system

Abstract

An object of a system according to an embodiment is to analyze a posture and a way of walking of an individual and propose a specific training plan.SOLUTION: A system according to an embodiment includes a posture analysis unit, an improvement point specification unit, a training proposal suggestion unit, a moving image linkage unit, and a goods suggestion unit. The posture analysis unit analyzes a posture and a way of walking of an individual. The improvement point specification unit specifies an improvement point on the basis of a result analyzed by the posture analysis unit. The training proposal suggestion unit suggests a specific training proposal based on the improvement point specified by the improvement point specification unit. The moving image linking unit searches the SNS for a moving image related to the training proposal proposed by the training proposal proposing unit, and provides the moving image. The goods proposing unit proposes goods related to the training proposed by the training proposal proposing 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] Conventional technologies have difficulty providing specific training suggestions for improving an individual's posture and walking style, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze an individual's posture and walking style and propose specific training plans. [Means for solving the problem]

[0006] The system according to the embodiment includes a posture analysis unit, an improvement point identification unit, a training plan proposal unit, a video linking unit, and a goods proposal unit. The posture analysis unit analyzes an individual's posture and walking style. The improvement point identification unit identifies improvements based on the results of the analysis by the posture analysis unit. The training plan proposal unit proposes specific training plans based on the improvements identified by the improvement point identification unit. The video linking unit searches social media for videos related to the training plan proposed by the training plan proposal unit and provides them. The goods proposal unit proposes goods related to the training proposed by the training plan proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze an individual's posture and walking style and propose specific training plans. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The posture improvement system according to an embodiment of the present invention uses AI to perform a detailed analysis of an individual's posture and walking style, identifies areas for improvement based on the results, and proposes specific training plans. Furthermore, the system links with related videos on social media to promote the sale of video lessons and related goods. This allows the posture improvement system to improve the user's posture and walking style, enabling them to live a healthier life. For example, it is expected to prevent stiff shoulders and back pain, and improve walking balance. Furthermore, related videos and video lessons on social media make it easier to maintain motivation for training. Furthermore, the sale of related goods can help create a better training environment for users.

[0029] A posture improvement system according to an embodiment includes a posture analysis unit, an improvement point identification unit, a training plan proposal unit, a video linking unit, and a product proposal unit. The posture analysis unit analyzes an individual's posture and gait. For example, the posture analysis unit records the individual's movements in real time using a smartphone camera or a dedicated wearable device. The posture analysis unit also performs detailed analysis of the movements of individual muscles and joints based on the posture and gait data analyzed by AI to identify subtle deviations in movement. The improvement point identification unit identifies improvements based on the results of the analysis by the posture analysis unit. For example, if the shoulders are positioned forward, the improvement point identification unit suggests training to retract the shoulder blades. The improvement point identification unit also compares the results with the user's past posture and gait data to analyze long-term changes and trends in improvement. The training plan proposal unit proposes specific training plans based on the improvements identified by the improvement point identification unit. For example, the training plan proposal unit proposes exercises to retract the shoulder blades or exercises to improve knee joint stability. The video linking unit searches social media for videos related to the training plans proposed by the training plan proposal unit and provides them. For example, the system automatically selects appropriate training videos from platforms such as YouTube and Instagram and encourages users to watch them. The goods suggestion unit suggests goods related to the training suggested by the training plan suggestion unit, such as posture correction belts, training mats, and walking shoes. As a result, the posture improvement system according to the embodiment analyzes the user's posture and walking style in detail, identifies areas for improvement, suggests specific training plans, and provides related videos and goods to support the user in improving their health.

[0030] The posture analysis unit can record an individual's movements in real time using a smartphone camera or a dedicated wearable device. For example, the posture analysis unit scans a handwritten answer sheet and saves it as image data. It then converts the image data into text data using OCR technology. The posture analysis unit can also photograph a handwritten answer sheet using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app automatically corrects the image and performs character recognition. The posture analysis unit can also write handwritten answers with a dedicated digital pen, which then converts the data into digital data in real time. For example, a sensor can detect the pen's movements and save them as text data. This conversion of handwritten answers into digital data makes it easier for generative AI to analyze.

