system

Abstract

The system according to this embodiment aims to alleviate feelings of loneliness during exercise and improve motivation. [Solution] The system according to this embodiment comprises a tracking unit, an analysis unit, and a provision unit. The tracking unit tracks the user's activity. The analysis unit analyzes the exercise data collected by the tracking unit. The provision unit provides encouragement and advice based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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, there is a problem that a person may feel lonely during exercise and it is difficult to maintain motivation.

[0005] The system according to the embodiment aims to eliminate the sense of loneliness during exercise and improve motivation.

Means for Solving the Problems

[0006] The system according to the embodiment includes a tracking unit, an analysis unit, and a providing unit. The tracking unit tracks the user's activities. The analysis unit analyzes the motion data collected by the tracking unit. The providing unit provides encouragement and advice based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can alleviate feelings of loneliness during exercise and improve motivation. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The wearable terminal system equipped with an AI agent according to an embodiment of the present invention is a system that alleviates feelings of loneliness during exercise and improves motivation. This system operates when the user wears a wearable terminal and synchronizes it with an app. The AI ​​agent tracks the user's activity in real time and collects and analyzes exercise data. The AI ​​agent grasps the user's physical and psychological state in real time and provides optimal encouragement and advice. As a result, the user can have an experience as if they were with friends while exercising, and their motivation is improved. Furthermore, the AI ​​agent creates regular exercise reports based on the user's exercise data and provides reviews of the activity and suggestions for improvement. As a result, the user can continue to exercise consistently. For example, the user wears a wearable terminal and connects it to a smartphone app via Bluetooth®. This information is input to the AI ​​agent. Next, the AI ​​agent tracks the user's activity in real time. The AI ​​agent collects and analyzes the user's exercise data using sensors. For example, if the user is running, the AI ​​agent collects data such as the user's heart rate, steps, and speed in real time. This allows the AI ​​agent to understand the user's physical and psychological state. The AI ​​agent provides users with the most appropriate encouragement and advice based on the data it collects. For example, if a user is tired, the AI ​​agent will send an encouraging message such as, "Let's try a little harder!" It also provides advice to improve the user's exercise performance. For example, it may give specific advice such as, "Let's try to pick up the pace a little." This allows users to experience exercise as if they were with a friend, boosting their motivation. Furthermore, the AI ​​agent creates regular exercise reports based on the user's exercise data. These reports include the user's exercise performance and progress, allowing users to check the results of their exercise. The AI ​​agent also makes suggestions for improvement based on the exercise reports. For example, it may make specific suggestions such as, "Next time, let's try running a slightly longer distance."This allows users to continue exercising consistently. This mechanism helps users overcome feelings of isolation during exercise and improves their motivation. Users can continue exercising enjoyably with the support of an AI agent. Furthermore, the AI ​​agent provides personalized support based on the user's exercise data, allowing users to receive exercise guidance tailored to their needs. This enables users to continue exercising consistently and lead a healthy lifestyle. Thus, the AI ​​agent-equipped wearable device system can overcome feelings of isolation during exercise and improve motivation.

[0029] The wearable terminal system equipped with an AI agent according to this embodiment comprises a tracking unit, an analysis unit, and a data provision unit. The tracking unit tracks the user's activity. The tracking unit collects the user's exercise data in real time, for example, using sensors. The tracking unit can accurately collect the user's exercise data using an accelerometer, a heart rate sensor, etc. For example, when the user is running, the tracking unit collects data such as heart rate, steps, and speed in real time. When collecting the user's exercise data, the tracking unit can adjust the frequency of data collection. For example, if the user is tired, the tracking unit can reduce the tracking frequency to encourage rest. If the user is excited, the tracking unit can increase the tracking frequency to understand the progress of the exercise in detail. If the user is relaxed, the tracking unit can maintain the tracking frequency at a normal level. The analysis unit analyzes the exercise data collected by the tracking unit. The analysis unit analyzes the exercise data using, for example, a machine learning algorithm. The analysis unit can adjust the level of detail of the analysis based on the importance of the exercise data. The analysis unit can, for example, perform detailed analysis on important exercise data and provide feedback to the user. The analysis unit can perform simplified analysis on less important exercise data. The analysis unit can adjust the level of detail of the analysis in stages according to the importance of the exercise data. The analysis unit can determine the priority of analysis based on when the exercise data was collected. For example, the analysis unit can prioritize the analysis of the most recent exercise data and provide real-time feedback. The analysis unit can analyze current exercise data while referring to past exercise data. The analysis unit can adjust the priority of analysis in stages based on when the exercise data was collected. The analysis unit can adjust the order of analysis based on the relevance of the exercise data. For example, the analysis unit can prioritize the analysis of highly relevant exercise data and provide feedback to the user. The analysis unit can perform simplified analysis on less relevant exercise data. The analysis unit can adjust the order of analysis in stages according to the relevance of the exercise data.The service provider provides encouragement and advice based on the analysis results obtained by the analysis unit. For example, if the user is tired, the service provider will send an encouraging message. If the user is excited, the service provider can provide specific advice. If the user is relaxed, the service provider can provide balanced advice. The service provider can adjust the level of detail of encouragement and advice based on the user's exercise performance. For example, the service provider can provide detailed advice to users who demonstrate high exercise performance. The service provider can provide concise advice to users who demonstrate low exercise performance. The service provider can adjust the level of detail of encouragement and advice in stages according to exercise performance. The service provider can apply different encouragement and advice depending on the user's exercise history. For example, the service provider can provide optimal encouragement and advice based on the user's past exercise history. The service provider can provide advice for specific exercise patterns based on the user's exercise history. The service provider can adjust the content of encouragement and advice while referring to the user's exercise history. As a result, the wearable terminal system equipped with the AI ​​agent according to this embodiment can track the user's activity in real time and provide encouragement and advice based on the analysis results, thereby alleviating feelings of loneliness during exercise and improving motivation.

