system

Abstract

The system according to this embodiment aims to provide exercise plans tailored to the individual needs of each user, and to support motivation maintenance and goal setting. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's behavior patterns and health data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates an optimal exercise plan based on the analysis results obtained by the analysis unit. The provision unit provides the exercise plan generated by the generation 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 performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003] <*

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to provide an exercise plan tailored to the needs of individual users, and there is a problem that motivation maintenance and goal setting support are not sufficiently provided.

[0005] The system according to the embodiment aims to provide an exercise plan tailored to the needs of individual users and support motivation maintenance and goal setting.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's behavior patterns and health data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates an optimal exercise plan based on the analysis results obtained by the analysis unit. The provision unit provides the exercise plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide exercise plans tailored to the individual user's needs and support motivation maintenance and goal setting. [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 applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The Fit Agent System according to an embodiment of the present invention is a system that utilizes AI to provide exercise plans tailored to individual needs, supporting motivation maintenance and goal setting. The Fit Agent System collects the user's behavioral patterns and health data, and the AI ​​analyzes this data to generate an optimal exercise plan. The generated exercise plan is personalized to the user's needs and is ideal for people who want to make exercise a habit but don't know how, or for those who have difficulty maintaining motivation. The AI ​​agent aims to improve the user's exercise continuation rate, improve their health status, and increase their life satisfaction. For example, the AI ​​suggests appropriate exercise intensity and frequency based on the user's past exercise history and health data. It also adjusts the exercise plan based on user feedback and continuously optimizes it. This system targets busy modern people in their 20s to 50s who are health-conscious, and provides innovative exercise plans tailored to individual lifestyles. Through the Fit Agent, users can make exercise a habit and maintain their health. As a result, the Fit Agent System can provide an optimal exercise plan based on the user's behavioral patterns and health data.

[0029] The Fit Agent System according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's behavioral patterns and health data. For example, the collection unit can collect behavioral patterns such as the user's daily activities, exercise habits, and eating patterns. The collection unit can also collect health data such as heart rate, blood pressure, weight, and sleep data. The collection unit collects data using, for example, a wearable device or a smartphone application. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the data and evaluate the user's health status. Based on the collected data, the analysis unit can perform a detailed analysis of the user's health status and behavioral patterns. The generation unit generates an optimal exercise plan based on the analysis results obtained by the analysis unit. For example, the generation unit uses AI to generate a personalized exercise plan tailored to the user's needs. Based on the user's past exercise history and health data, the generation unit can suggest appropriate exercise intensity and frequency. The provision unit provides the exercise plan generated by the generation unit. For example, the provision unit can receive user feedback and adjust the exercise plan. The service provider can continuously optimize the exercise plan based on user feedback. This allows the Fit Agent System according to the embodiment to provide an optimal exercise plan based on the user's behavioral patterns and health data.

[0030] The data collection unit collects user behavior patterns and health data. Specifically, it can collect detailed information on users' daily activities, exercise habits, and eating patterns. For example, it records how much a user walks in a day, what kind of exercise they do, the content of their meals, and their calorie intake. The data collection unit can also collect health data such as heart rate, blood pressure, weight, and sleep data. This data is collected in real time using wearable devices and smartphone apps. Wearable devices are worn on the user's wrist or chest and constantly monitor vital signs such as heart rate, blood pressure, and body temperature. Smartphone apps record meal and exercise content based on user input and measure steps and distance traveled using GPS functionality. This allows the data collection unit to comprehensively collect user behavior patterns and health data and build a detailed database. Furthermore, the data collection unit can send this data to a cloud server and link it with other systems and departments. For example, collected data is stored in the cloud so that the analysis and generation units can access it. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The analysis department analyzes the data collected by the data collection department. Specifically, it uses AI to analyze the data and evaluate the user's health status. The AI ​​uses machine learning algorithms to analyze the user's health status and behavioral patterns in detail from the collected data. For example, it evaluates stress levels from fluctuations in heart rate and blood pressure, and evaluates sleep quality from sleep data. It also analyzes exercise habits and eating patterns to identify areas for improvement in the user's lifestyle. Furthermore, the analysis department can also evaluate long-term health risks by utilizing historical data and statistical information. For example, it can predict the risk of specific diseases based on past health data and take early countermeasures. The AI ​​can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the analysis department to not only assess health status in real time but also to handle long-term health management and anomaly detection, improving the reliability and safety of the entire system. Furthermore, the analysis department can collect user feedback and continuously improve the accuracy and effectiveness of the analysis results. This allows the analysis department to accurately assess the user's health status and provide appropriate advice.

[0032] The generation unit generates an optimal exercise plan based on the analysis results obtained by the analysis unit. Specifically, it uses AI to generate a personalized exercise plan tailored to the user's needs. The AI ​​suggests appropriate exercise intensity and frequency based on the user's past exercise history and health data. For example, it suggests exercise menus such as walking, running, and strength training according to the user's fitness level and health condition. It can also adjust the type and timing of exercise according to the user's goals and preferences. Furthermore, the generation unit can continuously optimize the exercise plan based on user feedback. For example, when a user provides feedback on an exercise plan, the AI ​​learns from that feedback and incorporates it into the next exercise plan. This allows the generation unit to provide an optimal exercise plan that meets the user's needs and supports the user in achieving their health goals. In addition, the generation unit can monitor the progress of the exercise plan and adjust it as needed. This allows the generation unit to provide a flexible exercise plan that suits the user's health condition and goals, supporting the user's health management.

