system

Abstract

The system according to this embodiment aims to reduce the monotony of exercise and enable users to continue exercising without getting bored. [Solution] The system according to the embodiment comprises a story generation unit, an exercise program generation unit, and an exercise data analysis unit. The story generation unit generates a story. The exercise program generation unit generates an exercise program. The exercise data analysis unit analyzes the user's exercise data.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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 it is difficult to reduce the monotony of exercise and enable the user to continue exercising without getting bored.

[0005] The system according to the embodiment aims to reduce the monotony of exercise and enable the user to continue exercising without getting bored.

Means for Solving the Problems

[0006] The system according to the embodiment includes a story generation unit, an exercise program generation unit, and an exercise data analysis unit. The story generation unit generates a story. The exercise program generation unit generates an exercise program. The exercise data analysis unit analyzes the user's exercise data.

Effects of the Invention

[0007] The system according to this embodiment reduces the monotony of exercise, allowing users to continue exercising without getting bored. [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 exercise experience application according to an embodiment of the present invention is a system that uses AI to enrich the progress of exercise with a story, thereby reducing the monotony of the exercise experience. In this system, the story progresses as the user exercises, and new story episodes are provided regularly. This allows the user to continue exercising without getting bored. It is particularly targeted at working adults who get bored with monotonous exercise and find it difficult to exercise regularly. By progressing a new story episode each time the user exercises, the system maintains motivation for exercise. Specifically, the generative AI is utilized in two main ways. First, it generates a story tailored to the user, providing a story customized to the progress of the exercise and the user's preferences. Second, it supports the user in continuing to exercise by automatically generating a continuous and effective exercise program. For example, when a user starts running, the generative AI generates a story tailored to that run. The story progresses with new episodes each time the user runs a certain distance, keeping the user interested. The generative AI also analyzes the user's exercise data and automatically generates the next exercise program. This allows the user to always enjoy a new exercise experience. Furthermore, future plans include expanding the user experience with features such as optimizing exercise programs based on weather forecasts and providing the latest news as users complete their exercises. This will allow users to maintain their health through exercise while also gaining up-to-date information. This application is poised to enter a market now, given the increased demand for home exercise during the COVID-19 pandemic and the advancement of AI that enables personalized exercises. Ultimately, the goal is to create a healthy society where exercise is an integral part of life, with "maintaining health while having fun" as the top priority. This will allow the exercise experience application to enrich the user's exercise experience with a story, reducing the monotony of exercise.

[0029] The exercise experience application according to this embodiment comprises a story generation unit, an exercise program generation unit, and an exercise data analysis unit. The story generation unit generates stories using a generation AI. The story generation unit customizes the story based on, for example, the user's preferences and the progress of their exercise. The story generation unit can provide genres such as fantasy, adventure, and mystery. The story generation unit can also adjust the progression of the story based on the user's exercise data. For example, a new episode progresses each time the user runs a certain distance. The exercise program generation unit generates exercise programs using a generation AI. The exercise program generation unit analyzes the user's exercise data and automatically generates the next exercise program. The exercise program generation unit can provide programs such as running, yoga, and strength training. The exercise program generation unit can also customize the program to match the user's exercise goals. For example, if the user is aiming to lose weight, the unit provides a program that emphasizes calorie consumption. The exercise data analysis unit analyzes the user's exercise data using AI. The exercise data analysis unit collects and analyzes data such as heart rate, steps taken, and calories burned. The exercise data analysis unit, for example, evaluates the user's exercise performance and provides feedback. The exercise data analysis unit can also provide data to the exercise program generation unit based on the user's exercise data. For example, the exercise data analysis unit analyzes the user's exercise data in real time and provides feedback to the exercise program generation unit. This allows the exercise experience application according to this embodiment to enrich the user's exercise experience with a story and reduce the monotony of exercise.

[0030] The story generation unit generates stories using a generative AI. Specifically, the generative AI utilizes natural language processing technology to automatically create stories based on the user's preferences and exercise progress. For example, if a user likes fantasy, the generative AI will generate a story with magic and adventure themes to increase the user's motivation to continue exercising. The generative AI can also analyze the user's exercise data in real time and dynamically adjust the story's progression. For example, a new episode will progress each time the user runs a certain distance, and the next challenge or quest will be presented. Furthermore, the generative AI can incorporate user feedback and continuously improve the story's content and progression. For example, if a user likes a particular character or scenario, it will generate a story that strengthens those elements. In this way, the story generation unit can provide users with a personalized exercise experience, reduce the monotony of exercise, and increase enjoyment.

[0031] The exercise program generation unit generates exercise programs using a generation AI. Specifically, the generation AI analyzes the user's exercise data and automatically generates the next exercise program. For example, based on data such as the user's heart rate, steps taken, and calories burned, it creates a program tailored to the user's exercise level and goals. The generation AI can provide a variety of exercise programs, such as running, yoga, and strength training, and customizes the program according to the user's preferences and goals. For example, if the user is aiming to lose weight, it will provide a program that emphasizes calorie consumption, and if the user is aiming to improve muscle strength, it will suggest training that targets specific muscle groups. In addition, the generation AI can incorporate user feedback and continuously adjust the content and difficulty of the program. For example, if the user finds a particular exercise difficult, it will either lower the difficulty of that exercise or suggest an alternative exercise. In this way, the exercise program generation unit can provide the user with the optimal exercise program and maximize the effects of exercise.

[0032] The Exercise Data Analysis Unit uses AI to analyze users' exercise data. Specifically, it collects data such as heart rate, steps taken, and calories burned, and analyzes this data in real time using AI. For example, it analyzes the user's heart rate data to evaluate the intensity and effectiveness of exercise. It also evaluates the user's activity level and exercise performance based on step count data and provides feedback. Furthermore, the Exercise Data Analysis Unit can also provide data to the Exercise Program Generation Unit based on the user's exercise data. For example, by analyzing the user's exercise data in real time and providing feedback to the Exercise Program Generation Unit, it optimizes the next exercise program. The Exercise Data Analysis Unit also analyzes the user's exercise history and performance trends to support the achievement of long-term exercise goals. For example, based on past data, it evaluates the user's improvement in exercise performance and progress in weight loss, and provides appropriate advice and motivation. In this way, the Exercise Data Analysis Unit can provide personalized feedback to users and maximize the effectiveness of their exercise.

[0033] The optimization unit can optimize exercise programs using weather forecast services. For example, the optimization unit adjusts exercise programs based on weather forecasts. For instance, it might suggest indoor exercises on rainy days, or outdoor exercises on sunny days. Furthermore, the optimization unit can adjust exercise programs according to temperature and humidity. For example, it might suggest a program emphasizing hydration on hot days. By optimizing exercise programs based on weather forecasts, the user's exercise experience can be improved. Weather forecast services include information such as temperature, probability of precipitation, and wind speed. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can input weather forecast data into AI to generate an optimal exercise program.

[0034] The news provider can deliver the latest news as users complete exercises. For example, the news provider can deliver the latest news each time a user achieves a certain exercise goal. For instance, the news provider can display the latest news after a user completes a 5-kilometer run. The news provider can also customize the news to suit the user's interests. For example, the news provider can select news from genres such as sports, entertainment, and technology. Furthermore, the news provider can adjust the frequency of news delivery. For example, if a user exercises every day, the news provider can deliver daily news. This allows users to obtain the latest news through exercise. Some or all of the above processes in the news provider may be performed using AI or not. For example, the news provider can input news data into AI to select the most relevant news for the user.