[0030] The tracking unit tracks the user's activity. For example, the tracking unit collects the user's exercise data in real time using sensors. Specifically, it can accurately collect user exercise data using accelerometers and heart rate sensors. The accelerometer detects the direction and speed of the user's movement, and the heart rate sensor measures the user's heart rate. This allows for real-time collection of data such as heart rate, steps, and speed when the user is running. The tracking unit can adjust the frequency of data collection. For example, if the user is tired, the tracking frequency can be reduced to encourage rest. Conversely, if the user is excited, the tracking frequency can be increased to gain a detailed understanding of their exercise progress. If the user is relaxed, the tracking frequency can be maintained at normal levels. Furthermore, the tracking unit centrally manages the user's exercise data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and accessed by the analysis and provision departments. Adjusting the data collection frequency and accuracy allows for flexible responses to specific situations and conditions. This allows the tracking unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the exercise data collected by the tracking unit. The analysis unit analyzes the exercise data using, for example, machine learning algorithms. Specifically, it evaluates the user's exercise patterns and health status based on the collected exercise data. The analysis unit can adjust the level of detail of the analysis based on the importance of the exercise data. For example, it can perform a detailed analysis on important exercise data and provide feedback to the user. It can perform a simplified analysis on less important exercise data. The analysis unit can adjust the level of detail of the analysis in stages according to the importance of the exercise data. Furthermore, the analysis unit can determine the priority of the analysis based on when the exercise data was collected. For example, it can prioritize the analysis of the most recent exercise data and provide real-time feedback. It can also analyze current exercise data while referring to past exercise data. The analysis unit can adjust the priority of the analysis in stages based on when the exercise data was collected. Additionally, the analysis unit can adjust the order of analysis based on the relevance of the exercise data. For example, it can prioritize the analysis of highly relevant exercise data and provide feedback to the user. It can perform a simplified analysis on less relevant exercise data. The analysis unit can adjust the order of analysis in stages according to the relevance of the exercise data. This allows the analysis unit to quickly and accurately analyze the collected data and provide appropriate feedback to the user.

[0032] The service provider provides encouragement and advice based on the analysis results obtained by the analysis unit. Specifically, if the user is tired, it can send encouraging messages. For example, it can send messages such as "Keep going!" or "Take a break and refresh yourself!" If the user is excited, it can provide specific advice. For example, it can provide advice such as "Keep your pace and keep running!" or "You're almost at your next goal!" If the user is relaxed, it can provide balanced advice. For example, it can provide advice such as "Relax and take a deep breath!" or "Keep going like this!" The service provider can adjust the level of detail of the encouragement and advice based on the user's exercise performance. For example, it can provide detailed advice to users who show high exercise performance. Specifically, it can provide advice such as "Try increasing the distance in your next training session!" or "Maintain this pace and achieve your goal!" For users who show low exercise performance, it can provide concise advice. For example, it can provide advice such as "Don't push yourself too hard, keep going at your own pace!" or "You're getting closer to your goal little by little!" The service provider can adjust the level of detail of the encouragement and advice in stages according to the exercise performance. Furthermore, the service provider can apply different encouragement and advice depending on the user's exercise history. For example, it can provide optimal encouragement and advice based on the user's past exercise history. Specifically, it can provide advice such as, "You're on a better pace than your last training session!" or "Let's beat your past record!" The service provider can also provide advice for specific exercise patterns based on the user's exercise history. For example, it can provide advice such as, "If you keep up this pace, you'll be closer to achieving your goal!" or "Let's plan your next training session based on your past data!" The service provider can adjust the content of encouragement and advice while referring to the user's exercise history. This allows the service provider to quickly provide users with appropriate encouragement and advice, alleviate feelings of isolation during exercise, and improve motivation.

[0033] The tracking unit can collect user exercise data in real time using sensors. For example, the tracking unit can collect user exercise data using an accelerometer built into a wearable device worn on the wrist. The tracking unit can monitor the user's heart rate in real time using a heart rate sensor. The tracking unit can acquire the user's location information in real time using a GPS sensor. This allows the tracking unit to accurately collect user exercise data. Some or all of the above-described processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input data acquired from sensors into an AI, which can then analyze the data and collect exercise data.

[0034] The service provider can understand the user's psychological state and provide optimal encouragement and advice. For example, the service provider can capture the user's facial expressions with a camera and understand their psychological state using facial recognition technology. The service provider can record the user's voice and understand their psychological state using voice analysis technology. The service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors and understand their psychological state. As a result, the service provider can provide encouragement and advice tailored to the user's psychological state. 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 facial expression data captured by the camera into an AI, which can then analyze the facial expression data to understand the user's psychological state.

[0035] The analysis unit can create periodic exercise reports based on the user's exercise data. For example, the analysis unit can aggregate the user's exercise data weekly and create an exercise report. The analysis unit can include the user's exercise performance and progress in the exercise report. The analysis unit can include graphs and charts in the exercise report that allow the user to see the results of their exercise. This allows the analysis unit to enable the user to see the results of their exercise. 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 exercise data into AI, and the AI ​​can analyze the data and create an exercise report.

[0036] The service provider can provide improvement suggestions to the user based on the exercise report. For example, the service provider can analyze the user's exercise data and make specific suggestions for the next exercise session. The service provider can provide advice to improve the user's exercise performance. For example, the service provider can make specific suggestions such as, "Next time, try running a slightly longer distance." Based on the user's exercise data, the service provider can suggest adjusting the intensity and frequency of exercise. This allows the service provider to improve the quality of the user's exercise. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the exercise report into AI, which can then analyze the data and generate improvement suggestions.

[0037] The tracking unit can analyze the user's past exercise data and select the optimal tracking method. For example, the tracking unit can select a tracking method based on the type of exercise the user has preferred to perform in the past. The tracking unit can select the most effective tracking method from the user's past exercise data. The tracking unit can analyze the user's past exercise history and optimize the timing of tracking. In this way, the tracking unit can select the optimal tracking method by analyzing the user's past exercise data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input past exercise data into AI, and the AI ​​can analyze the data and select the optimal tracking method.

[0038] The tracking unit can identify specific exercise patterns based on the user's exercise history during tracking and provide real-time notifications. For example, the tracking unit can identify exercise patterns the user has performed in the past and provide notifications when similar patterns appear. The tracking unit can identify specific exercise patterns from the user's exercise history in real time and provide notifications. The tracking unit can send alerts when specific exercise patterns appear based on the user's exercise history. This allows the tracking unit to identify specific exercise patterns based on the user's exercise history and provide real-time notifications. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input exercise history data into AI, which can analyze the data to identify specific exercise patterns and provide real-time notifications.

[0039] The tracking unit can prioritize the collection of highly relevant exercise data while considering the user's geographical location information during tracking. For example, if the user is exercising in a park, the tracking unit can prioritize the collection of data related to a specific route within the park. If the user is exercising in a gym, the tracking unit can prioritize the collection of data related to a specific area within the gym. If the user is exercising at home, the tracking unit can prioritize the collection of data related to a specific location within the home. This allows the tracking unit to prioritize the collection of highly relevant exercise data while considering the user's geographical location information. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input geographical location data into AI, which can then analyze the data and prioritize the collection of highly relevant exercise data.

