System

Abstract

An object of a system according to an embodiment is to easily create an optimal training menu for an individual user.SOLUTION: A system includes a collection part, an analysis part, a generation part, a moving image generation part, and a navigation part. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit and extracts a training method necessary for health promotion and an ideal body shape. The generation unit generates a training menu based on the training method extracted by the analysis unit. The moving image generation unit creates a moving image of the training method from the text generated by the generation unit. The navigation unit performs training based on the moving image generated by the moving image generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional techniques, it is difficult to easily create an optimal training menu for each individual user, and there is room for improvement.

[0005] The system according to the embodiment aims to easily create an optimal training menu for each individual user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, a video generation unit, and a navigation unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit and extracts training methods necessary for promoting health and achieving an ideal body shape. The generation unit generates a training menu based on the training methods extracted by the analysis unit. The video generation unit creates videos of the training methods from the text generated by the generation unit. The navigation unit performs training based on the videos created by the video generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can easily create an optimal training menu for each individual user. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A training menu generation system according to an embodiment of the present invention utilizes AI and Big Data to easily create personalized training menus. In this system, a generation AI extracts training methods necessary for health promotion and achieving an ideal physique from Big Data on the Internet and generates a menu that takes into account each individual's preferences. Next, the generation AI creates training method videos from the generated text. Furthermore, a vital sensor is used to acquire each individual's lifestyle data (e.g., sleep time, number of steps, weight), and the training menu is constantly updated based on this data. For example, the training menu generation system analyzes Big Data on the Internet to extract training methods necessary for health promotion and achieving an ideal physique. This also takes into account each individual's preferences, generating an optimal training menu for each individual. Next, the generation AI creates training method videos from the generated text. This makes it easier for users to visually understand the training methods. Furthermore, a vital sensor is used to acquire each individual's lifestyle data. Specifically, vital data such as sleep time, number of steps, and weight is collected, and the training menu is constantly updated based on this data. In this way, the training menu generation system provides optimal training menus for each individual, supporting health promotion and achieving an ideal physique. This allows the training menu generation system to provide the optimal training menu for each individual, supporting them in promoting health and achieving their ideal body shape. For example, it is possible to provide training tailored to individual goals, such as a menu focusing on strength training or a menu emphasizing aerobic exercise. In addition, by updating the menu based on lifestyle data, training tailored to the latest conditions is always provided.

[0029] A training menu generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a video generation unit, and a navigation unit. The collection unit collects data. Examples of the data include, but are not limited to, articles, videos, and research papers about training methods on the Internet. For example, the collection unit collects articles about training methods on the Internet. The collection unit can also collect videos about training methods on the Internet. The collection unit can also collect research papers about training methods on the Internet. For example, the collection unit collects articles about training methods from specific websites. The collection unit can also collect videos about training methods from video sharing sites. The collection unit can also collect research papers about training methods from academic databases. The analysis unit analyzes the data collected by the collection unit and extracts training methods necessary for promoting health and achieving an ideal physique. The analysis unit extracts characteristics of the training methods using, for example, natural language processing technology. The analysis unit can also extract characteristics of the training methods using image recognition technology. The analysis unit can also extract characteristics of the training methods by combining natural language processing technology and image recognition technology. For example, the analysis unit analyzes text data of a training method using morphological analysis. The analysis unit can also analyze video data of a training method using object detection technology. The analysis unit can combine natural language processing technology and image recognition technology to extract features of the training method with high accuracy. The generation unit generates a training menu based on the training method extracted by the analysis unit. The generation unit generates the training menu using, for example, a generation AI. The generation unit can also generate a training menu taking into account each person's preferences. The generation unit can also update the training menu based on vital data. For example, the generation unit generates a training menu that combines strength training and aerobic exercise using a generation AI. The generation unit can also generate a training menu that includes stretching and yoga based on each person's preferences.The generation unit can constantly update the training menu to the latest state based on the vital data. The video generation unit creates videos of training methods from the text generated by the generation unit. The video generation unit creates videos of training methods from text using, for example, a generation AI. The video generation unit can also create videos of strength training using the generation AI. The video generation unit can also create videos of aerobic exercise using the generation AI. The video generation unit can also create videos of stretching using the generation AI. For example, the video generation unit uses a generation AI to create videos of strength training based on text data. The video generation unit can also use a generation AI to create videos of aerobic exercise based on the text data. The video generation unit can also use a generation AI to create videos of stretching based on the text data. The navigation unit performs training based on the videos created by the video generation unit. The navigation unit performs navigation based on, for example, the orientation of the user's smartphone. The navigation unit can also display videos based on the user's walking speed. The navigation unit can also estimate the user's emotions and adjust the navigation method based on the estimated emotions. For example, the navigation unit may provide navigation optimized for portrait orientation according to the orientation of the user's smartphone. The navigation unit may also adjust the video playback speed according to the user's walking speed. The navigation unit may also estimate the user's emotions and provide simple navigation if the user is feeling stressed. As a result, the training menu generation system according to the embodiment can provide an optimal training menu for each individual, supporting the promotion of health and the achievement of an ideal body shape.

[0030] The collection unit collects data on articles, videos, and research papers about training methods from the Internet. The collection unit collects data on articles, videos, and research papers about training methods from the Internet. For example, the collection unit collects articles about training methods from a specific website. The collection unit can also collect videos about training methods from video sharing sites. The collection unit can also collect research papers about training methods from academic databases. This allows for the collection of training methods from various data sources on the Internet, thereby providing richer information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may perform web scraping using AI to collect articles about training methods from a specific website.

[0031] The analysis unit can extract features of the training method using natural language processing or image recognition technology. The analysis unit extracts features of the training method using natural language processing or image recognition technology. For example, the analysis unit analyzes text data of the training method using morphological analysis. The analysis unit can also analyze video data of the training method using object detection technology. The analysis unit can combine natural language processing technology and image recognition technology to extract features of the training method with high accuracy. As a result, by using natural language processing or image recognition technology, features of the training method can be extracted with high accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use AI to analyze text data of the training method using natural language processing technology and extract features.

[0032] The generation unit can generate a training menu based on each person's preferences. The generation unit generates a training menu based on each person's preferences. For example, the generation unit uses a generation AI to generate a training menu that combines strength training and aerobic exercise. The generation unit can also generate a training menu that includes stretching and yoga based on each person's preferences. The generation unit can constantly update the training menu to the latest version based on vital data. This makes it possible to provide a more individually optimized training menu by taking each person's preferences into consideration. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can use a generation AI to generate a training menu based on each person's preferences.

[0033] The video generation unit can create a training method video from text using a generation AI. The video generation unit creates a training method video from text using a generation AI. For example, the video generation unit can use a generation AI to create a strength training video based on text data. The video generation unit can also use a generation AI to create an aerobic exercise video based on text data. The video generation unit can also use a generation AI to create a stretching video based on text data. In this way, high-quality training videos can be automatically generated from text using a generation AI. Some or all of the above-described processing in the video generation unit may be performed using a generation AI, for example, or may be performed without using a generation AI. For example, the video generation unit can use a generation AI to create a training method video based on text data.

[0034] The navigation unit can perform navigation according to the orientation of the user's smartphone. The navigation unit performs navigation according to the orientation of the user's smartphone. For example, the navigation unit provides navigation optimized for portrait orientation according to the orientation of the user's smartphone. The navigation unit can also provide navigation optimized for landscape orientation according to the orientation of the user's smartphone. The navigation unit can also provide navigation optimized for diagonal orientation according to the orientation of the user's smartphone. This improves user convenience by providing navigation according to the orientation of the smartphone. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can use a gyro sensor or an acceleration sensor to detect the orientation of the smartphone.

[0035] The collection unit can collect vital data such as sleep time, number of steps, and weight using a vital sensor. The collection unit collects vital data such as sleep time, number of steps, and weight using a vital sensor. For example, the collection unit collects heart rate using a heart rate sensor. The collection unit can also collect number of steps using a pedometer. The collection unit can also collect weight using a weight scale. In this way, by using the vital sensor, the user's health condition can be understood in detail. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from the vital sensor into AI and have the AI ​​analyze the data.

