in a hurry, the generation unit will provide a short-term plan. For example, if the user is relaxed, the generation unit will provide a long-term plan. For example, if the user is excited, the generation unit will provide a visually stimulating plan. By adjusting the length of the diet plan according to the user's emotions, a plan that is manageable for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI, or not using a generation AI. For example, the generation unit can input user emotion data into a generation AI, and the generation AI can analyze the data and adjust the length of the diet plan.

[0085] The generation unit can determine the priority of diet plans based on the user's lifestyle when generating them. For example, if the user is a morning person, the generation unit will provide a plan that emphasizes breakfast. For example, if the user is a night owl, the generation unit will provide a plan that emphasizes dinner. For example, if the user has an irregular lifestyle, the generation unit will provide a flexible plan. By determining the priority of plans based on the user's lifestyle, it is possible to provide a plan that is easy for the user to follow. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's lifestyle data into a generation AI, and the generation AI can analyze the data to determine the priority of plans.

[0086] The generation unit can adjust the order of the diet plan based on the user's past successes when generating the plan. For example, the generation unit can provide a new plan based on the user's past successful plans. For example, the generation unit can suggest the optimal order of the plan based on the user's past successes. For example, the generation unit can analyze the user's past successes and provide the most effective plan. In this way, by adjusting the order of the plan based on the user's past successes, it is possible to provide a plan that is effective for the user. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the user's past success data into a generation AI, and the generation AI can analyze the data and adjust the order of the plan.

[0087] The tracking unit can estimate the user's emotions and adjust the progress tracking method based on the estimated user emotions. For example, if the user is relaxed, the tracking unit will perform detailed progress tracking. For example, if the user is in a hurry, the tracking unit will perform concise progress tracking. For example, if the user is excited, the tracking unit will perform visually stimulating progress tracking. In this way, by adjusting the progress tracking method according to the user's emotions, the system can provide the optimal progress tracking 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 tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can input user emotion data into a generative AI, and the generative AI can analyze the data and adjust the progress tracking method.

[0088] The tracking unit can optimize the tracking algorithm by referring to the user's past progress data when tracking progress. For example, the tracking unit provides the optimal tracking algorithm based on the user's past successful progress data. For example, the tracking unit proposes the optimal tracking method from the user's past progress data. For example, the tracking unit analyzes the user's past progress data and provides the most effective tracking algorithm. In this way, the optimal tracking algorithm can be provided by referring to the user's past progress data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's past progress data into a generating AI, and the generating AI can analyze the data to optimize the tracking algorithm.

[0089] The tracking unit can adjust the tracking frequency based on changes in the user's health and lifestyle when tracking progress. For example, the tracking unit performs detailed progress tracking when the user is healthy. For example, the tracking unit performs simplified progress tracking when the user is tired. For example, the tracking unit adjusts the tracking frequency based on changes in the user's lifestyle. This allows for more appropriate progress tracking by adjusting the tracking frequency according to changes in the user's health and lifestyle. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input data on the user's health and lifestyle into a generating AI, which can then analyze the data and adjust the tracking frequency.

[0090] The tracking unit can estimate the user's emotions and determine the priority of progress tracking based on the estimated user emotions. For example, if the user is stressed, the tracking unit will prioritize tracking progress that helps reduce stress. For example, if the user is relaxed, the tracking unit will prioritize tracking detailed progress. For example, if the user is in a hurry, the tracking unit will prioritize tracking simplified progress. This allows for prioritizing the tracking of more important progress by determining the priority of progress tracking according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI or not using AI. For example, the tracking unit can input user emotion data into a generative AI, which can analyze the data to determine the priority of progress tracking.

[0091] The tracking unit can select the optimal tracking method when tracking progress, taking into account the user's geographical location information. For example, if the user is in a specific region, the tracking unit will prioritize tracking progress related to that region. For example, if the user is traveling, the tracking unit will prioritize tracking progress related to the travel destination. For example, if the user is at home, the tracking unit will prioritize tracking progress related to home. By selecting the optimal tracking method based on the user's geographical location information, more useful progress tracking can be performed. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's geographical location information into a generating AI, which can then analyze the data and select the optimal tracking method.