[0033] The service provider provides the exercise plan generated by the generation unit. Specifically, it can receive user feedback and adjust the exercise plan. Based on user feedback, the service provider can continuously optimize the exercise plan. For example, when a user provides feedback on the exercise plan, the service provider collects that feedback and sends it to the generation unit. The generation unit adjusts the exercise plan based on that feedback, and the service provider provides the adjusted exercise plan to the user. This allows the service provider to provide the optimal exercise plan tailored to the user's needs and support the user in achieving their health goals. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the exercise plan. For example, when a user provides feedback on the exercise plan, the service provider collects that feedback and sends it to the generation unit. The generation unit adjusts the exercise plan based on that feedback, and the service provider provides the adjusted exercise plan to the user. This allows the service provider to provide the optimal exercise plan tailored to the user's needs and support the user in achieving their health goals. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the exercise plan. This allows the service provider to provide the optimal exercise plan tailored to the user's needs and support the user in achieving their health goals.

[0034] The generation unit can generate an exercise plan based on the user's past exercise history and health data. For example, the generation unit can generate an optimal exercise plan based on the user's past exercise history. The generation unit can also adjust the exercise intensity and frequency based on the user's past exercise history. The generation unit can also analyze the user's past exercise history and customize the exercise plan. This allows for the generation of a more appropriate exercise plan based on the user's past exercise history and health data. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past exercise history data into a generation AI and have the generation AI perform the generation of the exercise plan.

[0035] The service provider can receive user feedback and adjust the exercise plan. The service provider can collect feedback such as user satisfaction, exercise effectiveness, and areas for improvement. Based on user feedback, the service provider can continuously optimize the exercise plan. The service provider can also adjust the content and delivery method of the exercise plan based on user feedback. This allows for the provision of more effective exercise plans by adjusting the exercise plan based on user feedback. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user feedback data into a generating AI and have the generating AI perform the adjustment of the exercise plan.

[0036] The data collection unit can collect user behavior patterns and health data in real time. For example, the data collection unit can collect the user's daily activities and health data in real time using wearable devices or smartphone apps. The data collection unit can collect data in real time, taking into account the data update frequency and latency. By collecting user behavior patterns and health data in real time, the data collection unit can provide exercise plans based on the latest data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the data collected in real time into a generating AI and have the generating AI perform data analysis.

[0037] The analysis unit can analyze the collected data and evaluate the user's health status. For example, the analysis unit can use AI to analyze the collected data and evaluate the user's health status. Based on the collected data, the analysis unit can perform a detailed analysis of the user's health status and behavioral patterns. The analysis unit can also evaluate the health status based on the user's health checkup results and self-reported data. By analyzing the collected data and evaluating the user's health status, it is possible to provide a more appropriate exercise plan. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the health status evaluation.

[0038] The generation unit can generate personalized exercise plans tailored to the user's needs. For example, the generation unit collects the user's needs, such as their exercise goals, preferences, and constraints, and generates an exercise plan based on that. The generation unit can also adjust the exercise intensity and frequency according to the user's needs. The generation unit can also customize the exercise plan, taking the user's needs into consideration. This allows for the provision of more effective exercise plans by generating personalized exercise plans tailored to the user's needs. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user needs data into a generation AI and have the generation AI perform the generation of the exercise plan.

[0039] The data collection unit can analyze the user's past behavior patterns and select the optimal data collection method. For example, the data collection unit can determine the timing of data collection based on the time of day when the user frequently exercised in the past. The data collection unit can also select the optimal data collection method (wearable device, smartphone app, etc.) from the user's past exercise history. The data collection unit can also analyze the user's past behavior patterns and adjust the frequency of data collection. This allows for more effective data collection by analyzing the user's past behavior patterns and selecting the optimal data collection method. 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 behavior pattern data into a generating AI and have the generating AI select the optimal data collection method.

[0040] The data collection unit can filter health data based on the user's current lifestyle and activity level. For example, if the user is at work, the data collection unit may refrain from collecting exercise data. If the user is exercising, the data collection unit may also collect data corresponding to the exercise intensity. If the user is resting, the data collection unit may also collect heart rate and sleep data. This allows for the collection of more relevant data by filtering the data based on the user's current lifestyle and activity 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 the user's lifestyle data into a generating AI and have the generating AI perform the data filtering.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, if the user is at a gym, the data collection unit can prioritize the collection of exercise data. If the user is at home, the data collection unit can also prioritize the collection of relaxation data. If the user is out, the data collection unit can also prioritize the collection of step count and distance traveled data. By considering the user's geographical location when collecting data, more relevant 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 data into a generating AI and have the generating AI determine the priority of the data.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user posts about exercise on social media, the data collection unit can collect that data. The data collection unit can also collect data if a user posts about stress on social media. The data collection unit can also collect data if a user posts about relaxation on social media. By analyzing and collecting data from a user's social media activity, more relevant 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 social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can analyze important health data (heart rate, blood pressure, etc.) in detail. The analysis unit can also analyze general health data (steps, calorie consumption, etc.) in a simplified manner. The analysis unit can also adjust the level of detail of the analysis according to the user's health status. This allows for more detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. The analysis unit can also apply a gait pattern analysis algorithm to step count data. The analysis unit can also apply a sleep stage analysis algorithm to sleep data. This allows for more appropriate analysis by applying different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also analyze the most recent data while referring to past data. The analysis unit can also prioritize the analysis of data collected during a specific period. This allows for the prioritization of analysis based on the data collection period, thereby prioritizing the analysis of the most recent data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. This allows for prioritizing the analysis of more relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0047] The generation unit can generate an optimal exercise plan by analyzing the user's past exercise history during the generation process. For example, the generation unit can generate an optimal exercise plan based on the user's past exercise history. The generation unit can also adjust the exercise intensity and frequency based on the user's past exercise history. The generation unit can also customize the exercise plan by analyzing the user's past exercise history. This allows for the provision of a more effective exercise plan by analyzing the user's past exercise history and generating an optimal exercise plan. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past exercise history data into a generation AI and have the generation AI perform the generation of the exercise plan.