[0036] The generation unit can update the training menu based on the vital data. The generation unit updates the training menu based on the vital data. For example, if the sleep time is short, the generation unit may add stretches that have a relaxing effect. If the number of steps is low, the generation unit may also increase aerobic exercise. If weight is increasing, the generation unit may also provide a training menu that emphasizes calorie consumption. In this way, by updating the training menu based on the vital data, training that is always tailored to the latest condition is provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit may input vital data into the generation AI and cause the generation AI to update the training menu.

[0037] The collection unit may prioritize reliable sources when collecting data such as articles, videos, and research papers about training methods on the Internet. The collection unit prioritizes reliable sources when collecting data such as articles, videos, and research papers about training methods on the Internet. For example, the collection unit may prioritize collecting academic papers and expert blogs. The collection unit may also prioritize collecting information from public institutions and certified fitness organizations. The collection unit may also prioritize collecting training videos that are highly rated by users. This prioritizes the selection of reliable sources, thereby providing more accurate information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may use AI to evaluate the reliability of information in order to select reliable sources.

[0038] The collection unit can customize the type of data collected using the vital sensor according to the user's health condition. The collection unit customizes the type of data collected using the vital sensor according to the user's health condition. For example, if the user is tired, the collection unit may focus on collecting heart rate and stress level. If the user is in good health, the collection unit may also focus on collecting exercise volume and calorie consumption. If the user is in poor health, the collection unit may also focus on collecting sleep quality and rest time. This enables more appropriate data collection by customizing the type of data collected according to the user's health condition. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data acquired from the vital sensor into AI and have the AI ​​perform analysis to customize the type of data.

[0039] The collection unit can analyze the user's past training history and select the optimal data collection method. The collection unit analyzes the user's past training history and selects the optimal data collection method. For example, the collection unit analyzes the type and frequency of training the user has performed in the past and collects similar data. The collection unit can also customize data collection based on the user's preferred training methods in the past. The collection unit can also select an effective data collection method from the user's past training history. This enables more effective data collection by analyzing the user's past training history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past training history into AI and have the AI ​​perform an analysis to select the optimal data collection method.

[0040] When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. When collecting data, the collection unit prioritizes collecting highly relevant data based on the user's geographical location information. For example, when a user trains outdoors, the collection unit collects information about nearby parks and training facilities. When a user trains at home, the collection unit can also collect data on training methods that can be done at home. When a user is traveling, the collection unit can also collect information about training facilities and running courses at the user's location. This allows for collection of more relevant data by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's geographical location information into AI and cause the AI ​​to perform analysis to prioritize collecting highly relevant data.

[0041] The collection unit may analyze the user's social media activity and collect relevant data when collecting data. The collection unit may analyze the user's social media activity and collect relevant data when collecting data. For example, the collection unit may collect training methods shared by the user on social media. The collection unit may also collect training methods recommended by the user's followers. The collection unit may also collect training data from fitness communities in which the user participates. This allows for more relevant data to be collected by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the content of social media posts into AI and have the AI ​​perform analysis to collect relevant data.

[0042] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit customizes the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit prioritizes collecting training methods that the user has previously rated highly. The collection unit can also collect data by excluding training methods that the user has previously rated poorly. The collection unit can also adjust the type and frequency of data to be collected based on the user's feedback. This enables more appropriate data collection by reflecting the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into AI and have the AI ​​perform analysis to customize the collection method.

[0043] The analysis unit can apply different analysis algorithms when extracting features of a training method using natural language processing or image recognition technology. The analysis unit applies different analysis algorithms when extracting features of a training method using natural language processing or image recognition technology. For example, the analysis unit uses natural language processing to extract features from text data of a training method. The analysis unit can also use image recognition technology to extract movement features from a training video. The analysis unit can also combine different analysis algorithms to perform more accurate feature extraction. This enables more accurate feature extraction by applying different analysis algorithms. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use AI to combine natural language processing technology and image recognition technology to extract features of a training method.

[0044] The analysis unit can evaluate the effectiveness of the training method during analysis and select the optimal method. The analysis unit evaluates the effectiveness of the training method during analysis and selects the optimal method. For example, the analysis unit collects user feedback and reflects it in the analysis to evaluate the effectiveness of the training method. The analysis unit can also analyze vital data to evaluate the effectiveness of the training method and select the optimal method. The analysis unit can also analyze past training data to evaluate the effectiveness of the training method and select the optimal method. In this way, by evaluating the effectiveness of the training method, a more effective training method can be selected. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input user feedback data into AI and have the AI ​​perform an analysis to evaluate the effectiveness of the training method.

[0045] The analysis unit can improve the accuracy of the analysis by referring to the user's past training data during analysis. The analysis unit improves the accuracy of the analysis by referring to the user's past training data during analysis. For example, the analysis unit analyzes the user's past training data and evaluates the effectiveness of the training method. The analysis unit can also adjust the analysis algorithm by referring to the user's past training data. The analysis unit can also improve the accuracy of the analysis results based on the user's past training data. In this way, the accuracy of the analysis is improved by referring to the user's past training data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past training data into AI and have the AI ​​perform analysis to improve the accuracy of the analysis.

[0046] The analysis unit can determine the priority of analysis based on the time of submission of the training methods during analysis. The analysis unit determines the priority of analysis based on the time of submission of the training methods during analysis. For example, the analysis unit prioritizes analysis of the most recent training methods. The analysis unit can also prioritize analysis of training methods specified by the user. The analysis unit can also adjust the order of analysis based on the time of submission of the training methods. In this way, by determining the priority of analysis based on the time of submission of the training methods, the most recent information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of submission of the training methods into AI and cause the AI ​​to perform analysis to determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the training methods during analysis. The analysis unit adjusts the order of analysis based on the relevance of the training methods during analysis. For example, the analysis unit prioritizes analysis of training methods related to the user's goal. The analysis unit can also adjust the order of analysis based on the effectiveness of the training methods. The analysis unit can also adjust the order of analysis based on the popularity of the training methods. In this way, adjusting the order of analysis based on the relevance of the training methods enables more effective analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the training methods into AI and cause the AI ​​to perform analysis to adjust the order of analysis.

[0048] The analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise during analysis. The analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise during analysis. For example, if the user is a beginner, the analysis unit can provide analysis results using simple terminology. If the user is an intermediate user, the analysis unit can also provide analysis results using appropriate technical terminology. If the user is an advanced user, the analysis unit can also provide analysis results using detailed technical terminology. In this way, by adjusting the use of technical terminology in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into AI and have the AI ​​perform an analysis to adjust the use of technical terminology in the analysis.

[0049] The generation unit can apply different generation algorithms when generating a training menu taking into account each person's preferences. The generation unit can apply different generation algorithms when generating a training menu taking into account each person's preferences. For example, the generation unit generates a menu that combines strength training and aerobic exercise based on the user's preferences. The generation unit can also generate a menu that includes stretching and yoga based on the user's preferences. The generation unit can also apply different generation algorithms to generate an optimal menu. By applying different generation algorithms, it is possible to provide a more individually optimized training menu. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a training menu based on each person's preferences using different generation algorithms.

[0050] The generation unit can evaluate the effectiveness of the training menu and select an optimal menu when generating the menu. The generation unit evaluates the effectiveness of the training menu and selects an optimal menu when generating the menu. For example, the generation unit collects user feedback to evaluate the effectiveness of the training menu and reflects it in the generation. The generation unit can also analyze vital data to evaluate the effectiveness of the training menu and select an optimal menu. The generation unit can also analyze past training data to evaluate the effectiveness of the training menu and select an optimal menu. This allows the effectiveness of the training menu to be evaluated, thereby providing a more effective menu. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input user feedback data into the generation AI and cause the generation AI to perform an analysis to evaluate the effectiveness of the training menu.

[0051] The generation unit can improve the accuracy of generation by referring to the user's past training menus during generation. The generation unit can improve the accuracy of generation by referring to the user's past training menus during generation. For example, the generation unit analyzes the user's past training menus to generate an effective menu. The generation unit can also adjust the generation algorithm by referring to the user's past training menus. The generation unit can also improve the accuracy of the generated results based on the user's past training menus. In this way, by referring to the user's past training menus, the accuracy of generation is improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past training menus into the generation AI and cause the generation AI to perform analysis to improve the accuracy of the generation.