[0092] The tracking unit can analyze the user's social media activity and suggest tracking methods when tracking progress. For example, the tracking unit can automatically track progress shared by the user on social media. For example, the tracking unit can suggest the optimal tracking method based on health information shared by the user on social media. For example, the tracking unit can suggest the optimal tracking method based on exercise records shared by the user on social media. In this way, by analyzing the user's social media activity, the optimal tracking method can be suggested. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's social media activity data into a generating AI, which can then analyze the data and suggest the optimal tracking method.

[0093] The feedback unit can estimate the user's emotions and adjust the way it expresses the feedback based on the estimated emotions. For example, if the user is relaxed, the feedback unit will provide feedback in a calm tone. If the user is stressed, the feedback unit will emphasize encouraging messages. If the user is in a hurry, the feedback unit will provide concise and to-the-point feedback. By adjusting the way it expresses the feedback according to the user's emotions, it is possible to provide feedback that is 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 feedback unit may be performed using AI or not using AI. For example, the feedback unit can input user emotion data into a generative AI, which can analyze the data and adjust the way it expresses the feedback.

[0094] The feedback unit can provide optimal feedback by referring to the user's past feedback history when providing feedback. For example, the feedback unit may prioritize the format of feedback that the user has received favorably in the past. For example, the feedback unit may adjust the current feedback by referring to the content of feedback the user has received in the past. For example, the feedback unit may provide feedback at the optimal timing based on the user's past feedback history. In this way, optimal feedback can be provided by referring to the user's past feedback history. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's past feedback history data into a generating AI, and the generating AI can analyze the data to provide optimal feedback.

[0095] The feedback unit can customize the content of feedback based on changes in the user's health status and lifestyle when providing feedback. For example, if the user is in good health, the feedback unit will provide positive feedback. For example, if the user is tired, the feedback unit will provide feedback encouraging rest. For example, if the user's lifestyle changes, the feedback unit will provide feedback corresponding to that change. This allows for the provision of more appropriate feedback by customizing the content of feedback according to changes in the user's health status and lifestyle. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input data on the user's health status and lifestyle into a generating AI, which can then analyze the data and customize the content of the feedback.

[0096] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit will prioritize providing feedback that helps reduce stress. For example, if the user is relaxed, the feedback unit will prioritize providing detailed feedback. For example, if the user is in a hurry, the feedback unit will prioritize providing simplified feedback. This allows for the prioritization of more important feedback by determining the priority of feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not using AI. For example, the feedback unit can input user emotion data into a generative AI, which can analyze the data to determine the priority of feedback.

[0097] The feedback unit can provide optimal feedback by considering the user's geographical location information when providing feedback. For example, if the user is in a specific region, the feedback unit will provide feedback relevant to that region. For example, if the user is traveling, the feedback unit will provide feedback relevant to the travel destination. For example, if the user is at home, the feedback unit will provide feedback relevant to home. By providing optimal feedback based on the user's geographical location information, more useful feedback can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's geographical location information into a generating AI, and the generating AI can analyze the data to provide optimal feedback.

[0098] The feedback unit can analyze the user's social media activity and propose a method of providing feedback when providing feedback. For example, the feedback unit can provide optimal feedback based on the progress the user has shared on social media. For example, the feedback unit can provide optimal feedback based on the health information the user has shared on social media. For example, the feedback unit can provide optimal feedback based on the exercise records the user has shared on social media. In this way, by analyzing the user's social media activity, it is possible to propose an optimal method of feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the user's social media activity data into a generating AI, and the generating AI can analyze the data and propose an optimal method of feedback.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The data collection unit can collect data such as the user's diet, exercise habits, and sleep patterns. For example, the data collection unit can record the user's diet to understand calorie intake and nutrient balance. For example, the data collection unit can record the user's exercise habits to understand the type, frequency, and intensity of exercise. For example, the data collection unit can record the user's sleep patterns to understand sleep duration and quality. By collecting detailed lifestyle data of the user, a more accurate diet plan can be generated. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, to record the user's diet, the data collection unit can provide an interface for the user to input their diet, input the entered data into a generating AI, and the generating AI can analyze the data to understand calorie intake and nutrient balance.

[0101] The generation unit can analyze the user's physical characteristics and lifestyle based on the collected data and generate calorie-restricted meal plans and plans that incorporate moderate exercise. For example, the generation unit analyzes the user's physical characteristics and lifestyle based on the collected data. For example, if the user is consuming high-calorie meals, the generation unit proposes a calorie-restricted meal plan. For example, if the user has little exercise, the generation unit proposes a plan that incorporates moderate exercise. For example, the generation unit proposes a nutritionally balanced meal plan tailored to the user's physical characteristics. For example, the generation unit proposes an exercise plan tailored to the user's lifestyle. For example, the generation unit proposes a diet plan tailored to the user's target weight. This makes it possible to provide an optimal diet plan based on the user's physical characteristics and lifestyle. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the collected data into a generation AI, which can analyze the data and generate an optimal diet plan for the user.