[0031] The improvement point identifying unit can suggest training to retract the shoulder blades when the shoulders are positioned forward. For example, the improvement point identifying unit suggests training to retract the shoulder blades when the shoulders are positioned forward. For example, it suggests exercises to strengthen the muscles around the shoulder blades. The improvement point identifying unit also suggests a specific training method to improve the stability of the knee joint. For example, it suggests exercises to strengthen the muscles around the knee. The improvement point identifying unit also suggests a specific training method to improve the flexibility of the ankles. For example, it suggests ankle stretches and balance exercises. In this way, by suggesting appropriate training when the shoulders are positioned forward, improvement in posture can be expected.

[0032] The video linking unit can automatically select appropriate training videos from the YouTube or Instagram platform and encourage the user to watch them. The video linking unit can, for example, automatically select appropriate training videos from the YouTube or Instagram platform and encourage the user to watch them. For example, it can prioritize specific exercise videos. The video linking unit can also analyze the user's training needs and select appropriate videos from SNS based on that analysis. For example, it can suggest videos that match the training method desired by the user. The video linking unit can also perform detailed analysis of videos collected from SNS and select videos that best suit the user's training needs. For example, it can prioritize videos that train specific muscles. This automatically selects appropriate training videos and encourages the user to watch them, thereby improving the effectiveness of training.

[0033] The goods suggestion unit can suggest related goods such as posture correction belts, training mats, and walking shoes. The goods suggestion unit suggests related goods such as posture correction belts, training mats, and walking shoes. For example, a posture correction belt provides support for pulling the shoulder blades together. A training mat provides cushioning when exercising. Walking shoes improve stability when walking. In this way, the related goods are suggested to improve the user's training environment.

[0034] The posture analysis unit uses AI-analyzed posture and gait data to perform a detailed analysis of the movements of individual muscles and joints, identifying subtle deviations in movement. For example, the posture analysis unit analyzes the movements of each muscle and joint in detail based on data collected by AI. For example, it measures shoulder blade movement and knee joint angle down to the millimeter to identify subtle deviations. The posture analysis unit also analyzes the user's walking data using AI to identify subtle deviations in ankle and knee movement. For example, it analyzes the angle of the foot when it lands and the movement of the center of gravity in detail. The posture analysis unit also analyzes the movements of the spine and pelvis in detail based on posture data to identify subtle deviations. For example, it measures the curvature of the spine and the tilt of the pelvis down to the millimeter. This allows for the identification of subtle deviations in movement, enabling more accurate improvements to posture and gait.

[0035] The posture analysis unit can compare data on the user's past posture and walking style to analyze long-term changes and trends in improvement. For example, the posture analysis unit uses AI to compare the user's past posture data with current data and analyze long-term changes. For example, it can identify trends in posture improvement based on data from the past few months. The posture analysis unit also uses AI to compare the user's walking data with past data and analyze trends in improvement. For example, it can identify changes in walking balance based on past data. The posture analysis unit also uses AI to collect data on the user's posture and walking style over the long term and analyze trends in improvement. For example, it can collect data regularly and monitor the progress of improvement. This makes it easier to understand long-term changes and trends in improvement by comparing with past data.

[0036] The posture analysis unit recreates the user's posture and walking style in a 3D model based on data analyzed by the AI, making it easier to understand visually. For example, the posture analysis unit recreates the user's posture in a 3D model based on data collected by the AI. For example, it displays the movement of the spine and joints in 3D graphics. The posture analysis unit also recreates the user's walking style in a 3D model based on walking data. For example, it displays the movement of the feet and the shift in the center of gravity in 3D animation. The posture analysis unit also recreates the posture and walking style data in a 3D model, making it easier for the user to understand visually. For example, it shows posture distortions and walking balance in 3D graphics. By recreating it in a 3D model, the user can visually understand their own posture and walking style more easily.