[0048] The generation unit can customize the exercise plan based on the user's current health status during generation. For example, the generation unit evaluates the user's current health status and generates an optimal exercise plan. The generation unit can also adjust the exercise intensity and frequency according to the user's health status. The generation unit can also customize the exercise plan taking the user's health status into consideration. This allows for the provision of a more appropriate exercise plan by customizing the exercise plan based on the user's current health status. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's health status data into a generation AI and have the generation AI perform the customization of the exercise plan.

[0049] The generation unit can generate an optimal exercise plan by considering the user's geographical location information during the generation process. For example, if the user is at a gym, the generation unit will generate an exercise plan that can be done at the gym. If the user is at home, the generation unit can also generate an exercise plan that can be done at home. If the user is out, the generation unit can also generate an exercise plan that can be done outdoors. By generating an exercise plan that considers the user's geographical location information, a more appropriate exercise plan can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information data into a generation AI and have the generation AI perform the exercise plan generation.

[0050] The generation unit can analyze the user's social media activity and propose an exercise plan during the generation process. For example, if the user has posted about exercise on social media, the generation unit can propose an exercise plan based on that data. The generation unit can also propose an exercise plan based on the user's posts about stress on social media. The generation unit can also propose an exercise plan based on the user's posts about relaxation on social media. By analyzing the user's social media activity and proposing an exercise plan accordingly, it is possible to provide a more appropriate exercise plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI execute the exercise plan proposal.

[0051] The service provider can analyze the user's past feedback to select the optimal delivery method at the time of delivery. For example, the service provider can select the optimal delivery method based on the exercise plan the user has preferred in the past. The service provider can also adjust the frequency of exercise plan delivery based on the user's past feedback. The service provider can also analyze the user's past feedback and customize the delivery method. This allows for the provision of more effective exercise plans by analyzing the user's past feedback and selecting the optimal delivery method. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past feedback data into a generating AI and have the generating AI select the optimal delivery method.

[0052] The service provider can customize the means of delivering the exercise plan based on the user's current lifestyle at the time of delivery. For example, if the user is at work, the service provider can provide an exercise plan that can be completed in a short amount of time. If the user is at home, the service provider can also provide an exercise plan that can be done at home. If the user is out, the service provider can also provide an exercise plan that can be done outdoors. By customizing the means of delivering the exercise plan based on the user's current lifestyle, a more appropriate exercise plan can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's lifestyle data into a generating AI and have the generating AI perform the customization of the means of delivering the exercise plan.

[0053] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is at a gym, the service provider can provide an exercise plan that can be done at the gym. If the user is at home, the service provider can also provide an exercise plan that can be done at home. If the user is out, the service provider can also provide an exercise plan that can be done outdoors. By selecting the optimal delivery method by considering the user's geographical location information, a more appropriate exercise plan can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's geographical location information data into a generating AI and have the generating AI select the optimal delivery method.

[0054] The service provider can analyze the user's social media activity at the time of delivery and propose a means of providing an exercise plan. For example, if the user has posted about exercise on social media, the service provider can provide an exercise plan based on that data. The service provider can also provide an exercise plan based on the user's posts about stress on social media. The service provider can also provide an exercise plan based on the user's posts about relaxation on social media. By analyzing the user's social media activity and proposing a means of providing an exercise plan, a more appropriate exercise plan can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI propose a means of providing an exercise plan.

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

[0056] The fitness agent system can adjust the timing of exercise plan delivery by taking into account the user's geographical location. For example, if the user is at a gym, it can adjust the timing of providing an exercise plan that can be done at the gym. If the user is at home, it can also adjust the timing of providing an exercise plan that can be done at home. If the user is out, it can also adjust the timing of providing an exercise plan that can be done outdoors. By adjusting the timing of exercise plan delivery by taking into account the user's geographical location, a more appropriate exercise plan can be provided. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI. For example, the delivery unit can input the user's geographical location data into a generating AI and have the generating AI perform the adjustment of the timing of exercise plan delivery.

[0057] The Fit Agent system can analyze a user's social media activity and adjust the timing of exercise plan delivery. For example, if a user posts about exercise on social media, the timing of exercise plan delivery can be adjusted based on that data. Similarly, if a user posts about stress on social media, the timing of exercise plan delivery can be adjusted based on that data. If a user posts about relaxation on social media, the timing of exercise plan delivery can be adjusted based on that data. By analyzing a user's social media activity and adjusting the timing of exercise plan delivery, a more appropriate exercise plan can be provided. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's social media activity data into a generating AI and have the generating AI perform the adjustment of the timing of exercise plan delivery.

[0058] The Fit Agent system can analyze the user's past feedback and adjust the timing of exercise plan delivery. For example, it can select the optimal delivery timing based on the exercise plans the user has preferred in the past. It can also adjust the frequency of exercise plan delivery based on the user's past feedback. It can also customize the delivery timing by analyzing the user's past feedback. This allows for the provision of more effective exercise plans by analyzing the user's past feedback and adjusting the delivery timing. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past feedback data into a generating AI and have the generating AI perform the adjustment of the exercise plan delivery timing.

[0059] The Fit Agent system can adjust the timing of exercise plan delivery based on the user's current lifestyle. For example, if the user is at work, it can adjust the timing of delivery of short-duration exercise plans. If the user is at home, it can also adjust the timing of delivery of exercise plans that can be done at home. If the user is out, it can also adjust the timing of delivery of exercise plans that can be done outdoors. By adjusting the timing of delivery of exercise plans based on the user's current lifestyle, it is possible to provide more appropriate exercise plans. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's lifestyle data into a generating AI and have the generating AI perform the adjustment of the timing of exercise plan delivery.