[0052] The generation unit can determine the generation priority based on the submission time of the training menu at the time of generation. The generation unit determines the generation priority based on the submission time of the training menu at the time of generation. For example, the generation unit generates the most recent training menu with priority. The generation unit can also generate the training menu specified by the user with priority. The generation unit can also adjust the order of generation based on the submission time of the training menu. In this way, by determining the generation priority based on the submission time of the training menu, the most recent information can be generated with priority. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the submission time of the training menu into the generation AI and cause the generation AI to perform analysis to determine the generation priority.

[0053] The generation unit can adjust the order of generation based on the relevance of the training menus during generation. The generation unit adjusts the order of generation based on the relevance of the training menus during generation. For example, the generation unit prioritizes generating training menus related to the user's goals. The generation unit can also adjust the order of generation based on the effectiveness of the training menus. The generation unit can also adjust the order of generation based on the popularity of the training menus. In this way, by adjusting the order of generation based on the relevance of the training menus, a more effective menu can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the relevance of the training menus into the generation AI and cause the generation AI to perform analysis to adjust the order of generation.

[0054] The generation unit can adjust the use of technical terminology in the generated menu according to the user's level of expertise during generation. The generation unit adjusts the use of technical terminology in the generated menu according to the user's level of expertise during generation. For example, if the user is a beginner, the generation unit generates a menu using simple terminology. If the user is an intermediate user, the generation unit can also generate a menu using moderate terminology. If the user is an advanced user, the generation unit can also generate a menu using detailed terminology. This makes it possible to provide a menu that is easier to understand by adjusting the use of technical terminology in the generated menu according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise into the generation AI and cause the generation AI to perform an analysis to adjust the use of technical terminology in the generated menu.

[0055] The video generation unit can apply different generation algorithms when using a generation AI to create a training method video from text. The video generation unit can apply different generation algorithms when using a generation AI to create a training method video from text. For example, the video generation unit generates a strength training video based on text data using a generation AI. The video generation unit can also generate aerobic exercise videos based on text data using a generation AI. The video generation unit can also generate stretching videos based on text data using a generation AI. In this way, by applying different generation algorithms, a wider variety of training videos can be provided. Some or all of the above-described processing in the video generation unit may be performed using a generation AI, for example, or may be performed without using a generation AI. For example, the video generation unit can use different generation algorithms to create a training method video based on text data.

[0056] The video generation unit can evaluate the effectiveness of the training method and select the optimal video when generating the video. The video generation unit evaluates the effectiveness of the training method and selects the optimal video when generating the video. For example, the video generation unit collects user feedback to evaluate the effectiveness of the training method and reflects it in the generation. The video generation unit can also analyze vital data to evaluate the effectiveness of the training method and select the optimal video. The video generation unit can also analyze past training data to evaluate the effectiveness of the training method and select the optimal video. In this way, by evaluating the effectiveness of the training method, more effective videos can be provided. Some or all of the above-mentioned processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input user feedback data into the generation AI and cause the generation AI to perform an analysis to evaluate the effectiveness of the training method.

[0057] The video generation unit can improve the accuracy of generation by referring to the user's past training videos when generating a video. The video generation unit improves the accuracy of generation by referring to the user's past training videos when generating a video. For example, the video generation unit analyzes the user's past training videos to generate an effective video. The video generation unit can also adjust the generation algorithm by referring to the user's past training videos. The video generation unit can also improve the accuracy of the generation results based on the user's past training videos. In this way, by referring to the user's past training videos, the accuracy of generation is improved. Some or all of the above-mentioned processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input the user's past training videos into the generation AI and cause the generation AI to perform analysis to improve the accuracy of generation.

[0058] The video generation unit can determine the generation priority based on the submission time of the training method when generating the video. The video generation unit determines the generation priority based on the submission time of the training method when generating the video. For example, the video generation unit prioritizes animating the latest training method. The video generation unit can also prioritize animating a training method specified by a user. The video generation unit can also adjust the order of generation based on the submission time of the training method. In this way, by determining the generation priority based on the submission time of the training method, the latest information can be prioritized to be animated. Some or all of the above-described processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input the submission time of the training method to the generation AI and cause the generation AI to perform an analysis to determine the generation priority.

[0059] The video generation unit can adjust the generation order based on the relevance of the training methods when generating videos. The video generation unit adjusts the generation order based on the relevance of the training methods when generating videos. For example, the video generation unit prioritizes animating training methods related to the user's goal. The video generation unit can also adjust the generation order based on the effectiveness of the training methods. The video generation unit can also adjust the generation order based on the popularity of the training methods. In this way, by adjusting the generation order based on the relevance of the training methods, more effective videos can be provided. Some or all of the above-described processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input the relevance of the training methods into the generation AI and cause the generation AI to perform an analysis to adjust the generation order.

[0060] The video generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise when generating a video. The video generation unit adjusts the use of technical terminology in the generation according to the user's level of expertise when generating a video. For example, if the user is a beginner, the video generation unit generates a video using simple terminology. If the user is an intermediate user, the video generation unit can also generate a video using appropriate technical terminology. If the user is an advanced user, the video generation unit can also generate a video using detailed technical terminology. In this way, by adjusting the use of technical terminology in the generation according to the user's level of expertise, a video that is easier to understand can be provided. Some or all of the above-described processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input the user's level of expertise into the generation AI and cause the generation AI to perform an analysis to adjust the use of technical terminology in the generation.

[0061] The navigation unit can apply different navigation algorithms when performing navigation according to the orientation of the user's smartphone. The navigation unit applies different navigation algorithms when performing navigation according to the orientation of the user's smartphone. For example, if the user holds the smartphone vertically, the navigation unit provides navigation optimized for portrait orientation. If the user holds the smartphone horizontally, the navigation unit can also provide navigation optimized for landscape orientation. If the user holds the smartphone at an angle, the navigation unit can also provide navigation optimized for diagonal orientation. This improves user convenience by providing navigation according to the orientation of the smartphone. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can use a gyro sensor or an acceleration sensor to detect the orientation of the smartphone.

[0062] The navigation unit can adjust the level of detail of navigation based on the user's walking speed during navigation. The navigation unit can adjust the level of detail of navigation based on the user's walking speed during navigation. For example, the navigation unit provides detailed navigation when the user is walking slowly. The navigation unit can also provide concise navigation when the user is walking fast. The navigation unit can also pause navigation when the user stops and resume when the user starts walking again. In this way, more appropriate navigation can be provided by adjusting the level of detail of navigation according to the user's walking speed. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can detect the user's walking speed using a pedometer or an acceleration sensor and adjust the level of detail of navigation.

[0063] The navigation unit can improve navigation accuracy by referring to the user's past navigation history during navigation. The navigation unit can improve navigation accuracy by referring to the user's past navigation history during navigation. For example, the navigation unit analyzes the user's past navigation history to provide effective navigation. The navigation unit can also adjust a navigation algorithm by referring to the user's past navigation history. The navigation unit can also improve navigation accuracy based on the user's past navigation history. In this way, navigation accuracy is improved by referring to the user's past navigation history. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's past navigation history into AI and have the AI ​​perform analysis to improve navigation accuracy.

[0064] The navigation unit can select the optimal navigation method during navigation by taking into account the user's geographical location information. The navigation unit selects the optimal navigation method during navigation by taking into account the user's geographical location information. For example, if the user is in an urban area, the navigation unit can provide navigation using public transportation. If the user is in the suburbs, the navigation unit can also provide navigation using a car or bicycle. If the user is in a tourist destination, the navigation unit can also provide navigation around tourist spots. This makes it possible to provide more appropriate navigation by taking into account the user's geographical location information. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's geographical location information into AI and have the AI ​​perform analysis to select the optimal navigation method.

[0065] The navigation unit can analyze the user's social media activity during navigation and suggest relevant navigation methods. The navigation unit can analyze the user's social media activity during navigation and suggest relevant navigation methods. For example, the navigation unit provides navigation based on locations where the user has checked in on social media. The navigation unit can also analyze the user's social media posts and provide navigation to relevant tourist spots and stores. The navigation unit can also provide navigation to relevant places and events based on the activities of the user's friends on social media. In this way, more relevant navigation can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the content of social media posts into AI and have the AI ​​perform an analysis to suggest relevant navigation methods.