[0102] The tracking unit can periodically collect data such as the user's weight and body fat percentage to track their progress. For example, the tracking unit can record changes in the user's weight to understand their progress. For example, the tracking unit can record changes in the user's body fat percentage to understand their progress. For example, the tracking unit can record the user's exercise achievement to understand their progress. This allows the effectiveness of the diet plan to be checked by regularly tracking the user's progress, and the plan can be adjusted as needed. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's weight and body fat percentage data into a generating AI, which can then analyze the data to track progress.

[0103] The feedback unit can provide encouraging messages and advice according to the user's progress. For example, the feedback unit sends an encouraging message when the user approaches their target weight. For example, the feedback unit sends an encouraging message when the user's progress has stalled. For example, the feedback unit provides advice on diet and exercise according to the user's progress. For example, the feedback unit sends messages to maintain motivation according to the user's progress. For example, the feedback unit suggests adjusting the plan according to the user's progress. This allows for the maintenance of user motivation and support for healthy weight management by providing interactive feedback according to the user's progress. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user progress data into a generating AI, which can analyze the data and provide appropriate feedback.

[0104] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect data during relaxed periods. For example, if the user is relaxed, the data collection unit will collect detailed data. For example, if the user is in a hurry, the data collection unit will collect simplified data. By adjusting the timing of data collection according to the user's emotions, more accurate data can be collected. 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 a generative AI, which can analyze the data and adjust the timing of data collection.

[0105] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can automatically collect data that the user has frequently entered in the past. For example, the data collection unit can prioritize suggesting collection methods that the user has used in the past (manual, voice, etc.). For example, the data collection unit can suggest the optimal collection method for a specific time period based on the user's past data collection history. In this way, by analyzing the user's past data collection history, the optimal collection method can be selected and data can be collected efficiently. 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 past data collection history into a generating AI, which can then analyze the data and select the optimal collection method.

[0106] The data collection unit can filter data based on the user's current health status and stress level during data collection. For example, if the user is tired, the data collection unit will perform simplified data collection. For example, if the user is healthy, the data collection unit will perform detailed data collection. For example, if the user is stressed, the data collection unit will prioritize collecting data that helps reduce stress. This allows for the collection of more appropriate data by filtering the data according to the user's health status and stress level. 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 stress level into a generating AI, which can then analyze and filter the data.

[0107] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data that helps reduce stress. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit will prioritize collecting simplified data. This allows for the priority collection of more important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, 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, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can analyze the data and determine the priority of data to collect.

[0108] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit will prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit will prioritize the collection of data related to home. By prioritizing the collection of highly relevant data based on the user's geographical location information, more useful data can be collected. 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 a generating AI, which can then analyze the data and prioritize the collection of highly relevant data.