[0035] The story generation unit can generate stories tailored to the user, providing customized stories that match the user's exercise progress and preferences. For example, the story generation unit can generate stories based on the user's exercise data. For example, the story generation unit can provide a new episode each time the user runs a certain distance. The story generation unit can also customize the genre and content of the stories to match the user's preferences. For example, the story generation unit can offer genres such as fantasy, adventure, and mystery. Furthermore, the story generation unit can adjust the progression of the story to match the user's exercise goals. For example, if the user is aiming to lose weight, the story generation unit can provide a story that emphasizes calorie consumption. This helps maintain motivation for exercise by providing stories tailored to the user. Some or all of the above processes in the story generation unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the story generation unit can input the user's exercise data into a generation AI and generate a customized story.

[0036] The exercise program generation unit can analyze the user's exercise data and automatically generate the next exercise program. For example, the exercise program generation unit generates the next exercise program based on the user's exercise data. For example, the exercise program generation unit evaluates the user's exercise performance and adjusts the next program accordingly. The exercise program generation unit can also customize the program to match the user's exercise goals. For example, if the user prioritizes strength training, the exercise program generation unit provides a strength training program. Furthermore, the exercise program generation unit can analyze the user's exercise data in real time and adjust the next program accordingly. For example, the exercise program generation unit generates the next program based on the user's heart rate and calories burned. This allows for the automatic generation of the next exercise program based on the user's exercise data, thereby supporting continuous exercise. Some or all of the above-described processes in the exercise program generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the exercise program generation unit can input the user's exercise data into a generation AI and generate the next exercise program.

[0037] The exercise data analysis unit can analyze the user's exercise data and provide it to the exercise program generation unit. For example, the exercise data analysis unit collects and analyzes data such as the user's heart rate, steps taken, and calories burned. The exercise data analysis unit can also evaluate the user's exercise performance and provide feedback. Furthermore, the exercise data analysis unit can provide data to the exercise program generation unit based on the user's exercise data. For example, the exercise data analysis unit can analyze the user's exercise data in real time and provide feedback to the exercise program generation unit. This allows for the generation of more effective exercise programs by analyzing the user's exercise data and providing it to the exercise program generation unit. Some or all of the above-described processes in the exercise data analysis unit may be performed using AI or not. For example, the exercise data analysis unit can input the user's exercise data into AI and provide the analysis results to the exercise program generation unit.

[0038] The story generation unit can maintain story consistency by referring to the user's past story history during story generation. For example, the story generation unit can generate a new story based on characters and settings previously chosen by the user. For example, the story generation unit can continue to use story themes that the user has previously enjoyed. The story generation unit can also generate new episodes that reflect the progress of stories the user has previously achieved. In this way, story consistency can be maintained by referring to the user's past story history. Some or all of the above processes in the story generation unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the story generation unit can input the user's past story history into a generation AI to generate a consistent story.

[0039] The story generation unit can adjust the difficulty of the story according to the user's exercise intensity during story generation. For example, if the user is performing high-intensity exercise, the story generation unit will increase the difficulty of the story to make it more challenging. For example, if the user is performing low-intensity exercise, the story generation unit will decrease the difficulty of the story to make it more relaxing. The story generation unit can also adjust the pace of the story and the frequency of events according to the user's exercise intensity. In this way, the exercise experience can be optimized by adjusting the difficulty of the story according to the user's exercise intensity. Some or all of the above processing in the story generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the story generation unit can input the user's exercise intensity data into the generation AI and adjust the difficulty of the story.

[0040] The story generation unit can provide region-related stories by considering the user's geographical location information during story generation. For example, if the user is in a tourist area, the story generation unit can provide a story themed on the history and culture of that area. If the user is in a place rich in nature, the story generation unit can provide a story themed on nature and environmental protection. Furthermore, if the user is in an urban area, the story generation unit can provide a story themed on urban life and events. In this way, by considering the user's geographical location information, region-related stories can be provided. Some or all of the above processing in the story generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the story generation unit can input the user's geographical location information into a generation AI and generate region-related stories.

[0041] The story generation unit can analyze a user's social media activity and generate relevant stories during story generation. For example, it can generate relevant stories based on photos and posts recently shared by the user. It can also provide stories that reflect accounts the user follows and topics they are interested in. Furthermore, the story generation unit can analyze the user's social media activity patterns and generate optimal stories. This allows it to provide relevant stories by analyzing the user's social media activity. Some or all of the above processing in the story generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the story generation unit can input user social media activity data into a generation AI and generate relevant stories.

[0042] The exercise program generation unit can provide an optimal program by referring to the user's past exercise history when generating an exercise program. For example, the exercise program generation unit generates an optimal program based on data of exercises the user has performed in the past. For example, the exercise program generation unit suggests effective exercises based on the user's past exercise history. The exercise program generation unit can also analyze the user's past exercise patterns and provide a program that is continuously effective. In this way, it can provide an optimal exercise program by referring to the user's past exercise history. Some or all of the above processing in the exercise program generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the exercise program generation unit can input the user's past exercise history data into a generation AI and generate an optimal program.

[0043] The exercise program generation unit can customize the program according to the user's health condition when generating the exercise program. For example, the exercise program generation unit can suggest appropriate exercises based on the user's health checkup results. For example, the exercise program generation unit can adjust the program considering the user's current physical condition and health status. The exercise program generation unit can also provide a customized exercise program tailored to the user's health goals. This allows for the provision of more appropriate exercises by customizing the program according to the user's health condition. Some or all of the above-described processes in the exercise program generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the exercise program generation unit can input the user's health data into a generation AI and generate a customized program.

[0044] The exercise program generation unit can provide exercises appropriate to the region by considering the user's geographical location information when generating exercise programs. For example, if the user is at the beach, the exercise program generation unit can suggest beach running or surfing. If the user is in a mountainous area, the exercise program generation unit can suggest hiking or trail running. Furthermore, if the user is in an urban area, the exercise program generation unit can suggest gym training or exercise in a park. In this way, by considering the user's geographical location information, it is possible to provide exercises appropriate to the region. Some or all of the above processing in the exercise program generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the exercise program generation unit can input the user's geographical location information into a generation AI and generate exercises appropriate to the region.

[0045] The exercise program generation unit can analyze the user's social media activity and suggest relevant exercises when generating an exercise program. For example, the exercise program generation unit can suggest exercises based on fitness challenges shared by the user on social media. For example, the exercise program generation unit can provide a program that reflects the training of fitness influencers followed by the user. The exercise program generation unit can also analyze the user's social media activity patterns and suggest the most suitable exercises. In this way, relevant exercises can be provided by analyzing the user's social media activity. Some or all of the above processing in the exercise program generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the exercise program generation unit can input the user's social media activity data into a generation AI and generate relevant exercises.

[0046] The exercise data analysis unit can improve the accuracy of its analysis by referring to the user's past exercise data during the analysis process. For example, the exercise data analysis unit can compare and analyze the user's current exercise performance based on the user's past exercise data. For example, the exercise data analysis unit can extract trends and patterns from the user's past exercise history and reflect them in the analysis. The exercise data analysis unit can also refer to the user's past exercise data and provide individualized feedback. This allows the accuracy of the analysis to be improved by referring to the user's past exercise data. Some or all of the above-described processes in the exercise data analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the exercise data analysis unit can input the user's past exercise data into a generative AI to improve the accuracy of the analysis.