[0066] The navigation unit can customize the navigation method by reflecting the user's past feedback during navigation. The navigation unit customizes the navigation method by reflecting the user's past feedback during navigation. For example, the navigation unit preferentially provides navigation methods that the user has previously rated highly. The navigation unit can also exclude and provide navigation methods that the user has previously rated poorly. The navigation unit can also adjust the level of detail and display method of the navigation based on the user's feedback. This makes it possible to provide more appropriate navigation by reflecting the user's past feedback. Some or all of the above-described processing in the navigation unit may be performed using, or without, AI, for example. For example, the navigation unit can input user feedback data into AI and have the AI ​​perform analysis to customize the navigation method.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The collection unit can also collect area-specific training methods and facility information based on the user's geographical location information. For example, if the user is in an urban area, the collection unit can collect city-specific training facility and event information. If the user is in the suburbs, the collection unit can collect training methods that utilize natural environments. If the user is traveling, the collection unit can collect information on training facilities and running courses in the user's location. This allows for more relevant data to be collected by taking the user's geographical location information into consideration.

[0069] The video generation unit can also customize the style and content of the video to be generated by referring to the user's past training videos. For example, the video generation unit analyzes the style of training videos that the user has liked to watch in the past and generates a new video in a similar style. The video generation unit can also generate a video including a related training method by referring to the content of videos that the user has watched in the past. The video generation unit can also adjust the length and level of detail of the video to be generated based on the user's past viewing history. In this way, by referring to the user's past training videos, it is possible to provide a more individually optimized video.

[0070] The collection unit may also analyze the user's social media activity to collect related training methods and trends. For example, the collection unit may collect training methods shared by the user on social media. The collection unit may also collect training methods recommended by the user's followers. The collection unit may also collect training data from fitness communities in which the user participates. This allows for more relevant data to be collected by analyzing the user's social media activity.

[0071] The generation unit can also increase the variety of menus to be generated by referring to the user's past training menus. For example, the generation unit analyzes the effects of training menus that the user has performed in the past and generates a new menu with similar effects. The generation unit can also generate a new menu by combining training methods that the user has preferred in the past. The generation unit can also adjust the difficulty and content of the menu to be generated based on the user's past training history. In this way, by referring to the user's past training menus, it is possible to provide a more individually optimized menu.

[0072] The navigation unit can also customize the navigation route and method by referring to the user's past navigation history. For example, the navigation unit can prioritize and suggest routes that the user has used favorably in the past. The navigation unit can also suggest routes that the user has avoided in the past by excluding them. The navigation unit can also adjust the level of detail and display method of the navigation based on the user's past navigation history. This makes it possible to provide more individually optimized navigation by referring to the user's past navigation history.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The collection unit collects data. The data includes, for example, articles, videos, research papers, etc. related to training methods on the Internet. The collection unit collects articles related to training methods from specific websites, collects videos related to training methods from video sharing sites, and collects research papers related to training methods from academic databases. Step 2: The analysis unit analyzes the data collected by the collection unit and extracts the training methods necessary for promoting health and achieving an ideal body shape. The analysis unit extracts the characteristics of the training methods using natural language processing technology and image recognition technology. For example, it analyzes text data of training methods using morphological analysis and video data of training methods using object detection technology. Step 3: The generation unit generates a training menu based on the training methods extracted by the analysis unit. The generation unit generates a training menu using a generation AI and updates the training menu based on each individual's preferences and vital data. Step 4: The video generation unit creates videos of training methods from the text generated by the generation unit. The video generation unit uses generation AI to create videos of strength training, aerobic exercise, and stretching based on the text data. Step 5: The navigation unit performs training based on the video created by the video generation unit. The navigation unit displays the video according to the user's smartphone orientation and walking speed, and estimates the user's emotions to adjust the navigation method.

[0075] (Example 2) A training menu generation system according to an embodiment of the present invention utilizes AI and Big Data to easily create personalized training menus. In this system, a generation AI extracts training methods necessary for health promotion and achieving an ideal physique from Big Data on the Internet and generates a menu that takes into account each individual's preferences. Next, the generation AI creates training method videos from the generated text. Furthermore, a vital sensor is used to acquire each individual's lifestyle data (e.g., sleep time, number of steps, weight), and the training menu is constantly updated based on this data. For example, the training menu generation system analyzes Big Data on the Internet to extract training methods necessary for health promotion and achieving an ideal physique. This also takes into account each individual's preferences, generating an optimal training menu for each individual. Next, the generation AI creates training method videos from the generated text. This makes it easier for users to visually understand the training methods. Furthermore, a vital sensor is used to acquire each individual's lifestyle data. Specifically, vital data such as sleep time, number of steps, and weight is collected, and the training menu is constantly updated based on this data. In this way, the training menu generation system provides optimal training menus for each individual, supporting health promotion and achieving an ideal physique. This allows the training menu generation system to provide the optimal training menu for each individual, supporting them in promoting health and achieving their ideal body shape. For example, it is possible to provide training tailored to individual goals, such as a menu focusing on strength training or a menu emphasizing aerobic exercise. In addition, by updating the menu based on lifestyle data, training tailored to the latest conditions is always provided.

[0076] A training menu generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a video generation unit, and a navigation unit. The collection unit collects data. Examples of the data include, but are not limited to, articles, videos, and research papers about training methods on the Internet. For example, the collection unit collects articles about training methods on the Internet. The collection unit can also collect videos about training methods on the Internet. The collection unit can also collect research papers about training methods on the Internet. For example, the collection unit collects articles about training methods from specific websites. The collection unit can also collect videos about training methods from video sharing sites. The collection unit can also collect research papers about training methods from academic databases. The analysis unit analyzes the data collected by the collection unit and extracts training methods necessary for promoting health and achieving an ideal physique. The analysis unit extracts characteristics of the training methods using, for example, natural language processing technology. The analysis unit can also extract characteristics of the training methods using image recognition technology. The analysis unit can also extract characteristics of the training methods by combining natural language processing technology and image recognition technology. For example, the analysis unit analyzes text data of a training method using morphological analysis. The analysis unit can also analyze video data of a training method using object detection technology. The analysis unit can combine natural language processing technology and image recognition technology to extract features of the training method with high accuracy. The generation unit generates a training menu based on the training method extracted by the analysis unit. The generation unit generates the training menu using, for example, a generation AI. The generation unit can also generate a training menu taking into account each person's preferences. The generation unit can also update the training menu based on vital data. For example, the generation unit generates a training menu that combines strength training and aerobic exercise using a generation AI. The generation unit can also generate a training menu that includes stretching and yoga based on each person's preferences.The generation unit can constantly update the training menu to the latest state based on the vital data. The video generation unit creates videos of training methods from the text generated by the generation unit. The video generation unit creates videos of training methods from text using, for example, a generation AI. The video generation unit can also create videos of strength training using the generation AI. The video generation unit can also create videos of aerobic exercise using the generation AI. The video generation unit can also create videos of stretching using the generation AI. For example, the video generation unit uses a generation AI to create videos of strength training based on text data. The video generation unit can also use a generation AI to create videos of aerobic exercise based on the text data. The video generation unit can also use a generation AI to create videos of stretching based on the text data. The navigation unit performs training based on the videos created by the video generation unit. The navigation unit performs navigation based on, for example, the orientation of the user's smartphone. The navigation unit can also display videos based on the user's walking speed. The navigation unit can also estimate the user's emotions and adjust the navigation method based on the estimated emotions. For example, the navigation unit may provide navigation optimized for portrait orientation according to the orientation of the user's smartphone. The navigation unit may also adjust the video playback speed according to the user's walking speed. The navigation unit may also estimate the user's emotions and provide simple navigation if the user is feeling stressed. As a result, the training menu generation system according to the embodiment can provide an optimal training menu for each individual, supporting the promotion of health and the achievement of an ideal body shape.

[0077] The collection unit collects data on articles, videos, and research papers about training methods from the Internet. The collection unit collects data on articles, videos, and research papers about training methods from the Internet. For example, the collection unit collects articles about training methods from a specific website. The collection unit can also collect videos about training methods from video sharing sites. The collection unit can also collect research papers about training methods from academic databases. This allows for the collection of training methods from various data sources on the Internet, thereby providing richer information. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may perform web scraping using AI to collect articles about training methods from a specific website.