[0040] The tracking unit can analyze the user's social media activity and collect relevant exercise data during tracking. For example, the tracking unit can collect relevant data based on exercise data shared by the user on social media. The tracking unit can analyze exercise trends from the user's social media activity and collect relevant data. The tracking unit can collect relevant data by referring to data from exercise influencers that the user follows on social media. In this way, the tracking unit can analyze the user's social media activity and collect relevant exercise data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input social media activity data into AI, which can analyze the data and collect relevant exercise data.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the exercise data during the analysis. For example, the analysis unit can perform a detailed analysis on important exercise data and provide feedback to the user. The analysis unit can perform a simplified analysis on less important exercise data. The analysis unit can adjust the level of detail of the analysis in stages according to the importance of the exercise data. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the exercise data. 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 exercise data into AI, and the AI ​​can analyze the data and adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the type of exercise during analysis. For example, in the case of running, the analysis unit can apply an analysis algorithm based on heart rate and speed. In the case of yoga, the analysis unit can apply an analysis algorithm based on flexibility and breathing. In the case of strength training, the analysis unit can apply an analysis algorithm based on weight and repetitions. This allows the analysis unit to obtain more accurate analysis results by applying different analysis algorithms depending on the type of exercise. 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 exercise data into AI, which can analyze the data and apply different analysis algorithms.

[0043] The analysis unit can determine the priority of analysis based on the timing of exercise data collection during the analysis. For example, the analysis unit can prioritize the analysis of the most recent exercise data and provide real-time feedback. The analysis unit can analyze current exercise data while referring to past exercise data. The analysis unit can adjust the priority of analysis in stages based on the timing of exercise data collection. This allows the analysis unit to determine the priority of analysis based on the timing of exercise data collection. 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 exercise data into AI, and the AI ​​can analyze the data and determine the priority of analysis.

[0044] The analysis unit can adjust the order of analysis based on the relevance of the exercise data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant exercise data and provide feedback to the user. The analysis unit can perform a simplified analysis on less relevant exercise data. The analysis unit can adjust the order of analysis step by step according to the relevance of the exercise data. This allows the analysis unit to adjust the order of analysis based on the relevance of the exercise data. 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 exercise data into AI, and the AI ​​can analyze the data and adjust the order of analysis.

[0045] The service provider can adjust the level of detail in encouragement and advice based on the user's exercise performance at the time of delivery. For example, the service provider can provide detailed advice to users who demonstrate high exercise performance. For users who demonstrate low exercise performance, the service provider can provide concise advice. The service provider can adjust the level of detail in encouragement and advice in stages according to exercise performance. This allows the service provider to adjust the level of detail in encouragement and advice according to the user's exercise performance. 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 exercise performance data into AI, and the AI ​​can analyze the data to adjust the level of detail in encouragement and advice.

[0046] The service provider can apply different encouragement and advice depending on the user's exercise history at the time of delivery. For example, the service provider can provide optimal encouragement and advice based on the user's past exercise history. The service provider can provide advice for specific exercise patterns based on the user's exercise history. The service provider can adjust the content of encouragement and advice while referring to the user's exercise history. In this way, the service provider can provide encouragement and advice that is tailored to the user's exercise history. 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 exercise history data into AI, and the AI ​​can analyze the data and apply different encouragement and advice.

[0047] The service provider can determine the priority of encouragement and advice based on when the user's exercise data was collected at the time of delivery. For example, the service provider can prioritize providing encouragement and advice based on the most recent exercise data. The service provider can also provide advice based on current exercise data while referring to past exercise data. The service provider can adjust the priority of encouragement and advice in stages based on when the exercise data was collected. This allows the service provider to determine the priority of encouragement and advice based on when the user's exercise data was collected. 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 exercise data into AI, and the AI ​​can analyze the data to determine the priority of encouragement and advice.

[0048] The service provider can adjust the order of encouragement and advice based on the relevance of the user's exercise data at the time of delivery. For example, the service provider can prioritize providing encouragement and advice based on highly relevant exercise data. For less relevant exercise data, the service provider can provide concise advice. The service provider can adjust the order of encouragement and advice in stages according to the relevance of the exercise data. This allows the service provider to adjust the order of encouragement and advice based on the relevance of the user's exercise data. 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 exercise data into AI, and the AI ​​can analyze the data to adjust the order of encouragement and advice.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] A wearable device system equipped with an AI agent can notify users of the optimal timing for hydration during exercise based on their exercise data. For example, if a user is running, the AI ​​agent can notify them of the optimal hydration timing based on their heart rate and temperature. Furthermore, if a user is exercising for an extended period, the AI ​​agent can send periodic reminders to hydrate. Additionally, if a user is engaging in high-intensity exercise, the AI ​​agent can prompt them to hydrate during breaks. This allows users to hydrate at the appropriate times and maintain their performance during exercise.

[0051] A wearable device system equipped with an AI agent can suggest post-exercise stretches and cool-downs based on the user's exercise data. For example, after a user finishes running, the AI ​​agent can suggest appropriate stretches based on the user's heart rate and exercise intensity. Similarly, after a user finishes strength training, the AI ​​agent can suggest cool-downs to promote muscle recovery. Furthermore, after a user finishes yoga, the AI ​​agent can suggest relaxing stretches. This allows users to properly care for themselves after exercise and maximize the benefits of their workout.

[0052] A wearable device system equipped with an AI agent can provide guidance on breathing techniques during exercise based on the user's exercise data. For example, if a user is running, the AI ​​agent can guide them on appropriate breathing techniques based on their heart rate and pace. If a user is doing yoga, the AI ​​agent can guide them on relaxing breathing techniques. Furthermore, if a user is performing high-intensity exercise, the AI ​​agent can guide them on breathing techniques to optimize oxygen supply. This allows users to practice proper breathing techniques during exercise, thereby enhancing the effectiveness of their workout.