[0047] The exercise data analysis unit can customize the analysis results according to the user's health condition during exercise data analysis. For example, the exercise data analysis unit can customize the analysis results based on the user's health checkup results. For example, the exercise data analysis unit can adjust the analysis results considering the user's current physical condition and health status. The exercise data analysis unit can also provide customized analysis results in line with the user's health goals. By customizing the analysis results according to the user's health condition, more appropriate feedback can be provided. Some or all of the above processing in the exercise data analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the exercise data analysis unit can input the user's health data into a generation AI and generate customized analysis results.

[0048] The exercise data analysis unit can prioritize the analysis of region-related data by considering the user's geographical location information during exercise data analysis. For example, if the user is in a mountainous area, the exercise data analysis unit will prioritize the analysis of data related to the climate and topography of that area. If the user is in an urban area, the exercise data analysis unit will prioritize the analysis of data related to the urban environment and traffic conditions. Furthermore, if the user is by the sea, the exercise data analysis unit can also prioritize the analysis of data related to sea breeze and humidity. In this way, by considering the user's geographical location information, region-related data can be prioritized for analysis. Some or all of the above processing in the exercise data analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the exercise data analysis unit can input the user's geographical location information into a generative AI and prioritize the analysis of region-related data.

[0049] The exercise data analysis unit can analyze the user's social media activity and analyze related data during exercise data analysis. For example, the exercise data analysis unit performs analysis based on exercise data shared by the user on social media. For example, the exercise data analysis unit performs analysis that reflects the training data of fitness influencers followed by the user. The exercise data analysis unit can also analyze the user's social media activity patterns and provide optimal analysis results. In this way, it can provide relevant data by analyzing the user's social media activity. Some or all of the above processing in the exercise data analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the exercise data analysis unit can input the user's social media activity data into a generative AI and analyze the related data.

[0050] The optimization unit can improve the accuracy of optimization by referring to the user's past exercise data during the optimization process. For example, the optimization unit optimizes the current exercise program based on the user's past exercise data. For example, the optimization unit proposes an effective exercise program based on the user's past exercise history. The optimization unit can also analyze the user's past exercise patterns and provide a program that is continuously effective. In this way, the accuracy of optimization can be improved by referring to the user's past exercise data. Some or all of the above processing in the optimization unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the optimization unit can input the user's past exercise data into a generative AI to improve the accuracy of optimization.

[0051] The optimization unit can perform optimization appropriate to the region by considering the user's geographical location information during the optimization process. For example, if the user is in a mountainous area, the optimization unit will perform optimization appropriate to the climate and topography of that area. For example, if the user is in an urban area, the optimization unit will perform optimization appropriate to the urban environment and traffic conditions. Furthermore, if the user is by the sea, the optimization unit can perform optimization appropriate to the sea breeze and humidity. In this way, by considering the user's geographical location information, optimization appropriate to the region can be performed. Some or all of the above processing in the optimization unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the optimization unit can input the user's geographical location information into a generative AI and perform optimization appropriate to the region.

[0052] The news delivery unit can provide the most relevant news by referring to the user's past news browsing history when delivering news. For example, the news delivery unit can provide relevant news based on the trends of news the user has previously viewed. For example, the news delivery unit can suggest news that the user might be interested in based on their past news browsing history. The news delivery unit can also analyze the user's past news browsing patterns and provide the most relevant news. In this way, the news delivery unit can provide the most relevant news by referring to the user's past news browsing history. Some or all of the above processing in the news delivery unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the news delivery unit can input the user's past news browsing history into a generative AI and provide the most relevant news.

[0053] The news delivery unit can prioritize providing news relevant to a user's region by considering the user's geographical location information when delivering news. For example, if the user is in a specific region, the news delivery unit will prioritize providing news from that region. For example, if the user is traveling, the news delivery unit will prioritize providing news from their travel destination. The news delivery unit can also prioritize providing news from the user's residential area. In this way, by considering the user's geographical location information, it is possible to prioritize providing news relevant to a region. Some or all of the above processing in the news delivery unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the news delivery unit can input the user's geographical location information into a generative AI and prioritize providing news relevant to that region.

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

[0055] An exercise experience application can provide region-specific exercise programs by considering the user's geographical location. For example, if the user is at the beach, it can suggest exercises such as beach running or surfing. If the user is in a mountainous area, it can suggest hiking or trail running. Furthermore, if the user is in an urban area, it can suggest gym training or exercise in a park. In this way, by considering the user's geographical location, it is possible to provide exercise programs that are appropriate for the region. Some or all of the above processing in the exercise experience application may be performed using generative AI, or it may be performed without generative AI. For example, the exercise experience application can input the user's geographical location information into a generative AI and generate an exercise program appropriate for the region.

[0056] An exercise experience application can analyze a user's social media activity and suggest relevant exercise programs. For example, it can suggest exercise programs based on fitness challenges shared by the user on social media. It can also provide programs that reflect the training of fitness influencers the user follows. Furthermore, it can analyze the user's social media activity patterns and suggest the optimal exercise program. In this way, relevant exercise programs can be provided by analyzing the user's social media activity. Some or all of the above processing in the exercise experience application may be performed using generative AI, or not. For example, the exercise experience application can input the user's social media activity data into a generative AI and generate relevant exercise programs.

[0057] An exercise experience application can provide an optimal exercise program by referring to the user's past exercise data. For example, it can generate an optimal program based on data of exercises the user has performed in the past. It can also suggest effective exercises based on the user's past exercise history. Furthermore, it can analyze the user's past exercise patterns and provide a program that is continuously effective. In this way, an optimal exercise program can be provided by referring to the user's past exercise data. Some or all of the above processing in the exercise experience application may be performed using a generative AI, or it may be performed without a generative AI. For example, the exercise experience application can input the user's past exercise data into a generative AI and generate an optimal program.

[0058] The exercise experience application can customize exercise programs according to the user's health condition. For example, it can suggest appropriate exercises based on the user's health check results. It can also adjust the program considering the user's current physical condition and health status. Furthermore, it can provide a customized exercise program tailored to the user's health goals. This allows for the provision of more appropriate exercises by customizing the program according to the user's health condition. Some or all of the above processes in the exercise experience application may be performed using generative AI, or they may not. For example, the exercise experience application can input the user's health data into a generative AI and generate a customized program.

[0059] The exercise experience application can prioritize the analysis of region-related exercise data by considering the user's geographical location. For example, if the user is in a mountainous area, it can prioritize the analysis of data related to the climate and topography of that area. Similarly, if the user is in an urban area, it can prioritize the analysis of data related to the urban environment and traffic conditions. Furthermore, if the user is by the sea, it can prioritize the analysis of data related to sea breeze and humidity. In this way, by considering the user's geographical location, region-related data can be prioritized for analysis. Some or all of the above processing in the exercise experience application may be performed using generative AI, or it may be performed without using generative AI. For example, the exercise experience application can input the user's geographical location information into a generative AI and prioritize the analysis of region-related data.