[0078] The analysis unit can extract features of the training method using natural language processing or image recognition technology. The analysis unit extracts features of the training method using natural language processing or image recognition technology. For example, the analysis unit analyzes text data of the training method using morphological analysis. The analysis unit can also analyze video data of the training method using object detection technology. The analysis unit can combine natural language processing technology and image recognition technology to extract features of the training method with high accuracy. As a result, by using natural language processing or image recognition technology, features of the training method can be extracted with high accuracy. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use AI to analyze text data of the training method using natural language processing technology and extract features.

[0079] The generation unit can generate a training menu based on each person's preferences. The generation unit generates a training menu based on each person's preferences. For example, the generation unit uses a generation AI to generate a training menu that combines strength training and aerobic exercise. The generation unit can also generate a training menu that includes stretching and yoga based on each person's preferences. The generation unit can constantly update the training menu to the latest version based on vital data. This makes it possible to provide a more individually optimized training menu by taking each person's preferences into consideration. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can use a generation AI to generate a training menu based on each person's preferences.

[0080] The video generation unit can create a training method video from text using a generation AI. The video generation unit creates a training method video from text using a generation AI. For example, the video generation unit can use a generation AI to create a strength training video based on text data. The video generation unit can also use a generation AI to create an aerobic exercise video based on text data. The video generation unit can also use a generation AI to create a stretching video based on text data. In this way, high-quality training videos can be automatically generated from text using a generation AI. Some or all of the above-described processing in the video generation unit may be performed using a generation AI, for example, or may be performed without using a generation AI. For example, the video generation unit can use a generation AI to create a training method video based on text data.

[0081] The navigation unit can perform navigation according to the orientation of the user's smartphone. The navigation unit performs navigation according to the orientation of the user's smartphone. For example, the navigation unit provides navigation optimized for portrait orientation according to the orientation of the user's smartphone. The navigation unit can also provide navigation optimized for landscape orientation according to the orientation of the user's smartphone. The navigation unit can also provide navigation optimized for diagonal orientation according to the orientation of the user's smartphone. This improves user convenience by providing navigation according to the orientation of the smartphone. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can use a gyro sensor or an acceleration sensor to detect the orientation of the smartphone.

[0082] The collection unit can collect vital data such as sleep time, number of steps, and weight using a vital sensor. The collection unit collects vital data such as sleep time, number of steps, and weight using a vital sensor. For example, the collection unit collects heart rate using a heart rate sensor. The collection unit can also collect number of steps using a pedometer. The collection unit can also collect weight using a weight scale. In this way, by using the vital sensor, the user's health condition can be understood in detail. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from the vital sensor into AI and have the AI ​​analyze the data.

[0083] The generation unit can update the training menu based on the vital data. The generation unit updates the training menu based on the vital data. For example, if the sleep time is short, the generation unit may add stretches that have a relaxing effect. If the number of steps is low, the generation unit may also increase aerobic exercise. If weight is increasing, the generation unit may also provide a training menu that emphasizes calorie consumption. In this way, by updating the training menu based on the vital data, training that is always tailored to the latest condition is provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit may input vital data into the generation AI and cause the generation AI to update the training menu.

[0084] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection and collect data when the user is relaxed. If the user is relaxed, the collection unit can also increase the frequency of data collection and collect more detailed data. If the user is in a hurry, the collection unit can temporarily stop data collection and resume it later. This enables more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0085] The collection unit may prioritize reliable sources when collecting data such as articles, videos, and research papers about training methods on the Internet. The collection unit prioritizes reliable sources when collecting data such as articles, videos, and research papers about training methods on the Internet. For example, the collection unit may prioritize collecting academic papers and expert blogs. The collection unit may also prioritize collecting information from public institutions and certified fitness organizations. The collection unit may also prioritize collecting training videos that are highly rated by users. This prioritizes the selection of reliable sources, thereby providing more accurate information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may use AI to evaluate the reliability of information in order to select reliable sources.

[0086] The collection unit can customize the type of data collected using the vital sensor according to the user's health condition. The collection unit customizes the type of data collected using the vital sensor according to the user's health condition. For example, if the user is tired, the collection unit may focus on collecting heart rate and stress level. If the user is in good health, the collection unit may also focus on collecting exercise volume and calorie consumption. If the user is in poor health, the collection unit may also focus on collecting sleep quality and rest time. This enables more appropriate data collection by customizing the type of data collected according to the user's health condition. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input data acquired from the vital sensor into AI and have the AI ​​perform analysis to customize the type of data.

[0087] The collection unit can analyze the user's past training history and select the optimal data collection method. The collection unit analyzes the user's past training history and selects the optimal data collection method. For example, the collection unit analyzes the type and frequency of training the user has performed in the past and collects similar data. The collection unit can also customize data collection based on the user's preferred training methods in the past. The collection unit can also select an effective data collection method from the user's past training history. This enables more effective data collection by analyzing the user's past training history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past training history into AI and have the AI ​​perform an analysis to select the optimal data collection method.

[0088] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting training data that has a relaxing effect. If the user is relaxed, the collection unit can also prioritize collecting data on strength training and aerobic exercise. If the user is in a hurry, the collection unit can also prioritize collecting training data that is effective in a short period of time. This enables more appropriate data collection by determining the priority of data to be collected based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the collection unit can be performed using an AI, for example, or without an AI. For example, the collection unit can input the user's facial expression data into a generation AI and have the generation AI perform emotion estimation.

[0089] When collecting data, the collection unit can prioritize collecting highly relevant data based on the user's geographical location information. When collecting data, the collection unit prioritizes collecting highly relevant data based on the user's geographical location information. For example, when a user trains outdoors, the collection unit collects information about nearby parks and training facilities. When a user trains at home, the collection unit can also collect data on training methods that can be done at home. When a user is traveling, the collection unit can also collect information about training facilities and running courses at the user's location. This allows for collection of more relevant data by taking the user's geographical location information into consideration. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the user's geographical location information into AI and cause the AI ​​to perform analysis to prioritize collecting highly relevant data.

[0090] The collection unit may analyze the user's social media activity and collect relevant data when collecting data. The collection unit may analyze the user's social media activity and collect relevant data when collecting data. For example, the collection unit may collect training methods shared by the user on social media. The collection unit may also collect training methods recommended by the user's followers. The collection unit may also collect training data from fitness communities in which the user participates. This allows for more relevant data to be collected by analyzing the user's social media activity. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the content of social media posts into AI and have the AI ​​perform analysis to collect relevant data.

[0091] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit customizes the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit prioritizes collecting training methods that the user has previously rated highly. The collection unit can also collect data by excluding training methods that the user has previously rated poorly. The collection unit can also adjust the type and frequency of data to be collected based on the user's feedback. This enables more appropriate data collection by reflecting the user's past feedback. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into AI and have the AI ​​perform analysis to customize the collection method.

[0092] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and visually easy-to-understand analysis result. If the user is relaxed, the analysis unit can provide a detailed analysis result to promote deeper understanding. If the user is in a hurry, the analysis unit can provide a concise analysis result that focuses on the main points. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can 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 processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0093] The analysis unit can apply different analysis algorithms when extracting features of a training method using natural language processing or image recognition technology. The analysis unit applies different analysis algorithms when extracting features of a training method using natural language processing or image recognition technology. For example, the analysis unit uses natural language processing to extract features from text data of a training method. The analysis unit can also use image recognition technology to extract movement features from a training video. The analysis unit can also combine different analysis algorithms to perform more accurate feature extraction. This enables more accurate feature extraction by applying different analysis algorithms. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use AI to combine natural language processing technology and image recognition technology to extract features of a training method.

[0094] The analysis unit can evaluate the effectiveness of the training method during analysis and select the optimal method. The analysis unit evaluates the effectiveness of the training method during analysis and selects the optimal method. For example, the analysis unit collects user feedback and reflects it in the analysis to evaluate the effectiveness of the training method. The analysis unit can also analyze vital data to evaluate the effectiveness of the training method and select the optimal method. The analysis unit can also analyze past training data to evaluate the effectiveness of the training method and select the optimal method. In this way, by evaluating the effectiveness of the training method, a more effective training method can be selected. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input user feedback data into AI and have the AI ​​perform an analysis to evaluate the effectiveness of the training method.