[0053] A wearable device system equipped with an AI agent can suggest improvements to the user's posture during exercise based on their exercise data. For example, if the user is running, the AI ​​agent can analyze the user's posture data and suggest an appropriate running form. If the user is doing strength training, the AI ​​agent can suggest training with the correct form. Furthermore, if the user is doing yoga, the AI ​​agent can suggest the correct poses. As a result, the user can maintain correct posture during exercise and maximize the effects of their workout.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The tracking unit tracks the user's activity. The tracking unit collects the user's exercise data in real time, for example, using sensors. The tracking unit can accurately collect the user's exercise data using sensors such as accelerometers and heart rate sensors. For example, if the user is running, the tracking unit collects data such as heart rate, steps, and speed in real time. When collecting the user's exercise data, the tracking unit can adjust the frequency of data collection. For example, if the user is tired, the tracking unit can reduce the tracking frequency to encourage rest. If the user is excited, the tracking unit can increase the tracking frequency to get a detailed understanding of the exercise progress. If the user is relaxed, the tracking unit can maintain the tracking frequency at a normal level. Step 2: The analysis unit analyzes the exercise data collected by the tracking unit. The analysis unit analyzes the exercise data using, for example, a machine learning algorithm. The analysis unit can adjust the level of detail of the analysis based on the importance of the exercise data. For example, the analysis unit performs a detailed analysis on important exercise data and provides feedback to the user. The analysis unit can perform a simplified analysis on less important exercise data. The analysis unit can adjust the level of detail of the analysis in stages according to the importance of the exercise data. The analysis unit can determine the priority of the analysis based on when the exercise data was collected. For example, the analysis unit prioritizes the analysis of the most recent exercise data and provides real-time feedback. The analysis unit can analyze current exercise data while referring to past exercise data. The analysis unit can adjust the priority of the analysis in stages based on when the exercise data was collected. The analysis unit can adjust the order of analysis based on the relevance of the exercise data. For example, the analysis unit prioritizes the analysis of highly relevant exercise data and provides feedback to the user. The analysis unit can perform a simplified analysis on less relevant exercise data. The analysis unit can adjust the order of analysis step by step according to the relationships between the motion data. Step 3: The service provider provides encouragement and advice based on the analysis results obtained by the analysis unit. For example, if the user is tired, the service provider will send an encouraging message. If the user is excited, the service provider can provide specific advice. If the user is relaxed, the service provider can provide balanced advice. The service provider can adjust the level of detail of the encouragement and advice based on the user's exercise performance. For example, the service provider will provide detailed advice to users who demonstrate high exercise performance. The service provider can provide concise advice to users who demonstrate low exercise performance. The service provider can adjust the level of detail of the encouragement and advice in stages according to exercise performance. The service provider can apply different encouragement and advice depending on the user's exercise history. For example, the service provider will provide optimal encouragement and advice based on the user's past exercise history. The service provider can provide advice for specific exercise patterns based on the user's exercise history. The service provider can adjust the content of the encouragement and advice while referring to the user's exercise history.

[0056] (Example of form 2) The wearable terminal system equipped with an AI agent according to an embodiment of the present invention is a system that alleviates feelings of loneliness during exercise and improves motivation. This system operates when the user wears a wearable terminal and synchronizes it with an app. The AI ​​agent tracks the user's activity in real time and collects and analyzes exercise data. The AI ​​agent grasps the user's physical and psychological state in real time and provides optimal encouragement and advice. As a result, the user can have an experience as if they were with friends while exercising, and their motivation is improved. Furthermore, the AI ​​agent creates regular exercise reports based on the user's exercise data and provides reviews of the activity and suggestions for improvement. As a result, the user can continue to exercise consistently. For example, the user wears a wearable terminal and connects it to a smartphone app via Bluetooth. This information is input to the AI ​​agent. Next, the AI ​​agent tracks the user's activity in real time. The AI ​​agent collects and analyzes the user's exercise data using sensors. For example, if the user is running, the AI ​​agent collects data such as the user's heart rate, steps, and speed in real time. This allows the AI ​​agent to understand the user's physical and psychological state. The AI ​​agent provides users with the most appropriate encouragement and advice based on the data it collects. For example, if a user is tired, the AI ​​agent will send an encouraging message such as, "Let's try a little harder!" It also provides advice to improve the user's exercise performance. For example, it may give specific advice such as, "Let's try to pick up the pace a little." This allows users to experience exercise as if they were with a friend, boosting their motivation. Furthermore, the AI ​​agent creates regular exercise reports based on the user's exercise data. These reports include the user's exercise performance and progress, allowing users to check the results of their exercise. The AI ​​agent also makes suggestions for improvement based on the exercise reports. For example, it may make specific suggestions such as, "Next time, let's try running a slightly longer distance."This allows users to continue exercising consistently. This mechanism helps users overcome feelings of isolation during exercise and improves their motivation. Users can continue exercising enjoyably with the support of an AI agent. Furthermore, the AI ​​agent provides personalized support based on the user's exercise data, allowing users to receive exercise guidance tailored to their needs. This enables users to continue exercising consistently and lead a healthy lifestyle. Thus, the AI ​​agent-equipped wearable device system can overcome feelings of isolation during exercise and improve motivation.

[0057] The wearable terminal system equipped with an AI agent according to this embodiment comprises a tracking unit, an analysis unit, and a data provision unit. The tracking unit tracks the user's activity. The tracking unit collects the user's exercise data in real time, for example, using sensors. The tracking unit can accurately collect the user's exercise data using an accelerometer, a heart rate sensor, etc. For example, when the user is running, the tracking unit collects data such as heart rate, steps, and speed in real time. When collecting the user's exercise data, the tracking unit can adjust the frequency of data collection. For example, if the user is tired, the tracking unit can reduce the tracking frequency to encourage rest. If the user is excited, the tracking unit can increase the tracking frequency to understand the progress of the exercise in detail. If the user is relaxed, the tracking unit can maintain the tracking frequency at a normal level. The analysis unit analyzes the exercise data collected by the tracking unit. The analysis unit analyzes the exercise data using, for example, a machine learning algorithm. The analysis unit can adjust the level of detail of the analysis based on the importance of the exercise data. The analysis unit can, for example, perform detailed analysis on important exercise data and provide feedback to the user. The analysis unit can perform simplified analysis on less important exercise data. The analysis unit can adjust the level of detail of the analysis in stages according to the importance of the exercise data. The analysis unit can determine the priority of analysis based on when the exercise data was collected. For example, the analysis unit can prioritize the analysis of the most recent exercise data and provide real-time feedback. The analysis unit can analyze current exercise data while referring to past exercise data. The analysis unit can adjust the priority of analysis in stages based on when the exercise data was collected. The analysis unit can adjust the order of analysis based on the relevance of the exercise data. For example, the analysis unit can prioritize the analysis of highly relevant exercise data and provide feedback to the user. The analysis unit can perform simplified analysis on less relevant exercise data. The analysis unit can adjust the order of analysis in stages according to the relevance of the exercise data.The service provider provides encouragement and advice based on the analysis results obtained by the analysis unit. For example, if the user is tired, the service provider will send an encouraging message. If the user is excited, the service provider can provide specific advice. If the user is relaxed, the service provider can provide balanced advice. The service provider can adjust the level of detail of encouragement and advice based on the user's exercise performance. For example, the service provider can provide detailed advice to users who demonstrate high exercise performance. The service provider can provide concise advice to users who demonstrate low exercise performance. The service provider can adjust the level of detail of encouragement and advice in stages according to exercise performance. The service provider can apply different encouragement and advice depending on the user's exercise history. For example, the service provider can provide optimal encouragement and advice based on the user's past exercise history. The service provider can provide advice for specific exercise patterns based on the user's exercise history. The service provider can adjust the content of encouragement and advice while referring to the user's exercise history. As a result, the wearable terminal system equipped with the AI ​​agent according to this embodiment can track the user's activity in real time and provide encouragement and advice based on the analysis results, thereby alleviating feelings of loneliness during exercise and improving motivation.