[0060] The Fit Agent system can select how to provide an exercise plan, taking into account the user's geographical location. For example, if the user is at a gym, it can provide an exercise plan that can be done at the gym. If the user is at home, it can provide an exercise plan that can be done at home. If the user is out, it can provide an exercise plan that can be done outdoors. By selecting how to provide the exercise plan, taking into account the user's geographical location, a more appropriate exercise plan can be provided. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can input the user's geographical location data into a generating AI and have the generating AI select how to provide the exercise plan.

[0061] The Fit Agent system can analyze a user's social media activity and propose methods for providing an exercise plan. For example, if a user posts about exercise on social media, it can provide an exercise plan based on that data. If a user posts about stress on social media, it can also provide an exercise plan based on that data. If a user posts about relaxation on social media, it can also provide an exercise plan based on that data. By analyzing the user's social media activity and proposing methods for providing an exercise plan, it is possible to provide a more appropriate exercise plan. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can input the user's social media activity data into a generating AI and have the generating AI execute a proposal for how to provide an exercise plan.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects user behavior patterns and health data. The data collection unit can collect behavior patterns such as the user's daily activities, exercise habits, and eating patterns. It can also collect health data such as heart rate, blood pressure, weight, and sleep data. The data collection unit collects data using, for example, wearable devices or smartphone apps. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses AI to analyze the data and evaluate the user's health status. Based on the collected data, the analysis unit can perform a detailed analysis of the user's health status and behavioral patterns. Step 3: The generation unit generates an optimal exercise plan based on the analysis results obtained by the analysis unit. For example, the generation unit uses AI to generate a personalized exercise plan tailored to the user's needs. The generation unit can suggest appropriate exercise intensity and frequency based on the user's past exercise history and health data. Step 4: The providing unit provides the exercise plan generated by the generating unit. The providing unit can, for example, receive user feedback and adjust the exercise plan. The providing unit can continuously optimize the exercise plan based on user feedback.

[0064] (Example of form 2) The Fit Agent System according to an embodiment of the present invention is a system that utilizes AI to provide exercise plans tailored to individual needs, supporting motivation maintenance and goal setting. The Fit Agent System collects the user's behavioral patterns and health data, and the AI ​​analyzes this data to generate an optimal exercise plan. The generated exercise plan is personalized to the user's needs and is ideal for people who want to make exercise a habit but don't know how, or for those who have difficulty maintaining motivation. The AI ​​agent aims to improve the user's exercise continuation rate, improve their health status, and increase their life satisfaction. For example, the AI ​​suggests appropriate exercise intensity and frequency based on the user's past exercise history and health data. It also adjusts the exercise plan based on user feedback and continuously optimizes it. This system targets busy modern people in their 20s to 50s who are health-conscious, and provides innovative exercise plans tailored to individual lifestyles. Through the Fit Agent, users can make exercise a habit and maintain their health. As a result, the Fit Agent System can provide an optimal exercise plan based on the user's behavioral patterns and health data.

[0065] The Fit Agent System according to this embodiment comprises a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects the user's behavioral patterns and health data. For example, the collection unit can collect behavioral patterns such as the user's daily activities, exercise habits, and eating patterns. The collection unit can also collect health data such as heart rate, blood pressure, weight, and sleep data. The collection unit collects data using, for example, a wearable device or a smartphone application. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses AI to analyze the data and evaluate the user's health status. Based on the collected data, the analysis unit can perform a detailed analysis of the user's health status and behavioral patterns. The generation unit generates an optimal exercise plan based on the analysis results obtained by the analysis unit. For example, the generation unit uses AI to generate a personalized exercise plan tailored to the user's needs. Based on the user's past exercise history and health data, the generation unit can suggest appropriate exercise intensity and frequency. The provision unit provides the exercise plan generated by the generation unit. For example, the provision unit can receive user feedback and adjust the exercise plan. The service provider can continuously optimize the exercise plan based on user feedback. This allows the Fit Agent System according to the embodiment to provide an optimal exercise plan based on the user's behavioral patterns and health data.

[0066] The data collection unit collects user behavior patterns and health data. Specifically, it can collect detailed information on users' daily activities, exercise habits, and eating patterns. For example, it records how much a user walks in a day, what kind of exercise they do, the content of their meals, and their calorie intake. The data collection unit can also collect health data such as heart rate, blood pressure, weight, and sleep data. This data is collected in real time using wearable devices and smartphone apps. Wearable devices are worn on the user's wrist or chest and constantly monitor vital signs such as heart rate, blood pressure, and body temperature. Smartphone apps record meal and exercise content based on user input and measure steps and distance traveled using GPS functionality. This allows the data collection unit to comprehensively collect user behavior patterns and health data and build a detailed database. Furthermore, the data collection unit can send this data to a cloud server and link it with other systems and departments. For example, collected data is stored in the cloud so that the analysis and generation units can access it. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0067] The analysis department analyzes the data collected by the data collection department. Specifically, it uses AI to analyze the data and evaluate the user's health status. The AI ​​uses machine learning algorithms to analyze the user's health status and behavioral patterns in detail from the collected data. For example, it evaluates stress levels from fluctuations in heart rate and blood pressure, and evaluates sleep quality from sleep data. It also analyzes exercise habits and eating patterns to identify areas for improvement in the user's lifestyle. Furthermore, the analysis department can also evaluate long-term health risks by utilizing historical data and statistical information. For example, it can predict the risk of specific diseases based on past health data and take early countermeasures. The AI ​​can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the analysis department to not only assess health status in real time but also to handle long-term health management and anomaly detection, improving the reliability and safety of the entire system. Furthermore, the analysis department can collect user feedback and continuously improve the accuracy and effectiveness of the analysis results. This allows the analysis department to accurately assess the user's health status and provide appropriate advice.