[0060] An exercise experience application can improve the accuracy of its analysis by referring to the user's past exercise data. For example, it can compare and analyze the user's current exercise performance based on the user's past exercise data. It can also extract trends and patterns from the user's past exercise history and reflect them in the analysis. Furthermore, it can provide personalized feedback by referring to the user's past exercise data. In this way, the accuracy of the analysis can be improved by referring to the user's past exercise data. Some or all of the above processes in the exercise experience application may be performed using generative AI, or they may not be performed using generative AI. For example, the exercise experience application can input the user's past exercise data into a generative AI to improve the accuracy of the analysis.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The story generation unit generates a story using a generation AI. The story generation unit customizes the story based on the user's preferences and progress during exercise. For example, it offers genres such as fantasy, adventure, and mystery, and a new episode progresses each time the user runs a certain distance. Step 2: The exercise program generation unit generates an exercise program using a generation AI. The exercise program generation unit analyzes the user's exercise data and automatically generates the next exercise program. For example, it provides programs such as running, yoga, and strength training, and customizes the program to match the user's exercise goals. If the user is aiming to lose weight, it provides a program that emphasizes calorie consumption. Step 3: The exercise data analysis unit uses AI to analyze the user's exercise data. The exercise data analysis unit collects and analyzes data such as heart rate, steps taken, and calories burned. It evaluates the user's exercise performance and provides feedback. The exercise data analysis unit also provides data to the exercise program generation unit based on the user's exercise data. For example, the exercise data analysis unit analyzes the user's exercise data in real time and provides feedback to the exercise program generation unit.

[0063] (Example of form 2) The exercise experience application according to an embodiment of the present invention is a system that uses AI to enrich the progress of exercise with a story, thereby reducing the monotony of the exercise experience. In this system, the story progresses as the user exercises, and new story episodes are provided regularly. This allows the user to continue exercising without getting bored. It is particularly targeted at working adults who get bored with monotonous exercise and find it difficult to exercise regularly. By progressing a new story episode each time the user exercises, the system maintains motivation for exercise. Specifically, the generative AI is utilized in two main ways. First, it generates a story tailored to the user, providing a story customized to the progress of the exercise and the user's preferences. Second, it supports the user in continuing to exercise by automatically generating a continuous and effective exercise program. For example, when a user starts running, the generative AI generates a story tailored to that run. The story progresses with new episodes each time the user runs a certain distance, keeping the user interested. The generative AI also analyzes the user's exercise data and automatically generates the next exercise program. This allows the user to always enjoy a new exercise experience. Furthermore, future plans include expanding the user experience with features such as optimizing exercise programs based on weather forecasts and providing the latest news as users complete their exercises. This will allow users to maintain their health through exercise while also gaining up-to-date information. This application is poised to enter a market now, given the increased demand for home exercise during the COVID-19 pandemic and the advancement of AI that enables personalized exercises. Ultimately, the goal is to create a healthy society where exercise is an integral part of life, with "maintaining health while having fun" as the top priority. This will allow the exercise experience application to enrich the user's exercise experience with a story, reducing the monotony of exercise.

[0064] The exercise experience application according to this embodiment comprises a story generation unit, an exercise program generation unit, and an exercise data analysis unit. The story generation unit generates stories using a generation AI. The story generation unit customizes the story based on, for example, the user's preferences and the progress of their exercise. The story generation unit can provide genres such as fantasy, adventure, and mystery. The story generation unit can also adjust the progression of the story based on the user's exercise data. For example, a new episode progresses each time the user runs a certain distance. The exercise program generation unit generates exercise programs using a generation AI. The exercise program generation unit analyzes the user's exercise data and automatically generates the next exercise program. The exercise program generation unit can provide programs such as running, yoga, and strength training. The exercise program generation unit can also customize the program to match the user's exercise goals. For example, if the user is aiming to lose weight, the unit provides a program that emphasizes calorie consumption. The exercise data analysis unit analyzes the user's exercise data using AI. The exercise data analysis unit collects and analyzes data such as heart rate, steps taken, and calories burned. The exercise data analysis unit, for example, evaluates the user's exercise performance and provides feedback. The exercise data analysis unit can also provide data to the exercise program generation unit based on the user's exercise data. For example, the exercise data analysis unit analyzes the user's exercise data in real time and provides feedback to the exercise program generation unit. This allows the exercise experience application according to this embodiment to enrich the user's exercise experience with a story and reduce the monotony of exercise.

[0065] The story generation unit generates stories using a generative AI. Specifically, the generative AI utilizes natural language processing technology to automatically create stories based on the user's preferences and exercise progress. For example, if a user likes fantasy, the generative AI will generate a story with magic and adventure themes to increase the user's motivation to continue exercising. The generative AI can also analyze the user's exercise data in real time and dynamically adjust the story's progression. For example, a new episode will progress each time the user runs a certain distance, and the next challenge or quest will be presented. Furthermore, the generative AI can incorporate user feedback and continuously improve the story's content and progression. For example, if a user likes a particular character or scenario, it will generate a story that strengthens those elements. In this way, the story generation unit can provide users with a personalized exercise experience, reduce the monotony of exercise, and increase enjoyment.

[0066] The exercise program generation unit generates exercise programs using a generation AI. Specifically, the generation AI analyzes the user's exercise data and automatically generates the next exercise program. For example, based on data such as the user's heart rate, steps taken, and calories burned, it creates a program tailored to the user's exercise level and goals. The generation AI can provide a variety of exercise programs, such as running, yoga, and strength training, and customizes the program according to the user's preferences and goals. For example, if the user is aiming to lose weight, it will provide a program that emphasizes calorie consumption, and if the user is aiming to improve muscle strength, it will suggest training that targets specific muscle groups. In addition, the generation AI can incorporate user feedback and continuously adjust the content and difficulty of the program. For example, if the user finds a particular exercise difficult, it will either lower the difficulty of that exercise or suggest an alternative exercise. In this way, the exercise program generation unit can provide the user with the optimal exercise program and maximize the effects of exercise.

[0067] The Exercise Data Analysis Unit uses AI to analyze users' exercise data. Specifically, it collects data such as heart rate, steps taken, and calories burned, and analyzes this data in real time using AI. For example, it analyzes the user's heart rate data to evaluate the intensity and effectiveness of exercise. It also evaluates the user's activity level and exercise performance based on step count data and provides feedback. Furthermore, the Exercise Data Analysis Unit can also provide data to the Exercise Program Generation Unit based on the user's exercise data. For example, by analyzing the user's exercise data in real time and providing feedback to the Exercise Program Generation Unit, it optimizes the next exercise program. The Exercise Data Analysis Unit also analyzes the user's exercise history and performance trends to support the achievement of long-term exercise goals. For example, based on past data, it evaluates the user's improvement in exercise performance and progress in weight loss, and provides appropriate advice and motivation. In this way, the Exercise Data Analysis Unit can provide personalized feedback to users and maximize the effectiveness of their exercise.

[0068] The optimization unit can optimize exercise programs using weather forecast services. For example, the optimization unit adjusts exercise programs based on weather forecasts. For instance, it might suggest indoor exercises on rainy days, or outdoor exercises on sunny days. Furthermore, the optimization unit can adjust exercise programs according to temperature and humidity. For example, it might suggest a program emphasizing hydration on hot days. By optimizing exercise programs based on weather forecasts, the user's exercise experience can be improved. Weather forecast services include information such as temperature, probability of precipitation, and wind speed. Some or all of the above-described processes in the optimization unit may be performed using AI or not. For example, the optimization unit can input weather forecast data into AI to generate an optimal exercise program.

[0069] The news provider can deliver the latest news as users complete exercises. For example, the news provider can deliver the latest news each time a user achieves a certain exercise goal. For instance, the news provider can display the latest news after a user completes a 5-kilometer run. The news provider can also customize the news to suit the user's interests. For example, the news provider can select news from genres such as sports, entertainment, and technology. Furthermore, the news provider can adjust the frequency of news delivery. For example, if a user exercises every day, the news provider can deliver daily news. This allows users to obtain the latest news through exercise. Some or all of the above processes in the news provider may be performed using AI or not. For example, the news provider can input news data into AI to select the most relevant news for the user.