[0058] The tracking unit tracks the user's activity. For example, the tracking unit collects the user's exercise data in real time using sensors. Specifically, it can accurately collect user exercise data using accelerometers and heart rate sensors. The accelerometer detects the direction and speed of the user's movement, and the heart rate sensor measures the user's heart rate. This allows for real-time collection of data such as heart rate, steps, and speed when the user is running. The tracking unit can adjust the frequency of data collection. For example, if the user is tired, the tracking frequency can be reduced to encourage rest. Conversely, if the user is excited, the tracking frequency can be increased to gain a detailed understanding of their exercise progress. If the user is relaxed, the tracking frequency can be maintained at normal levels. Furthermore, the tracking unit centrally manages the user's exercise data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and accessed by the analysis and provision departments. Adjusting the data collection frequency and accuracy allows for flexible responses to specific situations and conditions. This allows the tracking unit to collect data efficiently and effectively, improving the overall system performance.

[0059] The analysis unit analyzes the exercise data collected by the tracking unit. The analysis unit analyzes the exercise data using, for example, machine learning algorithms. Specifically, it evaluates the user's exercise patterns and health status based on the collected exercise data. The analysis unit can adjust the level of detail of the analysis based on the importance of the exercise data. For example, it can perform a detailed analysis on important exercise data and provide feedback to the user. It can perform a simplified analysis on less important exercise data. The analysis unit can adjust the level of detail of the analysis in stages according to the importance of the exercise data. Furthermore, the analysis unit can determine the priority of the analysis based on when the exercise data was collected. For example, it can prioritize the analysis of the most recent exercise data and provide real-time feedback. It can also analyze current exercise data while referring to past exercise data. The analysis unit can adjust the priority of the analysis in stages based on when the exercise data was collected. Additionally, the analysis unit can adjust the order of analysis based on the relevance of the exercise data. For example, it can prioritize the analysis of highly relevant exercise data and provide feedback to the user. It can perform a simplified analysis on less relevant exercise data. The analysis unit can adjust the order of analysis in stages according to the relevance of the exercise data. This allows the analysis unit to quickly and accurately analyze the collected data and provide appropriate feedback to the user.

[0060] The service provider provides encouragement and advice based on the analysis results obtained by the analysis unit. Specifically, if the user is tired, it can send encouraging messages. For example, it can send messages such as "Keep going!" or "Take a break and refresh yourself!" If the user is excited, it can provide specific advice. For example, it can provide advice such as "Keep your pace and keep running!" or "You're almost at your next goal!" If the user is relaxed, it can provide balanced advice. For example, it can provide advice such as "Relax and take a deep breath!" or "Keep going like this!" The service provider can adjust the level of detail of the encouragement and advice based on the user's exercise performance. For example, it can provide detailed advice to users who show high exercise performance. Specifically, it can provide advice such as "Try increasing the distance in your next training session!" or "Maintain this pace and achieve your goal!" For users who show low exercise performance, it can provide concise advice. For example, it can provide advice such as "Don't push yourself too hard, keep going at your own pace!" or "You're getting closer to your goal little by little!" The service provider can adjust the level of detail of the encouragement and advice in stages according to the exercise performance. Furthermore, the service provider can apply different encouragement and advice depending on the user's exercise history. For example, it can provide optimal encouragement and advice based on the user's past exercise history. Specifically, it can provide advice such as, "You're on a better pace than your last training session!" or "Let's beat your past record!" The service provider can also provide advice for specific exercise patterns based on the user's exercise history. For example, it can provide advice such as, "If you keep up this pace, you'll be closer to achieving your goal!" or "Let's plan your next training session based on your past data!" The service provider can adjust the content of encouragement and advice while referring to the user's exercise history. This allows the service provider to quickly provide users with appropriate encouragement and advice, alleviate feelings of isolation during exercise, and improve motivation.

[0061] The tracking unit can collect user exercise data in real time using sensors. For example, the tracking unit can collect user exercise data using an accelerometer built into a wearable device worn on the wrist. The tracking unit can monitor the user's heart rate in real time using a heart rate sensor. The tracking unit can acquire the user's location information in real time using a GPS sensor. This allows the tracking unit to accurately collect user exercise data. Some or all of the above-described processes in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input data acquired from sensors into an AI, which can then analyze the data and collect exercise data.

[0062] The service provider can understand the user's psychological state and provide optimal encouragement and advice. For example, the service provider can capture the user's facial expressions with a camera and understand their psychological state using facial recognition technology. The service provider can record the user's voice and understand their psychological state using voice analysis technology. The service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors and understand their psychological state. As a result, the service provider can provide encouragement and advice tailored to the user's psychological state. 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 facial expression data captured by the camera into an AI, which can then analyze the facial expression data to understand the user's psychological state.

[0063] The analysis unit can create periodic exercise reports based on the user's exercise data. For example, the analysis unit can aggregate the user's exercise data weekly and create an exercise report. The analysis unit can include the user's exercise performance and progress in the exercise report. The analysis unit can include graphs and charts in the exercise report that allow the user to see the results of their exercise. This allows the analysis unit to enable the user to see the results of their exercise. 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 exercise data into AI, and the AI ​​can analyze the data and create an exercise report.

[0064] The service provider can provide improvement suggestions to the user based on the exercise report. For example, the service provider can analyze the user's exercise data and make specific suggestions for the next exercise session. The service provider can provide advice to improve the user's exercise performance. For example, the service provider can make specific suggestions such as, "Next time, try running a slightly longer distance." Based on the user's exercise data, the service provider can suggest adjusting the intensity and frequency of exercise. This allows the service provider to improve the quality of the user's exercise. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the exercise report into AI, which can then analyze the data and generate improvement suggestions.