[0037] The posture analysis unit collects posture and walking style data by different age groups, gender, and occupation, and can propose the optimal improvement method for a specific group. For example, the posture analysis unit collects data on different age groups and proposes the optimal posture improvement method for each age group. For example, it proposes posture correction exercises for the elderly. The posture analysis unit also collects posture and walking style data by gender and proposes an improvement method that suits each gender. For example, it proposes pelvic correction exercises for women. The posture analysis unit also collects posture and walking style data by occupation and proposes an improvement method that suits each occupation. For example, it proposes exercises to relieve shoulder stiffness for people who do a lot of desk work. This allows it to cater to a wide range of users by proposing the optimal improvement method for each age group, gender, and occupation.

[0038] The improvement point identification unit is capable of generating a detailed exercise plan by suggesting specific training methods for individual muscles and joints based on the analysis results of the AI. For example, the improvement point identification unit suggests a specific training method for pulling the shoulder blades together based on the analysis results. For example, it suggests exercises to strengthen the muscles around the shoulder blades. Furthermore, the improvement point identification unit suggests a specific training method for improving knee joint stability based on the analysis results. For example, it suggests exercises to strengthen the muscles around the knee. Furthermore, the improvement point identification unit suggests a specific training method for improving ankle flexibility based on the analysis results. For example, it suggests ankle stretches and balance exercises. In this way, the AI ​​suggests specific training methods and generates a detailed exercise plan, thereby improving the training effect of the user.

[0039] The improvement point identification unit takes into account the user's lifestyle habits and daily movements and can suggest improvement methods that can be put into practice in daily life. For example, the improvement point identification unit uses AI to analyze the user's lifestyle habits and suggest posture improvement methods that can be put into practice in daily life. For example, it suggests simple stretches that can be done while doing desk work. The improvement point identification unit also uses AI to analyze the user's daily movements and suggest improvement methods that are easy to put into practice. For example, it suggests points to be aware of when walking and simple exercises. The improvement point identification unit also takes into account the user's lifestyle habits and suggests improvement methods that are easy to incorporate into daily life. For example, it suggests posture correction exercises that can be done while commuting. In this way, by suggesting improvement methods that can be put into practice in daily life, the user can continue to make improvements without any difficulty.

[0040] The improvement point identification unit can customize the training suggestions proposed by the AI ​​to suit the user's preferences and provide a plan that meets individual needs. In the improvement point identification unit, for example, the AI ​​analyzes the user's preferences and customizes the training suggestions. For example, it may prioritize suggesting exercises that the user likes. In addition, the improvement point identification unit customizes the training suggestions based on user feedback. For example, it may adjust the suggestions to avoid exercises that the user is not good at. In addition, the improvement point identification unit provides a training plan that meets the user's needs. For example, it may propose a plan that focuses on training specific muscles. In this way, by customizing the training suggestions to suit the user's preferences, it is possible to provide a plan that meets individual needs.

[0041] The improvement point identification unit can adjust the training suggestions according to different fitness levels and health conditions, making it possible to accommodate a wide range of users. In the improvement point identification unit, for example, AI analyzes the user's fitness level and suggests appropriate training suggestions. For example, it suggests exercises for beginners. In addition, the improvement point identification unit adjusts the training suggestions by taking the user's health condition into consideration. For example, it suggests exercises that do not put strain on the joints. In addition, the improvement point identification unit provides training plans according to different fitness levels and health conditions. For example, it suggests high-intensity training for advanced users. In this way, the training suggestions can be adjusted according to different fitness levels and health conditions, making it possible to accommodate a wide range of users.

[0042] The video linking unit can perform a detailed analysis of the content of videos collected by AI from SNS and select the video that best suits the user's training needs. For example, the video linking unit can analyze the content of videos collected by AI from SNS and select the video that best suits the user's training needs. For example, it can prioritize and suggest specific exercise videos. The video linking unit can also use AI to analyze the user's training needs and select appropriate videos from SNS based on that analysis. For example, it can suggest videos that match the training method desired by the user. The video linking unit can also perform a detailed analysis of videos collected by AI from SNS and select the video that best suits the user's training needs. For example, it can prioritize and suggest videos that train specific muscles. This supports effective training by selecting the video that best suits the user's training needs.