[0070] The story generation unit can generate stories tailored to the user, providing customized stories that match the user's exercise progress and preferences. For example, the story generation unit can generate stories based on the user's exercise data. For example, the story generation unit can provide a new episode each time the user runs a certain distance. The story generation unit can also customize the genre and content of the stories to match the user's preferences. For example, the story generation unit can offer genres such as fantasy, adventure, and mystery. Furthermore, the story generation unit can adjust the progression of the story to match the user's exercise goals. For example, if the user is aiming to lose weight, the story generation unit can provide a story that emphasizes calorie consumption. This helps maintain motivation for exercise by providing stories tailored to the user. Some or all of the above processes in the story generation unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the story generation unit can input the user's exercise data into a generation AI and generate a customized story.

[0071] The exercise program generation unit can analyze the user's exercise data and automatically generate the next exercise program. For example, the exercise program generation unit generates the next exercise program based on the user's exercise data. For example, the exercise program generation unit evaluates the user's exercise performance and adjusts the next program accordingly. The exercise program generation unit can also customize the program to match the user's exercise goals. For example, if the user prioritizes strength training, the exercise program generation unit provides a strength training program. Furthermore, the exercise program generation unit can analyze the user's exercise data in real time and adjust the next program accordingly. For example, the exercise program generation unit generates the next program based on the user's heart rate and calories burned. This allows for the automatic generation of the next exercise program based on the user's exercise data, thereby supporting continuous exercise. Some or all of the above-described processes in the exercise program generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the exercise program generation unit can input the user's exercise data into a generation AI and generate the next exercise program.

[0072] The exercise data analysis unit can analyze the user's exercise data and provide it to the exercise program generation unit. For example, the exercise data analysis unit collects and analyzes data such as the user's heart rate, steps taken, and calories burned. The exercise data analysis unit can also evaluate the user's exercise performance and provide feedback. Furthermore, the exercise data analysis unit can provide data to the exercise program generation unit based on the user's exercise data. For example, the exercise data analysis unit can analyze the user's exercise data in real time and provide feedback to the exercise program generation unit. This allows for the generation of more effective exercise programs by analyzing the user's exercise data and providing it to the exercise program generation unit. Some or all of the above-described processes in the exercise data analysis unit may be performed using AI or not. For example, the exercise data analysis unit can input the user's exercise data into AI and provide the analysis results to the exercise program generation unit.

[0073] The story generation unit can estimate the user's emotions and adjust the story's pace based on those emotions. For example, if the user is tired, the story generation unit can slow down the story's pace and make it more relaxing. If the user is excited, the story generation unit can speed up the story's pace and increase the number of action scenes. If the user is focused, the story generation unit can maintain a moderate pace and include detailed explanations. By adjusting the story's pace according to the user's emotions, a more appropriate story experience can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the story generation unit may be performed using AI or not. For example, the story generation unit can input user emotion data into the generative AI and adjust the story's pace.

[0074] The story generation unit can maintain story consistency by referring to the user's past story history during story generation. For example, the story generation unit can generate a new story based on characters and settings previously chosen by the user. For example, the story generation unit can continue to use story themes that the user has previously enjoyed. The story generation unit can also generate new episodes that reflect the progress of stories the user has previously achieved. In this way, story consistency can be maintained by referring to the user's past story history. Some or all of the above processes in the story generation unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the story generation unit can input the user's past story history into a generation AI to generate a consistent story.

[0075] The story generation unit can adjust the difficulty of the story according to the user's exercise intensity during story generation. For example, if the user is performing high-intensity exercise, the story generation unit will increase the difficulty of the story to make it more challenging. For example, if the user is performing low-intensity exercise, the story generation unit will decrease the difficulty of the story to make it more relaxing. The story generation unit can also adjust the pace of the story and the frequency of events according to the user's exercise intensity. In this way, the exercise experience can be optimized by adjusting the difficulty of the story according to the user's exercise intensity. Some or all of the above processing in the story generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the story generation unit can input the user's exercise intensity data into the generation AI and adjust the difficulty of the story.

[0076] The story generation unit can estimate the user's emotions and change the story's theme based on those emotions. For example, if the user is sad, the story generation unit can provide a story with themes of encouragement and hope. If the user is happy, the story generation unit can provide a story with themes of humor and adventure. Furthermore, if the user is stressed, the story generation unit can provide a story with themes of relaxation and healing. This allows for a more appropriate story experience by changing the story's theme according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the story generation unit may be performed using AI or not. For example, the story generation unit can input user emotion data into the generative AI to change the story's theme.

[0077] The story generation unit can provide region-related stories by considering the user's geographical location information during story generation. For example, if the user is in a tourist area, the story generation unit can provide a story themed on the history and culture of that area. If the user is in a place rich in nature, the story generation unit can provide a story themed on nature and environmental protection. Furthermore, if the user is in an urban area, the story generation unit can provide a story themed on urban life and events. In this way, by considering the user's geographical location information, region-related stories can be provided. Some or all of the above processing in the story generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the story generation unit can input the user's geographical location information into a generation AI and generate region-related stories.

[0078] The story generation unit can analyze a user's social media activity and generate relevant stories during story generation. For example, it can generate relevant stories based on photos and posts recently shared by the user. It can also provide stories that reflect accounts the user follows and topics they are interested in. Furthermore, the story generation unit can analyze the user's social media activity patterns and generate optimal stories. This allows it to provide relevant stories by analyzing the user's social media activity. Some or all of the above processing in the story generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the story generation unit can input user social media activity data into a generation AI and generate relevant stories.

[0079] The exercise program generation unit can estimate the user's emotions and adjust the exercise intensity based on the estimated emotions. For example, if the user is tired, the exercise program generation unit can suggest a low-intensity exercise. For example, if the user is energetic, the exercise program generation unit can suggest a high-intensity exercise. Furthermore, if the user is stressed, the exercise program generation unit can suggest a relaxing exercise. In this way, by adjusting the exercise intensity according to the user's emotions, a more appropriate exercise can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the exercise program generation unit may be performed using a generative AI or not. For example, the exercise program generation unit can input user emotion data into a generative AI and adjust the exercise intensity.

[0080] The exercise program generation unit can provide an optimal program by referring to the user's past exercise history when generating an exercise program. For example, the exercise program generation unit generates an optimal program based on data of exercises the user has performed in the past. For example, the exercise program generation unit suggests effective exercises based on the user's past exercise history. The exercise program generation unit can also analyze the user's past exercise patterns and provide a program that is continuously effective. In this way, it can provide an optimal exercise program by referring to the user's past exercise history. Some or all of the above processing in the exercise program generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the exercise program generation unit can input the user's past exercise history data into a generation AI and generate an optimal program.

[0081] The exercise program generation unit can customize the program according to the user's health condition when generating the exercise program. For example, the exercise program generation unit can suggest appropriate exercises based on the user's health checkup results. For example, the exercise program generation unit can adjust the program considering the user's current physical condition and health status. The exercise program generation unit can also provide a customized exercise program tailored to the user's health goals. This allows for the provision of more appropriate exercises by customizing the program according to the user's health condition. Some or all of the above-described processes in the exercise program generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the exercise program generation unit can input the user's health data into a generation AI and generate a customized program.

[0082] The exercise program generation unit can estimate the user's emotions and change the type of exercise based on the estimated emotions. For example, if the user is relaxed, the exercise program generation unit may suggest yoga or stretching. If the user is energetic, for example, the exercise program generation unit may suggest running or high-intensity training. Furthermore, if the user is stressed, the exercise program generation unit may suggest meditation or deep breathing exercises. In this way, by changing the type of exercise according to the user's emotions, a more appropriate exercise can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the exercise program generation unit may be performed using a generative AI or not. For example, the exercise program generation unit can input user emotion data into a generative AI and change the type of exercise.