[0065] The tracking unit can estimate the user's emotions and adjust the tracking frequency based on the estimated emotions. For example, the tracking unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The tracking unit can record the user's voice and estimate the emotions using voice analysis technology. The tracking unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotions. This allows the tracking unit to adjust the tracking frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 tracking unit may be performed using AI, or not using AI. For example, the tracking unit can input image data of the user captured by the camera into the generative AI, which can estimate the emotions and adjust the tracking frequency.

[0066] The tracking unit can analyze the user's past exercise data and select the optimal tracking method. For example, the tracking unit can select a tracking method based on the type of exercise the user has preferred to perform in the past. The tracking unit can select the most effective tracking method from the user's past exercise data. The tracking unit can analyze the user's past exercise history and optimize the timing of tracking. In this way, the tracking unit can select the optimal tracking method by analyzing the user's past exercise data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input past exercise data into AI, and the AI ​​can analyze the data and select the optimal tracking method.

[0067] The tracking unit can identify specific exercise patterns based on the user's exercise history during tracking and provide real-time notifications. For example, the tracking unit can identify exercise patterns the user has performed in the past and provide notifications when similar patterns appear. The tracking unit can identify specific exercise patterns from the user's exercise history in real time and provide notifications. The tracking unit can send alerts when specific exercise patterns appear based on the user's exercise history. This allows the tracking unit to identify specific exercise patterns based on the user's exercise history and provide real-time notifications. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input exercise history data into AI, which can analyze the data to identify specific exercise patterns and provide real-time notifications.

[0068] The tracking unit can estimate the user's emotions and determine the priority of data to track based on the estimated emotions. For example, if the user is tired, the tracking unit may prioritize tracking data such as heart rate and respiratory rate. If the user is excited, the tracking unit may prioritize tracking data such as exercise speed and distance. If the user is relaxed, the tracking unit may track overall exercise data in a balanced manner. In this way, the tracking unit can determine the priority of data to track according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 tracking unit may be performed using AI, or not using AI. For example, the tracking unit can input user image data captured by a camera into a generative AI, which can estimate emotions and determine the priority of data to track.

[0069] The tracking unit can prioritize the collection of highly relevant exercise data while considering the user's geographical location information during tracking. For example, if the user is exercising in a park, the tracking unit can prioritize the collection of data related to a specific route within the park. If the user is exercising in a gym, the tracking unit can prioritize the collection of data related to a specific area within the gym. If the user is exercising at home, the tracking unit can prioritize the collection of data related to a specific location within the home. This allows the tracking unit to prioritize the collection of highly relevant exercise data while considering the user's geographical location information. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input geographical location data into AI, which can then analyze the data and prioritize the collection of highly relevant exercise data.

[0070] The tracking unit can analyze the user's social media activity and collect relevant exercise data during tracking. For example, the tracking unit can collect relevant data based on exercise data shared by the user on social media. The tracking unit can analyze exercise trends from the user's social media activity and collect relevant data. The tracking unit can collect relevant data by referring to data from exercise influencers that the user follows on social media. In this way, the tracking unit can analyze the user's social media activity and collect relevant exercise data. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input social media activity data into AI, which can analyze the data and collect relevant exercise data.

[0071] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is tired, the analysis unit can adjust the analysis algorithm to emphasize the need for rest. If the user is excited, the analysis unit can adjust the analysis algorithm to suggest increasing the intensity of exercise. If the user is relaxed, the analysis unit can adjust the analysis algorithm to suggest balanced exercise. In this way, the analysis unit can adjust the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input emotion data into an AI, and the AI ​​can analyze the data and adjust the analysis algorithm.

[0072] The analysis unit can adjust the level of detail of the analysis based on the importance of the exercise data during the analysis. For example, the analysis unit can perform a detailed analysis on important exercise data and provide feedback to the user. The analysis unit can perform a simplified analysis on less important exercise data. The analysis unit can adjust the level of detail of the analysis in stages according to the importance of the exercise data. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the exercise data. 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 exercise data into AI, and the AI ​​can analyze the data and adjust the level of detail of the analysis.

[0073] The analysis unit can apply different analysis algorithms depending on the type of exercise during analysis. For example, in the case of running, the analysis unit can apply an analysis algorithm based on heart rate and speed. In the case of yoga, the analysis unit can apply an analysis algorithm based on flexibility and breathing. In the case of strength training, the analysis unit can apply an analysis algorithm based on weight and repetitions. This allows the analysis unit to obtain more accurate analysis results by applying different analysis algorithms depending on the type of exercise. 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 exercise data into AI, which can analyze the data and apply different analysis algorithms.

[0074] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, the analysis unit can adjust the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input emotion data into an AI, and the AI ​​can analyze the data and adjust the display method of the analysis results.

[0075] The analysis unit can determine the priority of analysis based on the timing of exercise data collection during the analysis. For example, the analysis unit can prioritize the analysis of the most recent exercise data and provide real-time feedback. The analysis unit can analyze current exercise data while referring to past exercise data. The analysis unit can adjust the priority of analysis in stages based on the timing of exercise data collection. This allows the analysis unit to determine the priority of analysis based on the timing of exercise data collection. 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 exercise data into AI, and the AI ​​can analyze the data and determine the priority of analysis.

[0076] The analysis unit can adjust the order of analysis based on the relevance of the exercise data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant exercise data and provide feedback to the user. The analysis unit can perform a simplified analysis on less relevant exercise data. The analysis unit can adjust the order of analysis step by step according to the relevance of the exercise data. This allows the analysis unit to adjust the order of analysis based on the relevance of the exercise data. 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 exercise data into AI, and the AI ​​can analyze the data and adjust the order of analysis.

[0077] The service provider can estimate the user's emotions and adjust the content of encouragement and advice based on the estimated emotions. For example, if the user is tired, the service provider can send an encouraging message. If the user is excited, the service provider can provide specific advice. If the user is relaxed, the service provider can provide balanced advice. In this way, the service provider can adjust the content of encouragement and advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input emotion data into AI, and the AI ​​can analyze the data to adjust the content of encouragement and advice.