[0068] The generation unit generates an optimal exercise plan based on the analysis results obtained by the analysis unit. Specifically, it uses AI to generate a personalized exercise plan tailored to the user's needs. The AI ​​suggests appropriate exercise intensity and frequency based on the user's past exercise history and health data. For example, it suggests exercise menus such as walking, running, and strength training according to the user's fitness level and health condition. It can also adjust the type and timing of exercise according to the user's goals and preferences. Furthermore, the generation unit can continuously optimize the exercise plan based on user feedback. For example, when a user provides feedback on an exercise plan, the AI ​​learns from that feedback and incorporates it into the next exercise plan. This allows the generation unit to provide an optimal exercise plan that meets the user's needs and supports the user in achieving their health goals. In addition, the generation unit can monitor the progress of the exercise plan and adjust it as needed. This allows the generation unit to provide a flexible exercise plan that suits the user's health condition and goals, supporting the user's health management.

[0069] The service provider provides the exercise plan generated by the generation unit. Specifically, it can receive user feedback and adjust the exercise plan. Based on user feedback, the service provider can continuously optimize the exercise plan. For example, when a user provides feedback on the exercise plan, the service provider collects that feedback and sends it to the generation unit. The generation unit adjusts the exercise plan based on that feedback, and the service provider provides the adjusted exercise plan to the user. This allows the service provider to provide the optimal exercise plan tailored to the user's needs and support the user in achieving their health goals. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the exercise plan. For example, when a user provides feedback on the exercise plan, the service provider collects that feedback and sends it to the generation unit. The generation unit adjusts the exercise plan based on that feedback, and the service provider provides the adjusted exercise plan to the user. This allows the service provider to provide the optimal exercise plan tailored to the user's needs and support the user in achieving their health goals. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the exercise plan. This allows the service provider to provide the optimal exercise plan tailored to the user's needs and support the user in achieving their health goals.

[0070] The generation unit can generate an exercise plan based on the user's past exercise history and health data. For example, the generation unit can generate an optimal exercise plan based on the user's past exercise history. The generation unit can also adjust the exercise intensity and frequency based on the user's past exercise history. The generation unit can also analyze the user's past exercise history and customize the exercise plan. This allows for the generation of a more appropriate exercise plan based on the user's past exercise history and health data. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past exercise history data into a generation AI and have the generation AI perform the generation of the exercise plan.

[0071] The service provider can receive user feedback and adjust the exercise plan. The service provider can collect feedback such as user satisfaction, exercise effectiveness, and areas for improvement. Based on user feedback, the service provider can continuously optimize the exercise plan. The service provider can also adjust the content and delivery method of the exercise plan based on user feedback. This allows for the provision of more effective exercise plans by adjusting the exercise plan based on user feedback. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user feedback data into a generating AI and have the generating AI perform the adjustment of the exercise plan.

[0072] The data collection unit can collect user behavior patterns and health data in real time. For example, the data collection unit can collect the user's daily activities and health data in real time using wearable devices or smartphone apps. The data collection unit can collect data in real time, taking into account the data update frequency and latency. By collecting user behavior patterns and health data in real time, the data collection unit can provide exercise plans based on the latest data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the data collected in real time into a generating AI and have the generating AI perform data analysis.

[0073] The analysis unit can analyze the collected data and evaluate the user's health status. For example, the analysis unit can use AI to analyze the collected data and evaluate the user's health status. Based on the collected data, the analysis unit can perform a detailed analysis of the user's health status and behavioral patterns. The analysis unit can also evaluate the health status based on the user's health checkup results and self-reported data. By analyzing the collected data and evaluating the user's health status, it is possible to provide a more appropriate exercise plan. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into a generating AI and have the generating AI perform the health status evaluation.

[0074] The generation unit can generate personalized exercise plans tailored to the user's needs. For example, the generation unit collects the user's needs, such as their exercise goals, preferences, and constraints, and generates an exercise plan based on that. The generation unit can also adjust the exercise intensity and frequency according to the user's needs. The generation unit can also customize the exercise plan, taking the user's needs into consideration. This allows for the provision of more effective exercise plans by generating personalized exercise plans tailored to the user's needs. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user needs data into a generation AI and have the generation AI perform the generation of the exercise plan.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of health data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will collect health data during times when the user is relaxed. If the user is relaxed, the data collection unit can also collect data after exercise. If the user is tired, the data collection unit can also collect data during rest. By adjusting the timing of health data collection based on the user's emotions, more appropriate data can be collected. 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 the user's emotion data into the generative AI and have the generative AI adjust the timing of health data collection.

[0076] The data collection unit can analyze the user's past behavior patterns and select the optimal data collection method. For example, the data collection unit can determine the timing of data collection based on the time of day when the user frequently exercised in the past. The data collection unit can also select the optimal data collection method (wearable device, smartphone app, etc.) from the user's past exercise history. The data collection unit can also analyze the user's past behavior patterns and adjust the frequency of data collection. This allows for more effective data collection by analyzing the user's past behavior patterns and selecting the optimal data collection method. 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 behavior pattern data into a generating AI and have the generating AI select the optimal data collection method.

[0077] The data collection unit can filter health data based on the user's current lifestyle and activity level. For example, if the user is at work, the data collection unit may refrain from collecting exercise data. If the user is exercising, the data collection unit may also collect data corresponding to the exercise intensity. If the user is resting, the data collection unit may also collect heart rate and sleep data. This allows for the collection of more relevant data by filtering the data based on the user's current lifestyle and activity 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 the user's lifestyle data into a generating AI and have the generating AI perform the data filtering.