[0043] The video linking unit uses AI to learn the user's viewing history and preferences, and can provide an individually customized video list. For example, the video linking unit uses AI to analyze the user's viewing history and provide a video list customized based on the user's preferences. For example, it may suggest new videos based on the trends of videos viewed in the past. The video linking unit also uses AI to learn the user's preferences and provide an individually customized video list based on that. For example, it may preferentially suggest videos by specific trainers. The video linking unit also uses AI to learn the user's viewing history and preferences and provide an individually customized video list. For example, it may suggest videos based on the type of exercise the user prefers. This improves user satisfaction by providing a video list customized based on the user's viewing history and preferences.

[0044] The video linking unit can support effective learning by providing videos collected by AI from SNS in stages according to the user's training progress. For example, the video linking unit uses AI to analyze the user's training progress and provide videos collected from SNS in stages accordingly. For example, it sequentially suggests videos for beginners to advanced users. The video linking unit also provides videos at the appropriate time based on the user's training progress. For example, it suggests the next step video after completing a specific exercise. The video linking unit also supports effective learning by providing videos collected by AI from SNS in stages according to the user's training progress. For example, it customizes videos according to the training progress. This supports effective learning by providing videos in stages according to the training progress.

[0045] The video linking unit can integrate videos from different social media platforms to provide users with training methods from various perspectives. For example, the video linking unit uses AI to collect videos from different social media platforms, integrate them, and provide them to users. For example, it can combine videos from YouTube and Instagram into a single list. The video linking unit can also analyze videos collected from different social media platforms to provide training methods from various perspectives. For example, it can combine and suggest videos from different trainers. The video linking unit can also integrate videos from different social media platforms to provide users with training methods from various perspectives. For example, it can suggest different exercise variations. In this way, by integrating videos from different social media platforms, it is possible to provide training methods from various perspectives.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The posture improvement system can also be equipped with a nutritional analysis unit that analyzes the user's eating habits and makes suggestions for improving nutritional balance. For example, the system records the user's daily diet and uses AI to analyze whether there are any nutrient deficiencies or excesses. The nutritional analysis unit also suggests ingredients and recipes containing specific nutrients to maximize the user's training effects. For example, it suggests high-protein foods to promote muscle repair and foods containing omega-3 fatty acids to maintain joint health. This allows users to improve their health through both diet and training.

[0048] The posture analysis unit can also analyze the user's sleep patterns and make suggestions to improve the quality of their sleep. For example, the AI ​​can record the user's sleep duration and depth and analyze them. The sleep analysis unit also provides specific advice to improve the user's sleep environment. For example, it can suggest adjusting the temperature and humidity in the bedroom, or selecting appropriate bedding. This allows the user to ensure good quality sleep and enhance the effectiveness of posture improvement.

[0049] The posture improvement system can also include a stress analysis unit that analyzes the user's stress level and suggests relaxation methods. For example, the stress factors in the user's daily life are recorded and analyzed by AI. The stress analysis unit also suggests specific relaxation methods to reduce the user's stress. For example, it suggests relaxation exercises such as deep breathing, meditation, and yoga. This allows the user to reduce stress and improve their posture.

[0050] The posture improvement system can also be equipped with a motion analysis unit that analyzes the user's exercise history and proposes an optimal training plan based on past training data. For example, the type and frequency of training the user has done in the past can be recorded and analyzed by AI. The motion analysis unit can also propose an effective training plan based on the user's exercise history. For example, it can re-propose exercises that were effective in the past or suggest new training methods. This allows the user to utilize past data to conduct more effective training.