[0078] The service provider can adjust the level of detail in encouragement and advice based on the user's exercise performance at the time of delivery. For example, the service provider can provide detailed advice to users who demonstrate high exercise performance. For users who demonstrate low exercise performance, the service provider can provide concise advice. The service provider can adjust the level of detail in encouragement and advice in stages according to exercise performance. This allows the service provider to adjust the level of detail in encouragement and advice according to the user's exercise performance. 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 exercise performance data into AI, and the AI ​​can analyze the data to adjust the level of detail in encouragement and advice.

[0079] The service provider can apply different encouragement and advice depending on the user's exercise history at the time of delivery. For example, the service provider can provide optimal encouragement and advice based on the user's past exercise history. The service provider can provide advice for specific exercise patterns based on the user's exercise history. The service provider can adjust the content of encouragement and advice while referring to the user's exercise history. In this way, the service provider can provide encouragement and advice that is tailored to the user's exercise history. 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 exercise history data into AI, and the AI ​​can analyze the data and apply different encouragement and advice.

[0080] The service provider can estimate the user's emotions and adjust the timing of encouragement and advice based on the estimated emotions. For example, if the user is tired, the service provider can adjust the timing of sending encouraging messages. If the user is excited, the service provider can adjust the timing of sending advice. If the user is relaxed, the service provider can provide advice at a balanced timing. In this way, the service provider can adjust the timing of encouragement and advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input emotion data into an AI, and the AI ​​can analyze the data to adjust the timing of encouragement and advice.

[0081] The service provider can determine the priority of encouragement and advice based on when the user's exercise data was collected at the time of delivery. For example, the service provider can prioritize providing encouragement and advice based on the most recent exercise data. The service provider can also provide advice based on current exercise data while referring to past exercise data. The service provider can adjust the priority of encouragement and advice in stages based on when the exercise data was collected. This allows the service provider to determine the priority of encouragement and advice based on when the user's exercise data was collected. 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 exercise data into AI, and the AI ​​can analyze the data to determine the priority of encouragement and advice.

[0082] The service provider can adjust the order of encouragement and advice based on the relevance of the user's exercise data at the time of delivery. For example, the service provider can prioritize providing encouragement and advice based on highly relevant exercise data. For less relevant exercise data, the service provider can provide concise advice. The service provider can adjust the order of encouragement and advice in stages according to the relevance of the exercise data. This allows the service provider to adjust the order of encouragement and advice based on the relevance of the user's exercise data. 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 exercise data into AI, and the AI ​​can analyze the data to adjust the order of encouragement and advice.

[0083] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0084] A wearable device system equipped with an AI agent can automatically adjust music selection during exercise based on the user's exercise data. For example, if the user is running, the AI ​​agent can select fast-paced music based on the user's heart rate and pace. If the user is doing relaxing exercise, the AI ​​agent can select relaxing music. Furthermore, if the user is tired, the AI ​​agent can select motivational music along with encouraging messages. This allows users to enjoy optimal music during exercise and enhance the effectiveness of their workout.

[0085] A wearable device system equipped with an AI agent can notify users of the optimal timing for hydration during exercise based on their exercise data. For example, if a user is running, the AI ​​agent can notify them of the optimal hydration timing based on their heart rate and temperature. Furthermore, if a user is exercising for an extended period, the AI ​​agent can send periodic reminders to hydrate. Additionally, if a user is engaging in high-intensity exercise, the AI ​​agent can prompt them to hydrate during breaks. This allows users to hydrate at the appropriate times and maintain their performance during exercise.

[0086] A wearable device system equipped with an AI agent can suggest post-exercise stretches and cool-downs based on the user's exercise data. For example, after a user finishes running, the AI ​​agent can suggest appropriate stretches based on the user's heart rate and exercise intensity. Similarly, after a user finishes strength training, the AI ​​agent can suggest cool-downs to promote muscle recovery. Furthermore, after a user finishes yoga, the AI ​​agent can suggest relaxing stretches. This allows users to properly care for themselves after exercise and maximize the benefits of their workout.

[0087] A wearable device system equipped with an AI agent can provide guidance on breathing techniques during exercise based on the user's exercise data. For example, if a user is running, the AI ​​agent can guide them on appropriate breathing techniques based on their heart rate and pace. If a user is doing yoga, the AI ​​agent can guide them on relaxing breathing techniques. Furthermore, if a user is performing high-intensity exercise, the AI ​​agent can guide them on breathing techniques to optimize oxygen supply. This allows users to practice proper breathing techniques during exercise, thereby enhancing the effectiveness of their workout.

[0088] A wearable device system equipped with an AI agent can suggest improvements to the user's posture during exercise based on their exercise data. For example, if the user is running, the AI ​​agent can analyze the user's posture data and suggest an appropriate running form. If the user is doing strength training, the AI ​​agent can suggest training with the correct form. Furthermore, if the user is doing yoga, the AI ​​agent can suggest the correct poses. As a result, the user can maintain correct posture during exercise and maximize the effects of their workout.

[0089] A wearable device system equipped with an AI agent can estimate the user's emotions and adjust communication during exercise based on those emotions. For example, if the user is tired, the AI ​​agent can send encouraging messages. If the user is excited, the AI ​​agent can provide specific advice. Furthermore, if the user is relaxed, the AI ​​agent can provide balanced advice. This allows the user to receive optimal communication during exercise and maintain motivation.

[0090] A wearable device system equipped with an AI agent can estimate the user's emotions and adjust exercise goals based on those emotions. For example, if the user is tired, the AI ​​agent can set realistic goals. If the user is excited, the AI ​​agent can set challenging goals. Furthermore, if the user is relaxed, the AI ​​agent can set balanced goals. This allows users to have goals that match their emotions and maintain their motivation for exercise.

[0091] AI-powered wearable devices can estimate a user's emotions and adjust the frequency of feedback during exercise based on those emotions. For example, if a user is tired, the AI ​​agent can reduce the frequency of feedback to encourage rest. If a user is excited, the AI ​​agent can increase the frequency of feedback to gain a more detailed understanding of their exercise progress. Furthermore, if a user is relaxed, the AI ​​agent can maintain a normal feedback frequency. This allows users to receive feedback tailored to their emotions, maximizing the effectiveness of their exercise.

[0092] AI-powered wearable devices can estimate a user's emotions and adjust the content of their interactions during exercise based on those emotions. For example, if a user is tired, the AI ​​agent can send encouraging messages. If a user is excited, the AI ​​agent can provide specific advice. Furthermore, if a user is relaxed, the AI ​​agent can provide balanced advice. This allows users to receive optimal interactions during exercise and maintain their motivation.