[0083] The exercise program generation unit can provide exercises appropriate to the region by considering the user's geographical location information when generating exercise programs. For example, if the user is at the beach, the exercise program generation unit can suggest beach running or surfing. If the user is in a mountainous area, the exercise program generation unit can suggest hiking or trail running. Furthermore, if the user is in an urban area, the exercise program generation unit can suggest gym training or exercise in a park. In this way, by considering the user's geographical location information, it is possible to provide exercises appropriate to the region. Some or all of the above processing in the exercise program generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the exercise program generation unit can input the user's geographical location information into a generation AI and generate exercises appropriate to the region.

[0084] The exercise program generation unit can analyze the user's social media activity and suggest relevant exercises when generating an exercise program. For example, the exercise program generation unit can suggest exercises based on fitness challenges shared by the user on social media. For example, the exercise program generation unit can provide a program that reflects the training of fitness influencers followed by the user. The exercise program generation unit can also analyze the user's social media activity patterns and suggest the most suitable exercises. In this way, relevant exercises can be provided by analyzing the user's social media activity. Some or all of the above processing in the exercise program generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the exercise program generation unit can input the user's social media activity data into a generation AI and generate relevant exercises.

[0085] The exercise data analysis unit can estimate the user's emotions and adjust the method of analyzing the exercise data based on the estimated emotions. For example, if the user is tired, the exercise data analysis unit can simplify the analysis of the exercise data and display only the important points. If the user is excited, for example, the exercise data analysis unit can perform a detailed data analysis and provide detailed feedback. Furthermore, if the user is relaxed, the exercise data analysis unit can analyze the overall exercise performance and provide balanced feedback. In this way, by adjusting the method of analyzing the exercise data according to the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the exercise data analysis unit may be performed using a generative AI or not. For example, the exercise data analysis unit can input the user's emotion data into a generative AI and adjust the method of analyzing the exercise data.

[0086] The exercise data analysis unit can improve the accuracy of its analysis by referring to the user's past exercise data during the analysis process. For example, the exercise data analysis unit can compare and analyze the user's current exercise performance based on the user's past exercise data. For example, the exercise data analysis unit can extract trends and patterns from the user's past exercise history and reflect them in the analysis. The exercise data analysis unit can also refer to the user's past exercise data and provide individualized feedback. This allows the accuracy of the analysis to be improved by referring to the user's past exercise data. Some or all of the above-described processes in the exercise data analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the exercise data analysis unit can input the user's past exercise data into a generative AI to improve the accuracy of the analysis.

[0087] The exercise data analysis unit can customize the analysis results according to the user's health condition during exercise data analysis. For example, the exercise data analysis unit can customize the analysis results based on the user's health checkup results. For example, the exercise data analysis unit can adjust the analysis results considering the user's current physical condition and health status. The exercise data analysis unit can also provide customized analysis results in line with the user's health goals. By customizing the analysis results according to the user's health condition, more appropriate feedback can be provided. Some or all of the above processing in the exercise data analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the exercise data analysis unit can input the user's health data into a generation AI and generate customized analysis results.

[0088] The exercise data analysis unit can estimate the user's emotions and adjust the display method of the exercise data based on the estimated user emotions. For example, if the user is tense, the exercise data analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the exercise data analysis unit provides a display method that includes detailed information. Furthermore, if the user is in a hurry, the exercise data analysis unit can provide a concise display method. By adjusting the display method of exercise data according to the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the exercise data analysis unit may be performed using a generative AI or not. For example, the exercise data analysis unit can input user emotion data into a generative AI and adjust the display method of the exercise data.

[0089] The exercise data analysis unit can prioritize the analysis of region-related data by considering the user's geographical location information during exercise data analysis. For example, if the user is in a mountainous area, the exercise data analysis unit will prioritize the analysis of data related to the climate and topography of that area. If the user is in an urban area, the exercise data analysis unit will prioritize the analysis of data related to the urban environment and traffic conditions. Furthermore, if the user is by the sea, the exercise data analysis unit can also prioritize the analysis of data related to sea breeze and humidity. In this way, by considering the user's geographical location information, region-related data can be prioritized for analysis. Some or all of the above processing in the exercise data analysis unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the exercise data analysis unit can input the user's geographical location information into a generative AI and prioritize the analysis of region-related data.

[0090] The exercise data analysis unit can analyze the user's social media activity and analyze related data during exercise data analysis. For example, the exercise data analysis unit performs analysis based on exercise data shared by the user on social media. For example, the exercise data analysis unit performs analysis that reflects the training data of fitness influencers followed by the user. The exercise data analysis unit can also analyze the user's social media activity patterns and provide optimal analysis results. In this way, it can provide relevant data by analyzing the user's social media activity. Some or all of the above processing in the exercise data analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the exercise data analysis unit can input the user's social media activity data into a generative AI and analyze the related data.

[0091] The optimization unit can estimate the user's emotions and adjust the exercise program optimization method based on the estimated user emotions. For example, if the user is tired, the optimization unit can suggest a low-intensity exercise program. For example, if the user is energetic, the optimization unit can suggest a high-intensity exercise program. The optimization unit can also suggest a relaxing exercise program if the user is stressed. In this way, by adjusting the exercise program optimization method according to the user's emotions, a more appropriate exercise program can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the optimization unit may be performed using a generative AI or not. For example, the optimization unit can input user emotion data into a generative AI and adjust the exercise program optimization method.

[0092] The optimization unit can improve the accuracy of optimization by referring to the user's past exercise data during the optimization process. For example, the optimization unit optimizes the current exercise program based on the user's past exercise data. For example, the optimization unit proposes an effective exercise program based on the user's past exercise history. The optimization unit can also analyze the user's past exercise patterns and provide a program that is continuously effective. In this way, the accuracy of optimization can be improved by referring to the user's past exercise data. Some or all of the above processing in the optimization unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the optimization unit can input the user's past exercise data into a generative AI to improve the accuracy of optimization.

[0093] The optimization unit can estimate the user's emotions and determine optimization priorities based on the estimated emotions. For example, if the user is tired, the optimization unit will prioritize recovery. If the user is energetic, the optimization unit will prioritize performance improvement. The optimization unit can also prioritize relaxation if the user is stressed. By determining optimization priorities according to the user's emotions, a more appropriate exercise program can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using or without a generative AI. For example, the optimization unit can input user emotion data into a generative AI to determine optimization priorities.

[0094] The optimization unit can perform optimization appropriate to the region by considering the user's geographical location information during the optimization process. For example, if the user is in a mountainous area, the optimization unit will perform optimization appropriate to the climate and topography of that area. For example, if the user is in an urban area, the optimization unit will perform optimization appropriate to the urban environment and traffic conditions. Furthermore, if the user is by the sea, the optimization unit can perform optimization appropriate to the sea breeze and humidity. In this way, by considering the user's geographical location information, optimization appropriate to the region can be performed. Some or all of the above processing in the optimization unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the optimization unit can input the user's geographical location information into a generative AI and perform optimization appropriate to the region.

[0095] The news delivery unit can estimate the user's emotions and adjust how the news is displayed based on those emotions. For example, if the user is stressed, the news delivery unit can provide a simple and highly visible display method. If the user is relaxed, the news delivery unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the news delivery unit can provide a concise display method. This allows for the delivery of more relevant news by adjusting the news display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the news delivery unit may be performed using or without generative AI. For example, the news delivery unit can input user emotion data into a generative AI and adjust how the news is displayed.