[0095] The analysis unit can improve the accuracy of the analysis by referring to the user's past training data during analysis. The analysis unit improves the accuracy of the analysis by referring to the user's past training data during analysis. For example, the analysis unit analyzes the user's past training data and evaluates the effectiveness of the training method. The analysis unit can also adjust the analysis algorithm by referring to the user's past training data. The analysis unit can also improve the accuracy of the analysis results based on the user's past training data. In this way, the accuracy of the analysis is improved by referring to the user's past training data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past training data into AI and have the AI ​​perform analysis to improve the accuracy of the analysis.

[0096] The analysis unit can estimate the user's emotion and adjust the level of analysis detail based on the estimated user's emotion. The analysis unit can estimate the user's emotion and adjust the level of analysis detail based on the estimated user's emotion. For example, the analysis unit can provide a concise analysis result when the user is stressed. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a concise analysis result when the user is in a hurry. This allows for adjusting the level of analysis detail according to the user's emotion to provide a more appropriate analysis result. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0097] The analysis unit can determine the priority of analysis based on the time of submission of the training methods during analysis. The analysis unit determines the priority of analysis based on the time of submission of the training methods during analysis. For example, the analysis unit prioritizes analysis of the most recent training methods. The analysis unit can also prioritize analysis of training methods specified by the user. The analysis unit can also adjust the order of analysis based on the time of submission of the training methods. In this way, by determining the priority of analysis based on the time of submission of the training methods, the most recent information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time of submission of the training methods into AI and cause the AI ​​to perform analysis to determine the priority of analysis.

[0098] The analysis unit can adjust the order of analysis based on the relevance of the training methods during analysis. The analysis unit adjusts the order of analysis based on the relevance of the training methods during analysis. For example, the analysis unit prioritizes analysis of training methods related to the user's goal. The analysis unit can also adjust the order of analysis based on the effectiveness of the training methods. The analysis unit can also adjust the order of analysis based on the popularity of the training methods. In this way, adjusting the order of analysis based on the relevance of the training methods enables more effective analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the training methods into AI and cause the AI ​​to perform analysis to adjust the order of analysis.

[0099] The analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise during analysis. The analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise during analysis. For example, if the user is a beginner, the analysis unit can provide analysis results using simple terminology. If the user is an intermediate user, the analysis unit can also provide analysis results using appropriate technical terminology. If the user is an advanced user, the analysis unit can also provide analysis results using detailed technical terminology. In this way, by adjusting the use of technical terminology in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easier to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise into AI and have the AI ​​perform an analysis to adjust the use of technical terminology in the analysis.

[0100] The generation unit can estimate the user's emotions and adjust the training menu generation method based on the estimated user's emotions. The generation unit can estimate the user's emotions and adjust the training menu generation method based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit generates a training menu that has a relaxing effect. If the user is relaxed, the generation unit can also generate a menu that includes strength training and aerobic exercise. If the user is in a hurry, the generation unit can also generate a training menu that is effective in a short amount of time. This allows for adjusting the training menu generation method according to the user's emotions to provide a more appropriate menu. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be 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-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0101] The generation unit can apply different generation algorithms when generating a training menu taking into account each person's preferences. The generation unit can apply different generation algorithms when generating a training menu taking into account each person's preferences. For example, the generation unit generates a menu that combines strength training and aerobic exercise based on the user's preferences. The generation unit can also generate a menu that includes stretching and yoga based on the user's preferences. The generation unit can also apply different generation algorithms to generate an optimal menu. By applying different generation algorithms, it is possible to provide a more individually optimized training menu. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate a training menu based on each person's preferences using different generation algorithms.

[0102] The generation unit can evaluate the effectiveness of the training menu and select an optimal menu when generating the menu. The generation unit evaluates the effectiveness of the training menu and selects an optimal menu when generating the menu. For example, the generation unit collects user feedback to evaluate the effectiveness of the training menu and reflects it in the generation. The generation unit can also analyze vital data to evaluate the effectiveness of the training menu and select an optimal menu. The generation unit can also analyze past training data to evaluate the effectiveness of the training menu and select an optimal menu. This allows the effectiveness of the training menu to be evaluated, thereby providing a more effective menu. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input user feedback data into the generation AI and cause the generation AI to perform an analysis to evaluate the effectiveness of the training menu.

[0103] The generation unit can improve the accuracy of generation by referring to the user's past training menus during generation. The generation unit can improve the accuracy of generation by referring to the user's past training menus during generation. For example, the generation unit analyzes the user's past training menus to generate an effective menu. The generation unit can also adjust the generation algorithm by referring to the user's past training menus. The generation unit can also improve the accuracy of the generated results based on the user's past training menus. In this way, by referring to the user's past training menus, the accuracy of generation is improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past training menus into the generation AI and cause the generation AI to perform analysis to improve the accuracy of the generation.

[0104] The generation unit can estimate the user's emotions and adjust the level of detail of the training menu based on the estimated user's emotions. The generation unit can estimate the user's emotions and adjust the level of detail of the training menu based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can generate a concise and to-the-point menu. If the user is relaxed, the generation unit can also generate a menu with detailed explanations. If the user is in a hurry, the generation unit can also generate a menu that is effective in a short amount of time. This allows for adjusting the level of detail of the training menu according to the user's emotions, thereby providing a more appropriate menu. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0105] The generation unit can determine the generation priority based on the submission time of the training menu at the time of generation. The generation unit determines the generation priority based on the submission time of the training menu at the time of generation. For example, the generation unit generates the most recent training menu with priority. The generation unit can also generate the training menu specified by the user with priority. The generation unit can also adjust the order of generation based on the submission time of the training menu. In this way, by determining the generation priority based on the submission time of the training menu, the most recent information can be generated with priority. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the submission time of the training menu into the generation AI and cause the generation AI to perform analysis to determine the generation priority.

[0106] The generation unit can adjust the order of generation based on the relevance of the training menus during generation. The generation unit adjusts the order of generation based on the relevance of the training menus during generation. For example, the generation unit prioritizes generating training menus related to the user's goals. The generation unit can also adjust the order of generation based on the effectiveness of the training menus. The generation unit can also adjust the order of generation based on the popularity of the training menus. In this way, by adjusting the order of generation based on the relevance of the training menus, a more effective menu can be provided. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the relevance of the training menus into the generation AI and cause the generation AI to perform analysis to adjust the order of generation.

[0107] The generation unit can adjust the use of technical terminology in the generated menu according to the user's level of expertise during generation. The generation unit adjusts the use of technical terminology in the generated menu according to the user's level of expertise during generation. For example, if the user is a beginner, the generation unit generates a menu using simple terminology. If the user is an intermediate user, the generation unit can also generate a menu using moderate terminology. If the user is an advanced user, the generation unit can also generate a menu using detailed terminology. This makes it possible to provide a menu that is easier to understand by adjusting the use of technical terminology in the generated menu according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise into the generation AI and cause the generation AI to perform an analysis to adjust the use of technical terminology in the generated menu.

[0108] The video generation unit can estimate the user's emotions and adjust the video generation method based on the estimated user emotions. The video generation unit can estimate the user's emotions and adjust the video generation method based on the estimated user emotions. For example, if the user is relaxed, the video generation unit generates a video that progresses at a leisurely pace using the generation AI. If the user is in a hurry, the video generation unit can also generate a video that emphasizes the shortest route. If the user is excited, the video generation unit can also generate a video that adds visually stimulating effects. This allows for adjusting the video generation method according to the user's emotions to provide a more appropriate video. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be 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-mentioned processing in the video generation unit may be performed using, for example, the generation AI, or without the generation AI. For example, the video generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0109] The video generation unit can apply different generation algorithms when using a generation AI to create a training method video from text. The video generation unit can apply different generation algorithms when using a generation AI to create a training method video from text. For example, the video generation unit generates a strength training video based on text data using a generation AI. The video generation unit can also generate aerobic exercise videos based on text data using a generation AI. The video generation unit can also generate stretching videos based on text data using a generation AI. In this way, by applying different generation algorithms, a wider variety of training videos can be provided. Some or all of the above-described processing in the video generation unit may be performed using a generation AI, for example, or may be performed without using a generation AI. For example, the video generation unit can use different generation algorithms to create a training method video based on text data.