[0051] The posture improvement system can also be equipped with a lifestyle rhythm analysis unit that analyzes the user's lifestyle rhythm and suggests timing for posture improvement in daily life. For example, the user's wake-up time, bedtime, and meal timings are recorded and analyzed by AI. The lifestyle rhythm analysis unit also suggests timing for posture improvement that matches the user's lifestyle rhythm. For example, it suggests morning stretching, lunchtime exercises, and evening relaxation exercises. This allows the user to continue improving their posture without straining themselves in their daily lives.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The posture analysis unit analyzes an individual's posture and walking style. For example, it can record the individual's movements in real time using a smartphone camera or a dedicated wearable device. Based on the posture and walking style data analyzed by AI, it then performs a detailed analysis of the movements of individual muscles and joints to identify subtle deviations in movement. Step 2: The improvement point identification unit identifies areas for improvement based on the results of the analysis by the posture analysis unit. For example, if the shoulders are positioned forward, it will suggest training to pull the shoulder blades in. It also compares this with the user's past posture and walking style data to analyze long-term changes and trends in improvement. Step 3: The training suggestion unit proposes specific training plans based on the improvement points identified by the improvement point identification unit, such as exercises for pulling the shoulder blades together or exercises for improving the stability of the knee joint. Step 4: The video linking unit searches social media for videos related to the training plan proposed by the training plan suggestion unit and provides them. For example, it automatically selects appropriate training videos from platforms such as YouTube and Instagram and encourages users to watch them. Step 5: The goods suggestion unit suggests goods related to the training proposed by the training plan suggestion unit, such as posture correction belts, training mats, walking shoes, etc.

[0054] (Example 2) The posture improvement system according to an embodiment of the present invention uses AI to perform a detailed analysis of an individual's posture and walking style, identifies areas for improvement based on the results, and proposes specific training plans. Furthermore, the system links with related videos on social media to promote the sale of video lessons and related goods. This allows the posture improvement system to improve the user's posture and walking style, enabling them to live a healthier life. For example, it is expected to prevent stiff shoulders and back pain, and improve walking balance. Furthermore, related videos and video lessons on social media make it easier to maintain motivation for training. Furthermore, the sale of related goods can help create a better training environment for users.

[0055] A posture improvement system according to an embodiment includes a posture analysis unit, an improvement point identification unit, a training plan proposal unit, a video linking unit, and a product proposal unit. The posture analysis unit analyzes an individual's posture and gait. For example, the posture analysis unit records the individual's movements in real time using a smartphone camera or a dedicated wearable device. The posture analysis unit also performs detailed analysis of the movements of individual muscles and joints based on the posture and gait data analyzed by AI to identify subtle deviations in movement. The improvement point identification unit identifies improvements based on the results of the analysis by the posture analysis unit. For example, if the shoulders are positioned forward, the improvement point identification unit suggests training to retract the shoulder blades. The improvement point identification unit also compares the results with the user's past posture and gait data to analyze long-term changes and trends in improvement. The training plan proposal unit proposes specific training plans based on the improvements identified by the improvement point identification unit. For example, the training plan proposal unit proposes exercises to retract the shoulder blades or exercises to improve knee joint stability. The video linking unit searches social media for videos related to the training plans proposed by the training plan proposal unit and provides them. For example, the system automatically selects appropriate training videos from platforms such as YouTube and Instagram and encourages users to watch them. The goods suggestion unit suggests goods related to the training suggested by the training plan suggestion unit, such as posture correction belts, training mats, and walking shoes. As a result, the posture improvement system according to the embodiment analyzes the user's posture and walking style in detail, identifies areas for improvement, suggests specific training plans, and provides related videos and goods to support the user in improving their health.

[0056] The posture analysis unit can record an individual's movements in real time using a smartphone camera or a dedicated wearable device. For example, the posture analysis unit scans a handwritten answer sheet and saves it as image data. It then converts the image data into text data using OCR technology. The posture analysis unit can also photograph a handwritten answer sheet using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app automatically corrects the image and performs character recognition. The posture analysis unit can also write handwritten answers with a dedicated digital pen, which then converts the data into digital data in real time. For example, a sensor can detect the pen's movements and save them as text data. This conversion of handwritten answers into digital data makes it easier for generative AI to analyze.

[0057] The improvement point identifying unit can suggest training to retract the shoulder blades when the shoulders are positioned forward. For example, the improvement point identifying unit suggests training to retract the shoulder blades when the shoulders are positioned forward. For example, it suggests exercises to strengthen the muscles around the shoulder blades. The improvement point identifying unit also suggests a specific training method to improve the stability of the knee joint. For example, it suggests exercises to strengthen the muscles around the knee. The improvement point identifying unit also suggests a specific training method to improve the flexibility of the ankles. For example, it suggests ankle stretches and balance exercises. In this way, by suggesting appropriate training when the shoulders are positioned forward, improvement in posture can be expected.