[0096] The news delivery unit can provide the most relevant news by referring to the user's past news browsing history when delivering news. For example, the news delivery unit can provide relevant news based on the trends of news the user has previously viewed. For example, the news delivery unit can suggest news that the user might be interested in based on their past news browsing history. The news delivery unit can also analyze the user's past news browsing patterns and provide the most relevant news. In this way, the news delivery unit can provide the most relevant news by referring to the user's past news browsing history. Some or all of the above processing in the news delivery unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the news delivery unit can input the user's past news browsing history into a generative AI and provide the most relevant news.

[0097] The news delivery unit can estimate the user's emotions and prioritize news based on those emotions. For example, if the user is stressed, the news delivery unit will prioritize providing relaxing news. If the user is relaxed, the news delivery unit will prioritize providing detailed news. Furthermore, if the user is in a hurry, the news delivery unit can prioritize providing concise news. This allows for the delivery of more relevant news by prioritizing news according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the news delivery unit may be performed using or without generative AI. For example, the news delivery unit can input user emotion data into a generative AI to determine news priorities.

[0098] The news delivery unit can prioritize providing news relevant to a user's region by considering the user's geographical location information when delivering news. For example, if the user is in a specific region, the news delivery unit will prioritize providing news from that region. For example, if the user is traveling, the news delivery unit will prioritize providing news from their travel destination. The news delivery unit can also prioritize providing news from the user's residential area. In this way, by considering the user's geographical location information, it is possible to prioritize providing news relevant to a region. Some or all of the above processing in the news delivery unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the news delivery unit can input the user's geographical location information into a generative AI and prioritize providing news relevant to that region.

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

[0100] An exercise experience application can estimate the user's emotions and adjust the exercise program based on those emotions. For example, if the user is tired, the intensity of the exercise can be reduced to allow them to continue without strain. If the user is excited, the intensity can be increased to provide a more challenging program. Furthermore, if the user is relaxed, relaxing exercises such as stretching or yoga can be suggested. By providing an exercise program tailored to the user's emotions, a more effective exercise experience can be achieved. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the exercise experience application may be performed using generative AI or not. For example, the exercise experience application can input user emotion data into a generative AI and adjust the exercise program accordingly.

[0101] An exercise experience application can provide region-specific exercise programs by considering the user's geographical location. For example, if the user is at the beach, it can suggest exercises such as beach running or surfing. If the user is in a mountainous area, it can suggest hiking or trail running. Furthermore, if the user is in an urban area, it can suggest gym training or exercise in a park. In this way, by considering the user's geographical location, it is possible to provide exercise programs that are appropriate for the region. Some or all of the above processing in the exercise experience application may be performed using generative AI, or it may be performed without generative AI. For example, the exercise experience application can input the user's geographical location information into a generative AI and generate an exercise program appropriate for the region.

[0102] An exercise experience application can analyze a user's social media activity and suggest relevant exercise programs. For example, it can suggest exercise programs based on fitness challenges shared by the user on social media. It can also provide programs that reflect the training of fitness influencers the user follows. Furthermore, it can analyze the user's social media activity patterns and suggest the optimal exercise program. In this way, relevant exercise programs can be provided by analyzing the user's social media activity. Some or all of the above processing in the exercise experience application may be performed using generative AI, or not. For example, the exercise experience application can input the user's social media activity data into a generative AI and generate relevant exercise programs.

[0103] An exercise experience application can estimate the user's emotions and adjust the way exercise data is displayed based on those emotions. For example, if the user is tense, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. By adjusting the way exercise data is displayed according to the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the exercise experience application may be performed using generative AI or not. For example, the exercise experience application can input the user's emotion data into a generative AI and adjust the way exercise data is displayed.

[0104] An exercise experience application can provide an optimal exercise program by referring to the user's past exercise data. For example, it can generate an optimal program based on data of exercises the user has performed in the past. It can also suggest effective exercises based on the user's past exercise history. Furthermore, it can analyze the user's past exercise patterns and provide a program that is continuously effective. In this way, an optimal exercise program can be provided by referring to the user's past exercise data. Some or all of the above processing in the exercise experience application may be performed using a generative AI, or it may be performed without a generative AI. For example, the exercise experience application can input the user's past exercise data into a generative AI and generate an optimal program.

[0105] The exercise experience application can customize exercise programs according to the user's health condition. For example, it can suggest appropriate exercises based on the user's health check results. It can also adjust the program considering the user's current physical condition and health status. Furthermore, it can provide a customized exercise program tailored to the user's health goals. This allows for the provision of more appropriate exercises by customizing the program according to the user's health condition. Some or all of the above processes in the exercise experience application may be performed using generative AI, or they may not. For example, the exercise experience application can input the user's health data into a generative AI and generate a customized program.

[0106] An exercise experience application can estimate the user's emotions and change the type of exercise based on those emotions. For example, if the user is relaxed, it can suggest yoga or stretching. If the user is energetic, it can suggest running or high-intensity training. Furthermore, if the user is stressed, it can suggest meditation or deep breathing exercises. This allows for the provision of more appropriate exercise by changing the type of exercise according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the exercise experience application may be performed using generative AI or not. For example, the exercise experience application can input user emotion data into a generative AI and change the type of exercise.

[0107] The exercise experience application can prioritize the analysis of region-related exercise data by considering the user's geographical location. For example, if the user is in a mountainous area, it can prioritize the analysis of data related to the climate and topography of that area. Similarly, if the user is in an urban area, it can prioritize the analysis of data related to the urban environment and traffic conditions. Furthermore, if the user is by the sea, it can prioritize the analysis of data related to sea breeze and humidity. In this way, by considering the user's geographical location, region-related data can be prioritized for analysis. Some or all of the above processing in the exercise experience application may be performed using generative AI, or it may be performed without using generative AI. For example, the exercise experience application can input the user's geographical location information into a generative AI and prioritize the analysis of region-related data.

[0108] An exercise experience application can estimate the user's emotions and adjust the method of analyzing exercise data based on those emotions. For example, if the user is tired, the analysis of exercise data can be simplified, displaying only the important points. If the user is excited, a detailed data analysis can be performed, providing more specific feedback. Furthermore, if the user is relaxed, the overall exercise performance can be analyzed, providing balanced feedback. This allows for more appropriate feedback by adjusting the method of analyzing exercise data according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the exercise experience application may be performed using generative AI or not. For example, the exercise experience application can input the user's emotion data into a generative AI and adjust the method of analyzing exercise data.

[0109] An exercise experience application can improve the accuracy of its analysis by referring to the user's past exercise data. For example, it can compare and analyze the user's current exercise performance based on the user's past exercise data. It can also extract trends and patterns from the user's past exercise history and reflect them in the analysis. Furthermore, it can provide personalized feedback by referring to the user's past exercise data. In this way, the accuracy of the analysis can be improved by referring to the user's past exercise data. Some or all of the above processes in the exercise experience application may be performed using generative AI, or they may not be performed using generative AI. For example, the exercise experience application can input the user's past exercise data into a generative AI to improve the accuracy of the analysis.