[0110] The video generation unit can evaluate the effectiveness of the training method and select the optimal video when generating the video. The video generation unit evaluates the effectiveness of the training method and selects the optimal video when generating the video. For example, the video generation unit collects user feedback to evaluate the effectiveness of the training method and reflects it in the generation. The video generation unit can also analyze vital data to evaluate the effectiveness of the training method and select the optimal video. The video generation unit can also analyze past training data to evaluate the effectiveness of the training method and select the optimal video. In this way, by evaluating the effectiveness of the training method, more effective videos can be provided. Some or all of the above-mentioned processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input user feedback data into the generation AI and cause the generation AI to perform an analysis to evaluate the effectiveness of the training method.

[0111] The video generation unit can improve the accuracy of generation by referring to the user's past training videos when generating a video. The video generation unit improves the accuracy of generation by referring to the user's past training videos when generating a video. For example, the video generation unit analyzes the user's past training videos to generate an effective video. The video generation unit can also adjust the generation algorithm by referring to the user's past training videos. The video generation unit can also improve the accuracy of the generation results based on the user's past training videos. In this way, by referring to the user's past training videos, the accuracy of generation is improved. Some or all of the above-mentioned processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input the user's past training videos into the generation AI and cause the generation AI to perform analysis to improve the accuracy of generation.

[0112] The video generation unit can estimate the user's emotions and adjust the level of detail of the video based on the estimated user's emotions. The video generation unit can estimate the user's emotions and adjust the level of detail of the video based on the estimated user's emotions. For example, if the user is feeling stressed, the video generation unit generates a concise and to-the-point video. If the user is relaxed, the video generation unit can also generate a video that includes detailed explanations. If the user is in a hurry, the video generation unit can also generate a quick and effective video. This allows for providing a more appropriate video by adjusting the level of detail of the video according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the video generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the video generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0113] The video generation unit can determine the generation priority based on the submission time of the training method when generating the video. The video generation unit determines the generation priority based on the submission time of the training method when generating the video. For example, the video generation unit prioritizes animating the latest training method. The video generation unit can also prioritize animating a training method specified by a user. The video generation unit can also adjust the order of generation based on the submission time of the training method. In this way, by determining the generation priority based on the submission time of the training method, the latest information can be prioritized to be animated. Some or all of the above-described processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input the submission time of the training method to the generation AI and cause the generation AI to perform an analysis to determine the generation priority.

[0114] The video generation unit can adjust the generation order based on the relevance of the training methods when generating videos. The video generation unit adjusts the generation order based on the relevance of the training methods when generating videos. For example, the video generation unit prioritizes animating training methods related to the user's goal. The video generation unit can also adjust the generation order based on the effectiveness of the training methods. The video generation unit can also adjust the generation order based on the popularity of the training methods. In this way, by adjusting the generation order based on the relevance of the training methods, more effective videos can be provided. Some or all of the above-described processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input the relevance of the training methods into the generation AI and cause the generation AI to perform an analysis to adjust the generation order.

[0115] The video generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise when generating a video. The video generation unit adjusts the use of technical terminology in the generation according to the user's level of expertise when generating a video. For example, if the user is a beginner, the video generation unit generates a video using simple terminology. If the user is an intermediate user, the video generation unit can also generate a video using appropriate technical terminology. If the user is an advanced user, the video generation unit can also generate a video using detailed technical terminology. In this way, by adjusting the use of technical terminology in the generation according to the user's level of expertise, a video that is easier to understand can be provided. Some or all of the above-described processing in the video generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the video generation unit can input the user's level of expertise into the generation AI and cause the generation AI to perform an analysis to adjust the use of technical terminology in the generation.

[0116] The navigation unit can estimate the user's emotions and adjust the navigation method based on the estimated user emotions. The navigation unit can estimate the user's emotions and adjust the navigation method based on the estimated user emotions. For example, when the user is stressed, the navigation unit provides simple, highly visible navigation. When the user is relaxed, the navigation unit can also provide navigation that includes detailed information. When the user is in a hurry, the navigation unit can also provide navigation that focuses on the main points. This allows for more appropriate navigation by adjusting the navigation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0117] The navigation unit can apply different navigation algorithms when performing navigation according to the orientation of the user's smartphone. The navigation unit applies different navigation algorithms when performing navigation according to the orientation of the user's smartphone. For example, if the user holds the smartphone vertically, the navigation unit provides navigation optimized for portrait orientation. If the user holds the smartphone horizontally, the navigation unit can also provide navigation optimized for landscape orientation. If the user holds the smartphone at an angle, the navigation unit can also provide navigation optimized for diagonal orientation. This improves user convenience by providing navigation according to the orientation of the smartphone. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can use a gyro sensor or an acceleration sensor to detect the orientation of the smartphone.

[0118] The navigation unit can adjust the level of detail of navigation based on the user's walking speed during navigation. The navigation unit can adjust the level of detail of navigation based on the user's walking speed during navigation. For example, the navigation unit provides detailed navigation when the user is walking slowly. The navigation unit can also provide concise navigation when the user is walking fast. The navigation unit can also pause navigation when the user stops and resume when the user starts walking again. In this way, more appropriate navigation can be provided by adjusting the level of detail of navigation according to the user's walking speed. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can detect the user's walking speed using a pedometer or an acceleration sensor and adjust the level of detail of navigation.

[0119] The navigation unit can improve navigation accuracy by referring to the user's past navigation history during navigation. The navigation unit can improve navigation accuracy by referring to the user's past navigation history during navigation. For example, the navigation unit analyzes the user's past navigation history to provide effective navigation. The navigation unit can also adjust a navigation algorithm by referring to the user's past navigation history. The navigation unit can also improve navigation accuracy based on the user's past navigation history. In this way, navigation accuracy is improved by referring to the user's past navigation history. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's past navigation history into AI and have the AI ​​perform analysis to improve navigation accuracy.

[0120] The navigation unit can estimate the user's emotions and determine navigation priorities based on the estimated user emotions. The navigation unit can estimate the user's emotions and determine navigation priorities based on the estimated user emotions. For example, if the user is feeling stressed, the navigation unit can prioritize providing navigation that has a relaxing effect. If the user is relaxed, the navigation unit can also prioritize providing detailed navigation. If the user is in a hurry, the navigation unit can also prioritize providing navigation that focuses on the main points. This allows for more appropriate navigation to be provided by determining navigation priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the navigation unit may be performed using AI, for example, or without AI. For example, the navigation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0121] The navigation unit can select the optimal navigation method during navigation by taking into account the user's geographical location information. The navigation unit selects the optimal navigation method during navigation by taking into account the user's geographical location information. For example, if the user is in an urban area, the navigation unit can provide navigation using public transportation. If the user is in the suburbs, the navigation unit can also provide navigation using a car or bicycle. If the user is in a tourist destination, the navigation unit can also provide navigation around tourist spots. This makes it possible to provide more appropriate navigation by taking into account the user's geographical location information. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the user's geographical location information into AI and have the AI ​​perform analysis to select the optimal navigation method.

[0122] The navigation unit can analyze the user's social media activity during navigation and suggest relevant navigation methods. The navigation unit can analyze the user's social media activity during navigation and suggest relevant navigation methods. For example, the navigation unit provides navigation based on locations where the user has checked in on social media. The navigation unit can also analyze the user's social media posts and provide navigation to relevant tourist spots and stores. The navigation unit can also provide navigation to relevant places and events based on the activities of the user's friends on social media. In this way, more relevant navigation can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the navigation unit may be performed using, for example, AI, or may be performed without using AI. For example, the navigation unit can input the content of social media posts into AI and have the AI ​​perform an analysis to suggest relevant navigation methods.