[0058] The video linking unit can automatically select appropriate training videos from the YouTube or Instagram platform and encourage the user to watch them. The video linking unit can, for example, automatically select appropriate training videos from the YouTube or Instagram platform and encourage the user to watch them. For example, it can prioritize specific exercise videos. The video linking unit can also analyze the user's training needs and select appropriate videos from SNS based on that analysis. For example, it can suggest videos that match the training method desired by the user. The video linking unit can also perform detailed analysis of videos collected from SNS and select videos that best suit the user's training needs. For example, it can prioritize videos that train specific muscles. This automatically selects appropriate training videos and encourages the user to watch them, thereby improving the effectiveness of training.

[0059] The goods suggestion unit can suggest related goods such as posture correction belts, training mats, and walking shoes. The goods suggestion unit suggests related goods such as posture correction belts, training mats, and walking shoes. For example, a posture correction belt provides support for pulling the shoulder blades together. A training mat provides cushioning when exercising. Walking shoes improve stability when walking. In this way, the related goods are suggested to improve the user's training environment.

[0060] The posture analysis unit uses AI-analyzed posture and gait data to perform a detailed analysis of the movements of individual muscles and joints, identifying subtle deviations in movement. For example, the posture analysis unit analyzes the movements of each muscle and joint in detail based on data collected by AI. For example, it measures shoulder blade movement and knee joint angle down to the millimeter to identify subtle deviations. The posture analysis unit also analyzes the user's walking data using AI to identify subtle deviations in ankle and knee movement. For example, it analyzes the angle of the foot when it lands and the movement of the center of gravity in detail. The posture analysis unit also analyzes the movements of the spine and pelvis in detail based on posture data to identify subtle deviations. For example, it measures the curvature of the spine and the tilt of the pelvis down to the millimeter. This allows for the identification of subtle deviations in movement, enabling more accurate improvements to posture and gait.

[0061] The posture analysis unit can compare data on the user's past posture and walking style to analyze long-term changes and trends in improvement. For example, the posture analysis unit uses AI to compare the user's past posture data with current data and analyze long-term changes. For example, it can identify trends in posture improvement based on data from the past few months. The posture analysis unit also uses AI to compare the user's walking data with past data and analyze trends in improvement. For example, it can identify changes in walking balance based on past data. The posture analysis unit also uses AI to collect data on the user's posture and walking style over the long term and analyze trends in improvement. For example, it can collect data regularly and monitor the progress of improvement. This makes it easier to understand long-term changes and trends in improvement by comparing with past data.

[0062] The posture analysis unit uses the emotion estimation function to analyze the relationship between the user's emotional state and posture or walking style, and can identify the impact of stress or fatigue on posture. The posture analysis unit, for example, uses the emotion estimation function to analyze the relationship between the user's emotional state and posture. For example, it identifies a tendency for shoulders to be pushed forward when stress is high. The posture analysis unit also uses the emotion estimation function to analyze the relationship between the user's emotional state and walking style. For example, it identifies a tendency for walking balance to be disrupted when fatigue accumulates. The posture analysis unit also uses the emotion estimation function to integrate the user's emotional state with posture and walking style data and identify the impact of stress or fatigue on posture. For example, it compares the emotion score with posture data. In this way, by analyzing the relationship between the emotional state and posture or walking style, it is possible to understand the impact of stress or fatigue on posture.

[0063] The posture analysis unit recreates the user's posture and walking style in a 3D model based on data analyzed by the AI, making it easier to understand visually. For example, the posture analysis unit recreates the user's posture in a 3D model based on data collected by the AI. For example, it displays the movement of the spine and joints in 3D graphics. The posture analysis unit also recreates the user's walking style in a 3D model based on walking data. For example, it displays the movement of the feet and the shift in the center of gravity in 3D animation. The posture analysis unit also recreates the posture and walking style data in a 3D model, making it easier for the user to understand visually. For example, it shows posture distortions and walking balance in 3D graphics. By recreating it in a 3D model, the user can visually understand their own posture and walking style more easily.