[0109] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can automatically collect details of meals shared by the user on social media. For example, the data collection unit can automatically collect exercise records shared by the user on social media. For example, the data collection unit can automatically collect health information shared by the user on social media. This allows for the efficient collection of relevant data by analyzing the user's social media activity. 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 the user's social media activity data into a generating AI, which can then analyze the data and collect relevant data.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects data about the user's physical characteristics and lifestyle. Specifically, it collects data such as the user's diet, exercise habits, and sleep patterns, recording data such as daily calorie intake, exercise frequency, and sleep duration. Step 2: The generation unit analyzes the data collected by the collection unit and generates an optimal diet plan for each individual user. Specifically, it analyzes the user's physical characteristics and lifestyle, and proposes a calorie-reduced meal plan if the user consumes high-calorie foods, or a plan that incorporates moderate exercise if the user has little exercise habits. Step 3: The tracking unit tracks the user's progress based on the diet plan generated by the generation unit. Specifically, it periodically collects data such as the user's weight and body fat percentage to check how close they are to their target weight. Step 4: The feedback unit provides interactive feedback based on the progress tracked by the tracking unit. Specifically, it provides encouraging messages and advice according to the user's progress, and sends messages to maintain motivation if progress has stalled.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the collection unit, generation unit, tracking unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit records the user's diet and exercise habits using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A collects the data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to generate an optimal diet plan. The tracking unit periodically records the user's weight and body fat percentage using the control unit 46A of the smart device 14 and tracks their progress. The feedback unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and provides interactive feedback according to the user's progress. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the collection unit, generation unit, tracking unit, and feedback unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit records the user's diet and exercise habits using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A collects the data. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which analyzes the collected data to generate an optimal diet plan. The tracking unit periodically records the user's weight and body fat percentage using the control unit 46A of the smart glasses 214 and tracks their progress. The feedback unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which provides interactive feedback according to the user's progress. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the collection unit, generation unit, tracking unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit records the user's diet and exercise habits using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A collects the data. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to generate an optimal diet plan. The tracking unit periodically records the user's weight and body fat percentage using the control unit 46A of the headset terminal 314 and tracks their progress. The feedback unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and provides interactive feedback according to the user's progress. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the collection unit, generation unit, tracking unit, and feedback unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the collection unit records the user's diet and exercise habits using the camera 42 and microphone 238 of the robot 414, and the control unit 46A collects the data. The generation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which analyzes the collected data to generate an optimal diet plan. The tracking unit tracks the user's progress by periodically recording the user's weight and body fat percentage using the control unit 46A of the robot 414. The feedback unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, which provides interactive feedback according to the user's progress. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) A data collection unit that collects data on the user's physical characteristics and lifestyle, A generation unit analyzes the data collected by the aforementioned collection unit and generates an optimal diet plan for each individual user, A tracking unit tracks the user's progress based on the diet plan generated by the generation unit, The system includes a feedback unit that provides interactive feedback based on the progress tracked by the tracking unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data on users' dietary habits, exercise routines, sleep patterns, and more. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Based on the collected data, the system analyzes the user's physical characteristics and lifestyle to generate calorie-restricted meal plans and plans that incorporate moderate exercise. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned tracking unit is The system regularly collects data such as the user's weight and body fat percentage to track their progress. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is Provides encouraging messages and advice based on the user's progress. The system described in Appendix 1, characterized by the features described herein. (Note 6) 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 7) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, filtering is performed based on the user's current health status and stress level. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The generating unit is The system estimates the user's emotions and adjusts how the diet plan is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When generating a diet plan, adjust the level of detail in the plan based on the user's health goals. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating a diet plan, different plan generation algorithms are applied depending on the user's dietary preferences and allergy information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the length of the diet plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When generating a diet plan, the system prioritizes the plan based on the user's lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a diet plan, the order of the plan is adjusted based on the user's past success stories. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned tracking unit is We estimate the user's emotions and adjust the progress tracking method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned tracking unit is When tracking progress, the tracking algorithm is optimized by referencing the user's past progress data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned tracking unit is When tracking progress, adjust the tracking frequency based on changes in the user's health and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned tracking unit is It estimates user sentiment and prioritizes progress tracking based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned tracking unit is When tracking progress, the optimal tracking method is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned tracking unit is When tracking progress, we analyze users' social media activity and suggest tracking methods. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is When providing feedback, we refer to the user's past feedback history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is When providing feedback, customize the content of the feedback based on the user's health status and changes in lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is When providing feedback, we take the user's geographical location into consideration to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, we analyze the user's social media activity and suggest methods for providing feedback. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 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 data collection unit that collects data on the user's physical characteristics and lifestyle, A generation unit analyzes the data collected by the aforementioned collection unit and generates an optimal diet plan for each individual user, A tracking unit tracks the user's progress based on the diet plan generated by the generation unit, The system includes a feedback unit that provides interactive feedback based on the progress tracked by the tracking unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data on users' dietary habits, exercise routines, sleep patterns, and more. The system according to feature 1.

3. The generating unit is Based on the collected data, the system analyzes the user's physical characteristics and lifestyle to generate calorie-restricted meal plans and plans that incorporate moderate exercise. The system according to feature 1.

4. The aforementioned tracking unit is The system regularly collects data such as the user's weight and body fat percentage to track their progress. The system according to feature 1.

5. The aforementioned feedback unit is Provides encouraging messages and advice based on the user's progress. The system according to feature 1.

6. 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.

7. The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system according to feature 1.

8. The aforementioned collection unit is During data collection, filtering is performed based on the user's current health status and stress level. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data based on the user's geographical location information. The system according to feature 1.

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