[0123] The navigation unit can customize the navigation method by reflecting the user's past feedback during navigation. The navigation unit customizes the navigation method by reflecting the user's past feedback during navigation. For example, the navigation unit preferentially provides navigation methods that the user has previously rated highly. The navigation unit can also exclude and provide navigation methods that the user has previously rated poorly. The navigation unit can also adjust the level of detail and display method of the navigation based on the user's feedback. This makes it possible to provide more appropriate navigation by reflecting the user's past feedback. Some or all of the above-described processing in the navigation unit may be performed using, or without, AI, for example. For example, the navigation unit can input user feedback data into AI and have the AI ​​perform analysis to customize the navigation method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, video generation unit, and navigation unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the collection unit can collect articles and videos about training methods on the Internet using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to extract training methods necessary for promoting health and achieving an ideal body shape. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a training menu based on the analyzed data. The video generation unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and creates videos of training methods from the generated text. The navigation unit is realized, for example, by the control unit 46A of the smart device 14, and performs training based on the created videos. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, video generation unit, and navigation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the collection unit can collect articles and videos about training methods on the Internet using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to extract training methods necessary for promoting health and achieving an ideal body shape. The generation unit, for example, is realized by the specific processing unit 290 of the data processing device 12 and generates a training menu based on the analyzed data. The video generation unit, for example, is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and creates videos of training methods from the generated text. The navigation unit is realized by, for example, the control unit 46A of the smart glasses 214, and performs training based on the created video. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, video generation unit, and navigation unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the collection unit can collect articles and videos about training methods from the Internet using the camera 42 and communication I / F 44 of the headset-type terminal 314. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and extracts training methods necessary for promoting health and achieving an ideal body shape. The generation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, generates a training menu based on the analyzed data. The video generation unit, implemented, for example, by the control unit 46A of the headset-type terminal 314 or the specific processing unit 290 of the data processing device 12, creates videos of training methods from the generated text. The navigation unit is realized by, for example, the control unit 46A of the headset type terminal 314, and performs training based on the created video. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, video generation unit, and navigation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the collection unit can collect articles and videos about training methods from the Internet using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to extract training methods necessary for promoting health and achieving an ideal body shape. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates a training menu based on the analyzed data. The video generation unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12, and creates videos of training methods from the generated text. The navigation unit is realized, for example, by the control unit 46A of the robot 414, and performs training based on the created videos.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] The analysis unit can also estimate the user's emotions and determine the priority of analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing training methods that have a relaxing effect. If the user is relaxed, the analysis unit can also prioritize analyzing data on strength training or aerobic exercise. If the user is in a hurry, the analysis unit can also prioritize analyzing training methods that are effective in a short amount of time. In this way, by determining the priority of analysis according to the user's emotions, more appropriate analysis results can be provided.

[0126] The collection unit can also collect area-specific training methods and facility information based on the user's geographical location information. For example, if the user is in an urban area, the collection unit can collect city-specific training facility and event information. If the user is in the suburbs, the collection unit can collect training methods that utilize natural environments. If the user is traveling, the collection unit can collect information on training facilities and running courses in the user's location. This allows for more relevant data to be collected by taking the user's geographical location information into consideration.

[0127] The generation unit can also estimate the user's emotions and adjust the difficulty level of the training menu based on the estimated user's emotions. For example, if the user is feeling stressed, the generation unit can generate an easy training menu that has a relaxing effect. If the user is relaxed, the generation unit can also generate a menu that includes strength training and aerobic exercise. If the user is in a hurry, the generation unit can also generate an effective training menu that can be completed in a short amount of time. In this way, by adjusting the difficulty level of the training menu according to the user's emotions, a more appropriate menu can be provided.

[0128] The video generation unit can also customize the style and content of the video to be generated by referring to the user's past training videos. For example, the video generation unit analyzes the style of training videos that the user has liked to watch in the past and generates a new video in a similar style. The video generation unit can also generate a video including a related training method by referring to the content of videos that the user has watched in the past. The video generation unit can also adjust the length and level of detail of the video to be generated based on the user's past viewing history. In this way, by referring to the user's past training videos, it is possible to provide a more individually optimized video.

[0129] The navigation unit can also estimate the user's emotions and adjust the navigation voice guidance based on the estimated user's emotions. For example, if the user is feeling stressed, the navigation unit can provide a relaxing guide in a calm voice. If the user is relaxed, the navigation unit can provide a detailed guide in a cheerful voice. If the user is in a hurry, the navigation unit can provide a concise guide that focuses on the main points. In this way, more appropriate navigation can be provided by adjusting the navigation voice guidance according to the user's emotions.

[0130] The collection unit may also analyze the user's social media activity to collect related training methods and trends. For example, the collection unit may collect training methods shared by the user on social media. The collection unit may also collect training methods recommended by the user's followers. The collection unit may also collect training data from fitness communities in which the user participates. This allows for more relevant data to be collected by analyzing the user's social media activity.

[0131] The analysis unit can also estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide simple, visually easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results to encourage deeper understanding. If the user is in a hurry, the analysis unit can provide concise analysis results that focus on the main points. In this way, by adjusting the display method of the analysis results according to the user's emotions, more appropriate analysis results can be provided.

[0132] The generation unit can also increase the variety of menus to be generated by referring to the user's past training menus. For example, the generation unit analyzes the effects of training menus that the user has performed in the past and generates a new menu with similar effects. The generation unit can also generate a new menu by combining training methods that the user has preferred in the past. The generation unit can also adjust the difficulty and content of the menu to be generated based on the user's past training history. In this way, by referring to the user's past training menus, it is possible to provide a more individually optimized menu.

[0133] The video generation unit can also estimate the user's emotions and adjust video effects and music based on the estimated user's emotions. For example, if the user is relaxed, the video generation unit can generate a video using calm music and soothing effects. If the user is excited, the video generation unit can generate a video using up-tempo music and visually stimulating effects. If the user is feeling stressed, the video generation unit can generate a video using relaxing music and simple effects. In this way, by adjusting the video effects and music according to the user's emotions, more appropriate videos can be provided.

[0134] The navigation unit can also customize the navigation route and method by referring to the user's past navigation history. For example, the navigation unit can prioritize and suggest routes that the user has used favorably in the past. The navigation unit can also suggest routes that the user has avoided in the past by excluding them. The navigation unit can also adjust the level of detail and display method of the navigation based on the user's past navigation history. This makes it possible to provide more individually optimized navigation by referring to the user's past navigation history.

[0135] The processing flow of the second embodiment will be briefly explained below.

[0136] Step 1: The collection unit collects data. The data includes, for example, articles, videos, research papers, etc. related to training methods on the Internet. The collection unit collects articles related to training methods from specific websites, collects videos related to training methods from video sharing sites, and collects research papers related to training methods from academic databases. Step 2: The analysis unit analyzes the data collected by the collection unit and extracts the training methods necessary for promoting health and achieving an ideal body shape. The analysis unit extracts the characteristics of the training methods using natural language processing technology and image recognition technology. For example, it analyzes text data of training methods using morphological analysis and video data of training methods using object detection technology. Step 3: The generation unit generates a training menu based on the training methods extracted by the analysis unit. The generation unit generates a training menu using a generation AI and updates the training menu based on each individual's preferences and vital data. Step 4: The video generation unit creates videos of training methods from the text generated by the generation unit. The video generation unit uses generation AI to create videos of strength training, aerobic exercise, and stretching based on the text data. Step 5: The navigation unit performs training based on the video created by the video generation unit. The navigation unit displays the video according to the user's smartphone orientation and walking speed, and estimates the user's emotions to adjust the navigation method.

[0137] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0138] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0159] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0164] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0176] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0177] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0179] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0180] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0181] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0182] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0183] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0184] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0185] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0186] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0187] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0188] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0189] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0190] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0191] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0192] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0193] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0194] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0195] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0197] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0198] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0199] 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.

[0200] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0201] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0202] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0203] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0204] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0205] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0206] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0207] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0208] [Explanation of symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit and extracts training methods necessary for promoting health and achieving an ideal body shape; a generation unit that generates a training menu based on the training method extracted by the analysis unit; a video generation unit that generates a video of a training method from the text generated by the generation unit; a navigation unit that performs training based on the video created by the video creation unit. A system characterized by:

2. The system according to claim 1 , wherein the collection unit collects data on articles, videos, and research papers on training methods on the Internet.

3. The analysis unit Extracting the characteristics of training methods using natural language processing and image recognition technology 2. The system of claim 1.

4. The system according to claim 1 , wherein the generating unit generates a training menu based on the preferences of each individual.

5. The video generation unit Creating training videos from text using generative AI 2. The system of claim 1.

6. The navigation unit Navigation based on the orientation of the user's smartphone 2. The system of claim 1.

7. The collecting unit Using a vital sensor, vital data such as sleep time, number of steps, and weight are collected.

2. The system of claim 1.

8. The generation unit Update your training menu based on vital data 2. The system of claim 1.

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