[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 stress-related data. If the user is relaxed, the data collection unit may also prioritize collecting data after exercise. If the user is tired, the data collection unit may also prioritize collecting data during rest. This allows for the collection of more important data by prioritizing data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0079] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting health data. For example, if the user is at a gym, the data collection unit can prioritize the collection of exercise data. If the user is at home, the data collection unit can also prioritize the collection of relaxation data. If the user is out, the data collection unit can also prioritize the collection of step count and distance traveled data. By considering the user's geographical location when collecting data, more relevant 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 data into a generating AI and have the generating AI determine the priority of the data.

[0080] The data collection unit can analyze a user's social media activity and collect relevant data when collecting health data. For example, if a user posts about exercise on social media, the data collection unit can collect that data. The data collection unit can also collect data if a user posts about stress on social media. The data collection unit can also collect data if a user posts about relaxation on social media. By analyzing and collecting data from a user's social media activity, more relevant 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 social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0081] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit can focus on analyzing stress-related data. If the user is relaxed, the analysis unit can also focus on analyzing data after exercise. If the user is tired, the analysis unit can also focus on analyzing data during rest. This allows for more appropriate analysis by adjusting the data analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the data analysis method.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the health data during the analysis. For example, the analysis unit can analyze important health data (heart rate, blood pressure, etc.) in detail. The analysis unit can also analyze general health data (steps, calorie consumption, etc.) in a simplified manner. The analysis unit can also adjust the level of detail of the analysis according to the user's health status. This allows for more detailed analysis of important data by adjusting the level of detail of the analysis based on the importance of the health data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input health data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a heart rate variability analysis algorithm to heart rate data. The analysis unit can also apply a gait pattern analysis algorithm to step count data. The analysis unit can also apply a sleep stage analysis algorithm to sleep data. This allows for more appropriate analysis by applying different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0084] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing stress-related data. If the user is relaxed, the analysis unit may also prioritize analyzing data after exercise. If the user is tired, the analysis unit may also prioritize analyzing data during rest. This allows for prioritizing the analysis of more important data by determining the priority of analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0085] The analysis unit can determine the priority of analysis based on the data collection period during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also analyze the most recent data while referring to past data. The analysis unit can also prioritize the analysis of data collected during a specific period. This allows for the prioritization of analysis based on the data collection period, thereby prioritizing the analysis of the most recent data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection period into a generating AI and have the generating AI determine the analysis priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis according to the relevance of the data. This allows for prioritizing the analysis of more relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0087] The generation unit can estimate the user's emotions and adjust the method of generating the exercise plan based on the estimated user emotions. For example, if the user is feeling stressed, the generation unit will generate an exercise plan with a relaxing effect. If the user is relaxed, the generation unit can also generate a plan with a high exercise intensity. If the user is tired, the generation unit can also generate a light exercise plan. In this way, by adjusting the method of generating the exercise plan based on the user's emotions, a more appropriate exercise plan 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the method of generating the exercise plan.

[0088] The generation unit can generate an optimal exercise plan by analyzing the user's past exercise history during the generation process. For example, the generation unit can generate an optimal exercise plan based on the user's past exercise history. The generation unit can also adjust the exercise intensity and frequency based on the user's past exercise history. The generation unit can also customize the exercise plan by analyzing the user's past exercise history. This allows for the provision of a more effective exercise plan by analyzing the user's past exercise history and generating an optimal exercise plan. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past exercise history data into a generation AI and have the generation AI perform the generation of the exercise plan.

[0089] The generation unit can customize the exercise plan based on the user's current health status during generation. For example, the generation unit evaluates the user's current health status and generates an optimal exercise plan. The generation unit can also adjust the exercise intensity and frequency according to the user's health status. The generation unit can also customize the exercise plan taking the user's health status into consideration. This allows for the provision of a more appropriate exercise plan by customizing the exercise plan based on the user's current health status. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's health status data into a generation AI and have the generation AI perform the customization of the exercise plan.

[0090] The generation unit can estimate the user's emotions and determine the priority of exercise plans based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize exercise plans with a relaxing effect. If the user is relaxed, the generation unit may also prioritize exercise plans with a higher intensity. If the user is tired, the generation unit may also prioritize exercise plans with a lower intensity. This allows for the provision of more appropriate exercise plans by determining the priority of exercise plans based on the user's emotions. 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI determine the priority of exercise plans.

[0091] The generation unit can generate an optimal exercise plan by considering the user's geographical location information during the generation process. For example, if the user is at a gym, the generation unit will generate an exercise plan that can be done at the gym. If the user is at home, the generation unit can also generate an exercise plan that can be done at home. If the user is out, the generation unit can also generate an exercise plan that can be done outdoors. By generating an exercise plan that considers the user's geographical location information, a more appropriate exercise plan can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information data into a generation AI and have the generation AI perform the exercise plan generation.

[0092] The generation unit can analyze the user's social media activity and propose an exercise plan during the generation process. For example, if the user has posted about exercise on social media, the generation unit can propose an exercise plan based on that data. The generation unit can also propose an exercise plan based on the user's posts about stress on social media. The generation unit can also propose an exercise plan based on the user's posts about relaxation on social media. By analyzing the user's social media activity and proposing an exercise plan accordingly, it is possible to provide a more appropriate exercise plan. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI execute the exercise plan proposal.

[0093] The service provider can estimate the user's emotions and adjust how the exercise plan is delivered based on the estimated emotions. For example, if the user is feeling stressed, the service provider can provide a relaxing exercise plan. If the user is relaxed, the service provider can also provide a plan with a higher intensity. If the user is tired, the service provider can also provide a lighter exercise plan. By adjusting how the exercise plan is delivered based on the user's emotions, a more appropriate exercise plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust how the exercise plan is delivered.