[0093] AI-powered wearable devices can estimate a user's emotions and adjust their exercise activity selection based on those emotions. For example, if a user is tired, the AI ​​agent can suggest lighter exercise. If a user is excited, the AI ​​agent can suggest high-intensity exercise. Furthermore, if a user is relaxed, the AI ​​agent can suggest balanced exercise. This allows users to choose activities that match their emotions and maximize the benefits of their workouts.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The tracking unit tracks the user's activity. The tracking unit collects the user's exercise data in real time, for example, using sensors. The tracking unit can accurately collect the user's exercise data using sensors such as accelerometers and heart rate sensors. For example, if the user is running, the tracking unit collects data such as heart rate, steps, and speed in real time. When collecting the user's exercise data, the tracking unit can adjust the frequency of data collection. For example, if the user is tired, the tracking unit can reduce the tracking frequency to encourage rest. If the user is excited, the tracking unit can increase the tracking frequency to get a detailed understanding of the exercise progress. If the user is relaxed, the tracking unit can maintain the tracking frequency at a normal level. Step 2: The analysis unit analyzes the exercise data collected by the tracking unit. The analysis unit analyzes the exercise data using, for example, a machine learning algorithm. The analysis unit can adjust the level of detail of the analysis based on the importance of the exercise data. For example, the analysis unit performs a detailed analysis on important exercise data and provides feedback to the user. The analysis unit can perform a simplified analysis on less important exercise data. The analysis unit can adjust the level of detail of the analysis in stages according to the importance of the exercise data. The analysis unit can determine the priority of the analysis based on when the exercise data was collected. For example, the analysis unit prioritizes the analysis of the most recent exercise data and provides real-time feedback. The analysis unit can analyze current exercise data while referring to past exercise data. The analysis unit can adjust the priority of the analysis in stages based on when the exercise data was collected. The analysis unit can adjust the order of analysis based on the relevance of the exercise data. For example, the analysis unit prioritizes the analysis of highly relevant exercise data and provides feedback to the user. The analysis unit can perform a simplified analysis on less relevant exercise data. The analysis unit can adjust the order of analysis step by step according to the relationships between the motion data. Step 3: The service provider provides encouragement and advice based on the analysis results obtained by the analysis unit. For example, if the user is tired, the service provider will send an encouraging message. If the user is excited, the service provider can provide specific advice. If the user is relaxed, the service provider can provide balanced advice. The service provider can adjust the level of detail of the encouragement and advice based on the user's exercise performance. For example, the service provider will provide detailed advice to users who demonstrate high exercise performance. The service provider can provide concise advice to users who demonstrate low exercise performance. The service provider can adjust the level of detail of the encouragement and advice in stages according to exercise performance. The service provider can apply different encouragement and advice depending on the user's exercise history. For example, the service provider will provide optimal encouragement and advice based on the user's past exercise history. The service provider can provide advice for specific exercise patterns based on the user's exercise history. The service provider can adjust the content of the encouragement and advice while referring to the user's exercise history.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] Each of the multiple elements described above, including the tracking unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the tracking unit collects the user's motion data in real time using the sensors 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 motion data. The provision unit is implemented in the control unit 46A of the smart device 14 and provides encouragement and advice based on the analysis results. 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.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the tracking unit, analysis unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the tracking unit collects the user's motion data in real time using the sensors of the smart glasses 214. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected motion data. The provision unit is implemented in the control unit 46A of the smart glasses 214 and provides encouragement and advice based on the analysis results. 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.

[0116] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0117] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0131] Each of the multiple elements described above, including the tracking unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the tracking unit collects the user's movement data in real time using the sensors of the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and analyzes the collected movement data. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides encouragement and advice based on the analysis results. 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] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0133] 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.

[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 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.

[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 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).

[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] 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.

[0140] 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.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0143] In 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.

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

[0145] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0147] The data processing system 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.

[0148] Each of the multiple elements described above, including the tracking unit, analysis 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 tracking unit collects the user's motion data in real time using the sensors of the robot 414. The analysis unit is implemented in, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the collected motion data. The provision unit is implemented in, for example, the control unit 46A of the robot 414 and provides encouragement and advice based on the analysis results. 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] (Note 1) A tracking unit that tracks user activity, An analysis unit analyzes the motion data collected by the tracking unit, The system includes a provisioning unit that provides encouragement and advice based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned tracking unit is Sensors are used to collect user movement data in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, We understand the user's psychological state and provide optimal encouragement and advice. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Create regular exercise reports based on the user's exercise data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Based on exercise reports, we provide improvement suggestions to users. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned tracking unit is It estimates the user's emotions and adjusts the tracking frequency based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned tracking unit is Analyze the user's past exercise data and select the optimal tracking method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned tracking unit is During tracking, the system identifies specific exercise patterns based on the user's exercise history and provides real-time notifications. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned tracking unit is It estimates the user's emotions and prioritizes the data to track based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned tracking unit is During tracking, the system prioritizes collecting highly relevant exercise data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned tracking unit is During tracking, the system analyzes the user's social media activity and collects relevant exercise data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the motion data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the type of motion. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the exercise data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the motion data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts the content of encouragement and advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the service, the level of detail in encouragement and advice is adjusted based on the user's exercise performance. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the service, different encouragement and advice will be applied depending on the user's exercise history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and adjusts the timing of encouragement and advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing the service, the priority of encouragement and advice is determined based on when the user's exercise data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing the service, the order of encouragement and advice will be adjusted based on the relevance of the user's exercise data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0168] 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 tracking unit that tracks user activity, An analysis unit analyzes the motion data collected by the tracking unit, The system includes a provisioning unit that provides encouragement and advice based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned tracking unit is Sensors are used to collect user movement data in real time. The system according to feature 1.

3. The aforementioned supply unit is, We understand the user's psychological state and provide optimal encouragement and advice. The system according to feature 1.

4. The aforementioned analysis unit, Create regular exercise reports based on the user's exercise data. The system according to feature 1.

5. The aforementioned supply unit is, Based on exercise reports, we provide improvement suggestions to users. The system according to feature 1.

6. The aforementioned tracking unit is It estimates the user's emotions and adjusts the tracking frequency based on the estimated emotions. The system according to feature 1.

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

8. The aforementioned tracking unit is During tracking, the system identifies specific exercise patterns based on the user's exercise history and provides real-time notifications. The system according to feature 1.

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

10. The aforementioned tracking unit is During tracking, the system prioritizes collecting highly relevant exercise data by considering the user's geographical location. The system according to feature 1.

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