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

[0111] Step 1: The story generation unit generates a story using a generation AI. The story generation unit customizes the story based on the user's preferences and progress during exercise. For example, it offers genres such as fantasy, adventure, and mystery, and a new episode progresses each time the user runs a certain distance. Step 2: The exercise program generation unit generates an exercise program using a generation AI. The exercise program generation unit analyzes the user's exercise data and automatically generates the next exercise program. For example, it provides programs such as running, yoga, and strength training, and customizes the program to match the user's exercise goals. If the user is aiming to lose weight, it provides a program that emphasizes calorie consumption. Step 3: The exercise data analysis unit uses AI to analyze the user's exercise data. The exercise data analysis unit collects and analyzes data such as heart rate, steps taken, and calories burned. It evaluates the user's exercise performance and provides feedback. The exercise data analysis unit also provides data to the exercise program generation unit based on the user's exercise data. For example, the exercise data analysis unit analyzes the user's exercise data in real time and provides feedback to the exercise program generation unit.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the story generation unit, exercise program generation unit, exercise data analysis unit, optimization unit, and news provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the story generation unit is implemented by the control unit 46A of the smart device 14 and customizes the story based on the user's preferences and exercise progress. The exercise program generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates the next exercise program by analyzing the user's exercise data. The exercise data analysis unit collects the user's exercise data using the camera 42 and microphone 38B of the smart device 14 and analyzes it by the specific processing unit 290 of the data processing unit 12. The optimization unit optimizes the exercise program based on weather forecast data by the specific processing unit 290 of the data processing unit 12. The news provision unit provides the latest news after the user achieves their exercise goal by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the story generation unit, exercise program generation unit, exercise data analysis unit, optimization unit, and news provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the story generation unit is implemented by the control unit 46A of the smart glasses 214 and customizes the story based on the user's preferences and exercise progress. The exercise program generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates the next exercise program by analyzing the user's exercise data. The exercise data analysis unit collects the user's exercise data using the camera 42 and microphone 238 of the smart glasses 214 and analyzes it by the specific processing unit 290 of the data processing unit 12. The optimization unit optimizes the exercise program based on weather forecast data by the specific processing unit 290 of the data processing unit 12. The news provision unit provides the latest news after the user achieves their exercise goal by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the story generation unit, exercise program generation unit, exercise data analysis unit, optimization unit, and news provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the story generation unit is implemented by the control unit 46A of the headset terminal 314 and customizes the story based on the user's preferences and exercise progress. The exercise program generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates the next exercise program by analyzing the user's exercise data. The exercise data analysis unit collects the user's exercise data using the camera 42 and microphone 238 of the headset terminal 314 and analyzes it by the specific processing unit 290 of the data processing unit 12. The optimization unit optimizes the exercise program based on weather forecast data by the specific processing unit 290 of the data processing unit 12. The news provision unit provides the latest news after the user achieves their exercise goal by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the story generation unit, exercise program generation unit, exercise data analysis unit, optimization unit, and news provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the story generation unit is implemented by the control unit 46A of the robot 414 and customizes the story based on the user's preferences and exercise progress. The exercise program generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates the next exercise program by analyzing the user's exercise data. The exercise data analysis unit collects the user's exercise data using the camera 42 and microphone 238 of the robot 414 and analyzes it by the specific processing unit 290 of the data processing unit 12. The optimization unit optimizes the exercise program based on weather forecast data by the specific processing unit 290 of the data processing unit 12. The news provision unit provides the latest news after the user achieves their exercise goal by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0175] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) A story generation unit that generates stories, An exercise program generation unit that generates an exercise program, It includes a motion data analysis unit that analyzes the user's motion data. A system characterized by the following features. (Note 2) It features an optimization unit that optimizes exercise programs using weather forecast services. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a news service that provides the latest news through exercise. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned story generation unit, It generates stories tailored to the user, providing customized stories that match the user's exercise progress and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 5) The exercise program generation unit, It analyzes the user's exercise data and automatically generates the next exercise program. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned motion data analysis unit, The system analyzes the user's exercise data and provides it to the exercise program generation unit. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned story generation unit, It estimates the user's emotions and adjusts the pace of the story based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned story generation unit, When generating a story, the system references the user's past story history to maintain story consistency. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned story generation unit, When generating a story, the difficulty level of the story is adjusted according to the user's exercise intensity. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned story generation unit, It estimates the user's emotions and changes the story's theme based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned story generation unit, When generating stories, the system takes the user's geographical location into consideration and provides stories relevant to that region. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned story generation unit, When generating stories, the system analyzes users' social media activity and generates relevant stories. The system described in Appendix 1, characterized by the features described herein. (Note 13) The exercise program generation unit, It estimates the user's emotions and adjusts the exercise intensity based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The exercise program generation unit, When generating an exercise program, the system refers to the user's past exercise history to provide the most suitable program. The system described in Appendix 1, characterized by the features described herein. (Note 15) The exercise program generation unit, When generating an exercise program, customize the program according to the user's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 16) The exercise program generation unit, The system estimates the user's emotions and changes the type of exercise based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The exercise program generation unit, When generating exercise programs, the system takes the user's geographical location into consideration to provide exercises appropriate for their region. The system described in Appendix 1, characterized by the features described herein. (Note 18) The exercise program generation unit, When generating exercise programs, the system analyzes the user's social media activity and suggests relevant exercises. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned motion data analysis unit, The system estimates the user's emotions and adjusts the analysis method of the motion data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned motion data analysis unit, During exercise data analysis, the system references the user's past exercise data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned motion data analysis unit, When analyzing exercise data, the analysis results are customized according to the user's health status. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned motion data analysis unit, It estimates the user's emotions and adjusts how exercise data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned motion data analysis unit, When analyzing exercise data, the system prioritizes analyzing region-related data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned motion data analysis unit, During exercise data analysis, the system analyzes the user's social media activity and related data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The optimization unit, It estimates the user's emotions and adjusts the optimization method of the exercise program based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The optimization unit, During optimization, the system references the user's past exercise data to improve the accuracy of the optimization. The system described in Appendix 2, characterized by the features described herein. (Note 27) The optimization unit, It estimates user emotions and determines optimization priorities based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The optimization unit, During optimization, the user's geographical location information is taken into consideration to perform optimization appropriate for the region. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned news provision department, It estimates the user's sentiment and adjusts how news is displayed based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned news provision department, When providing news, the system refers to the user's past news browsing history to deliver the most relevant news. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned news provision department, It estimates user sentiment and prioritizes news based on that estimated sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned news provision department, When providing news, we take the user's geographical location into consideration and prioritize providing news relevant to their region. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A story generation unit that generates stories, An exercise program generation unit that generates an exercise program, It includes a motion data analysis unit that analyzes the user's motion data. A system characterized by the following features.

2. It features an optimization unit that optimizes exercise programs using weather forecast services. The system according to feature 1.

3. It features a news service that provides the latest news through exercise. The system according to feature 1.

4. The aforementioned story generation unit, It generates stories tailored to the user, providing customized stories that match the user's exercise progress and preferences. The system according to feature 1.

5. The exercise program generation unit, It analyzes the user's exercise data and automatically generates the next exercise program. The system according to feature 1.

6. The aforementioned motion data analysis unit, The system analyzes the user's exercise data and provides it to the exercise program generation unit. The system according to feature 1.

7. The aforementioned story generation unit, It estimates the user's emotions and adjusts the pace of the story based on those emotions. The system according to feature 1.

8. The aforementioned story generation unit, When generating a story, the system references the user's past story history to maintain story consistency. The system according to feature 1.

9. The aforementioned story generation unit, When generating a story, the difficulty level of the story is adjusted according to the user's exercise intensity. The system according to feature 1.

10. The aforementioned story generation unit, It estimates the user's emotions and changes the story's theme based on those estimated emotions. The system according to feature 1.

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