[0094] The service provider can analyze the user's past feedback to select the optimal delivery method at the time of delivery. For example, the service provider can select the optimal delivery method based on the exercise plan the user has preferred in the past. The service provider can also adjust the frequency of exercise plan delivery based on the user's past feedback. The service provider can also analyze the user's past feedback and customize the delivery method. This allows for the provision of more effective exercise plans by analyzing the user's past feedback and selecting the optimal delivery method. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past feedback data into a generating AI and have the generating AI select the optimal delivery method.

[0095] The service provider can customize the means of delivering the exercise plan based on the user's current lifestyle at the time of delivery. For example, if the user is at work, the service provider can provide an exercise plan that can be completed in a short amount of time. If the user is at home, the service provider can also provide an exercise plan that can be done at home. If the user is out, the service provider can also provide an exercise plan that can be done outdoors. By customizing the means of delivering the exercise plan based on the user's current lifestyle, a more appropriate exercise plan can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's lifestyle data into a generating AI and have the generating AI perform the customization of the means of delivering the exercise plan.

[0096] The service provider can estimate the user's emotions and determine the priority of exercise plans based on the estimated emotions. For example, if the user is stressed, the service provider may prioritize providing exercise plans with a relaxing effect. If the user is relaxed, the service provider may also prioritize providing plans with a higher intensity. If the user is tired, the service provider may also prioritize providing lighter exercise plans. By prioritizing the provision of exercise plans based on the user's emotions, a more appropriate exercise plan can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of exercise plan provision.

[0097] The service provider can select the optimal delivery method by considering the user's geographical location information at the time of delivery. For example, if the user is at a gym, the service provider can provide an exercise plan that can be done at the gym. If the user is at home, the service provider can also provide an exercise plan that can be done at home. If the user is out, the service provider can also provide an exercise plan that can be done outdoors. By selecting the optimal delivery method by considering the user's geographical location information, a more appropriate exercise plan can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's geographical location information data into a generating AI and have the generating AI select the optimal delivery method.

[0098] The service provider can analyze the user's social media activity at the time of delivery and propose a means of providing an exercise plan. For example, if the user has posted about exercise on social media, the service provider can provide an exercise plan based on that data. The service provider can also provide an exercise plan based on the user's posts about stress on social media. The service provider can also provide an exercise plan based on the user's posts about relaxation on social media. By analyzing the user's social media activity and proposing a means of providing an exercise plan, a more appropriate exercise plan can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI propose a means of providing an exercise plan.

[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 fitness agent system can estimate the user's emotions and adjust the timing of exercise plan delivery based on the estimated emotions. For example, if the user is stressed, the timing of providing a relaxing exercise plan can be adjusted. If the user is relaxed, the timing of providing a high-intensity exercise plan can also be adjusted. If the user is tired, the timing of providing a light exercise plan can also be adjusted. This allows for the provision of more effective exercise plans by adjusting the timing of exercise plan delivery based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input user emotion data into the generative AI and have the generative AI adjust the timing of exercise plan delivery.

[0101] The fitness agent system can adjust the timing of exercise plan delivery by taking into account the user's geographical location. For example, if the user is at a gym, it can adjust the timing of providing an exercise plan that can be done at the gym. If the user is at home, it can also adjust the timing of providing an exercise plan that can be done at home. If the user is out, it can also adjust the timing of providing an exercise plan that can be done outdoors. By adjusting the timing of exercise plan delivery by taking into account the user's geographical location, a more appropriate exercise plan can be provided. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without using AI. For example, the delivery unit can input the user's geographical location data into a generating AI and have the generating AI perform the adjustment of the timing of exercise plan delivery.

[0102] The Fit Agent system can analyze a user's social media activity and adjust the timing of exercise plan delivery. For example, if a user posts about exercise on social media, the timing of exercise plan delivery can be adjusted based on that data. Similarly, if a user posts about stress on social media, the timing of exercise plan delivery can be adjusted based on that data. If a user posts about relaxation on social media, the timing of exercise plan delivery can be adjusted based on that data. By analyzing a user's social media activity and adjusting the timing of exercise plan delivery, a more appropriate exercise plan can be provided. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's social media activity data into a generating AI and have the generating AI perform the adjustment of the timing of exercise plan delivery.

[0103] The fitness agent system can estimate the user's emotions and adjust the content of the exercise plan based on those emotions. For example, if the user is stressed, it can provide a relaxing exercise plan. If the user is relaxed, it can also provide a plan with higher intensity exercise. If the user is tired, it can also provide a lighter exercise plan. By adjusting the content of the exercise plan based on the user's emotions, a more effective exercise plan 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 processing described above in the service provider may be performed using AI, or not using AI. For example, the service provider can input user emotion data into the generative AI and have the generative AI adjust the content of the exercise plan.

[0104] The Fit Agent system can analyze the user's past feedback and adjust the timing of exercise plan delivery. For example, it can select the optimal delivery timing based on the exercise plans the user has preferred in the past. It can also adjust the frequency of exercise plan delivery based on the user's past feedback. It can also customize the delivery timing by analyzing the user's past feedback. This allows for the provision of more effective exercise plans by analyzing the user's past feedback and adjusting the delivery timing. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's past feedback data into a generating AI and have the generating AI perform the adjustment of the exercise plan delivery timing.

[0105] The fitness agent system can estimate the user's emotions and adjust how it delivers exercise plans based on those emotions. For example, if the user is stressed, it can provide a relaxing exercise plan. If the user is relaxed, it can also provide a plan with higher intensity exercise. If the user is tired, it can also provide a lighter exercise plan. By adjusting how the exercise plan is delivered based on the user's emotions, a more effective exercise plan can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the delivery unit may be performed using AI, or not using AI. For example, the delivery unit can input user emotion data into the generative AI and have the generative AI adjust how the exercise plan is delivered.

[0106] The Fit Agent system can adjust the timing of exercise plan delivery based on the user's current lifestyle. For example, if the user is at work, it can adjust the timing of delivery of short-duration exercise plans. If the user is at home, it can also adjust the timing of delivery of exercise plans that can be done at home. If the user is out, it can also adjust the timing of delivery of exercise plans that can be done outdoors. By adjusting the timing of delivery of exercise plans based on the user's current lifestyle, it is possible to provide more appropriate exercise plans. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's lifestyle data into a generating AI and have the generating AI perform the adjustment of the timing of exercise plan delivery.

[0107] The fitness agent system can estimate the user's emotions and determine the priority of exercise plans based on those emotions. For example, if the user is stressed, it can prioritize providing exercise plans with a relaxing effect. If the user is relaxed, it can also prioritize providing plans with a higher intensity. If the user is tired, it can also prioritize providing lighter exercise plans. By prioritizing exercise plans based on the user's emotions, it is possible to provide more effective exercise plans. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the delivery unit may be performed using AI, or not using AI. For example, the delivery unit can input user emotion data into the generative AI and have the generative AI determine the priority of exercise plan delivery.

[0108] The Fit Agent system can select how to provide an exercise plan, taking into account the user's geographical location. For example, if the user is at a gym, it can provide an exercise plan that can be done at the gym. If the user is at home, it can provide an exercise plan that can be done at home. If the user is out, it can provide an exercise plan that can be done outdoors. By selecting how to provide the exercise plan, taking into account the user's geographical location, a more appropriate exercise plan can be provided. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can input the user's geographical location data into a generating AI and have the generating AI select how to provide the exercise plan.

[0109] The Fit Agent system can analyze a user's social media activity and propose methods for providing an exercise plan. For example, if a user posts about exercise on social media, it can provide an exercise plan based on that data. If a user posts about stress on social media, it can also provide an exercise plan based on that data. If a user posts about relaxation on social media, it can also provide an exercise plan based on that data. By analyzing the user's social media activity and proposing methods for providing an exercise plan, it is possible to provide a more appropriate exercise plan. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can input the user's social media activity data into a generating AI and have the generating AI execute a proposal for how to provide an exercise plan.

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

[0111] Step 1: The data collection unit collects user behavior patterns and health data. The data collection unit can collect behavior patterns such as the user's daily activities, exercise habits, and eating patterns. It can also collect health data such as heart rate, blood pressure, weight, and sleep data. The data collection unit collects data using, for example, wearable devices or smartphone apps. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, uses AI to analyze the data and evaluate the user's health status. Based on the collected data, the analysis unit can perform a detailed analysis of the user's health status and behavioral patterns. Step 3: The generation unit generates an optimal exercise plan based on the analysis results obtained by the analysis unit. For example, the generation unit uses AI to generate a personalized exercise plan tailored to the user's needs. The generation unit can suggest appropriate exercise intensity and frequency based on the user's past exercise history and health data. Step 4: The providing unit provides the exercise plan generated by the generating unit. The providing unit can, for example, receive user feedback and adjust the exercise plan. The providing unit can continuously optimize the exercise plan based on user feedback.

[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, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's behavior patterns and health data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates an optimal exercise plan based on the analysis results. The provision unit is implemented in the control unit 46A of the smart device 14 and provides the generated exercise plan to the user and accepts feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[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, analysis unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's behavior patterns and health data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates an optimal exercise plan based on the analysis results. The provision unit is implemented in the control unit 46A of the smart glasses 214 and provides the generated exercise plan to the user and accepts feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[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, analysis unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's behavior patterns and health data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using AI. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and generates an optimal exercise plan based on the analysis results. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides the generated exercise plan to the user and accepts feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[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, analysis unit, generation unit, and provision unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects the user's behavior patterns and health data using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data using AI. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and generates an optimal exercise plan based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides the generated exercise plan to the user and accepts feedback. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes 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 user behavior patterns and health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates an optimal exercise plan based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides the motion plan generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is The system generates an exercise plan based on the user's past exercise history and health data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We accept user feedback and adjust exercise plans accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect user behavior patterns and health data in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is The collected data is analyzed to assess the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generates personalized exercise plans tailored to the user's needs. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze users' past behavioral patterns and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting health data, filtering is performed based on the user's current lifestyle and activity level. The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 11) The aforementioned collection unit is When collecting health data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting health data, we analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate user emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the health data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is We estimate the user's emotions and prioritize the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the method of generating the exercise plan based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the system analyzes the user's past exercise history to generate the optimal exercise plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the exercise plan is customized based on the user's current health status. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and determines the priority of the exercise plan based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the system takes the user's geographical location into consideration to create the optimal exercise plan. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the system analyzes the user's social media activity and suggests an exercise plan. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how the exercise plan is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, we analyze past user feedback to select the optimal delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the method of delivering the exercise plan will be customized based on the user's current lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes the provision of exercise plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose methods for delivering the exercise plan. 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 user behavior patterns and health data, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates an optimal exercise plan based on the analysis results obtained by the analysis unit, The system includes a providing unit that provides the motion plan generated by the generation unit. A system characterized by the following features.

2. The generating unit is The system generates an exercise plan based on the user's past exercise history and health data. The system according to feature 1.

3. The aforementioned supply unit is, We accept user feedback and adjust exercise plans accordingly. The system according to feature 1.

4. The aforementioned collection unit is Collect user behavior patterns and health data in real time. The system according to feature 1.

5. The aforementioned analysis unit is The collected data is analyzed to assess the user's health status. The system according to feature 1.

6. The generating unit is Generates personalized exercise plans tailored to the user's needs. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of health data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze users' past behavioral patterns and select the optimal data collection method. The system according to feature 1.

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

10. 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.

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