[0064] The posture analysis unit collects posture and walking style data by different age groups, gender, and occupation, and can propose the optimal improvement method for a specific group. For example, the posture analysis unit collects data on different age groups and proposes the optimal posture improvement method for each age group. For example, it proposes posture correction exercises for the elderly. The posture analysis unit also collects posture and walking style data by gender and proposes an improvement method that suits each gender. For example, it proposes pelvic correction exercises for women. The posture analysis unit also collects posture and walking style data by occupation and proposes an improvement method that suits each occupation. For example, it proposes exercises to relieve shoulder stiffness for people who do a lot of desk work. This allows it to cater to a wide range of users by proposing the optimal improvement method for each age group, gender, and occupation.

[0065] The posture analysis unit can use the emotion estimation function to monitor in real time the emotions of the user when assuming a specific posture or walking style, and suggest postures that elicit positive emotions. The posture analysis unit, for example, uses the emotion estimation function to monitor in real time the emotions of the user when assuming a specific posture. For example, it determines that assuming a relaxed posture increases positive emotions. The posture analysis unit also uses the emotion estimation function to monitor in real time the emotions of the user when assuming a specific walking style. For example, it determines that walking with a brisk pace increases positive emotions. The posture analysis unit also uses the emotion estimation function to suggest postures and walking styles that elicit positive emotions in the user. For example, it suggests optimal postures and walking styles based on the emotion score. In this way, the user's motivation is improved by being suggested postures that elicit positive emotions.

[0066] The improvement point identification unit is capable of generating a detailed exercise plan by suggesting specific training methods for individual muscles and joints based on the analysis results of the AI. For example, the improvement point identification unit suggests a specific training method for pulling the shoulder blades together based on the analysis results. For example, it suggests exercises to strengthen the muscles around the shoulder blades. Furthermore, the improvement point identification unit suggests a specific training method for improving knee joint stability based on the analysis results. For example, it suggests exercises to strengthen the muscles around the knee. Furthermore, the improvement point identification unit suggests a specific training method for improving ankle flexibility based on the analysis results. For example, it suggests ankle stretches and balance exercises. In this way, the AI ​​suggests specific training methods and generates a detailed exercise plan, thereby improving the training effect of the user.

[0067] The improvement point identification unit takes into account the user's lifestyle habits and daily movements and can suggest improvement methods that can be put into practice in daily life. For example, the improvement point identification unit uses AI to analyze the user's lifestyle habits and suggest posture improvement methods that can be put into practice in daily life. For example, it suggests simple stretches that can be done while doing desk work. The improvement point identification unit also uses AI to analyze the user's daily movements and suggest improvement methods that are easy to put into practice. For example, it suggests points to be aware of when walking and simple exercises. The improvement point identification unit also takes into account the user's lifestyle habits and suggests improvement methods that are easy to incorporate into daily life. For example, it suggests posture correction exercises that can be done while commuting. In this way, by suggesting improvement methods that can be put into practice in daily life, the user can continue to make improvements without any difficulty.

[0068] The improvement point identification unit can use the emotion estimation function to monitor the emotional state of the user when training and provide feedback to maintain motivation. The improvement point identification unit, for example, uses the emotion estimation function to monitor the emotional state of the user when training in real time. For example, it analyzes the emotion score during training. The improvement point identification unit also uses the emotion estimation function to provide feedback according to the user's emotional state. For example, it displays an encouraging message when motivation drops. The improvement point identification unit also uses the emotion estimation function to adjust the progress of training based on the user's emotional state. For example, it suggests increasing the intensity of training when positive emotions are strong. In this way, monitoring the emotional state and providing feedback to maintain motivation makes it easier for the user to continue training.

[0069] The improvement point identification unit can customize the training suggestions proposed by the AI ​​to suit the user's preferences and provide a plan that meets individual needs. In the improvement point identification unit, for example, the AI ​​analyzes the user's preferences and customizes the training suggestions. For example, it may prioritize suggesting exercises that the user likes. In addition, the improvement point identification unit customizes the training suggestions based on user feedback. For example, it may adjust the suggestions to avoid exercises that the user is not good at. In addition, the improvement point identification unit provides a training plan that meets the user's needs. For example, it may propose a plan that focuses on training specific muscles. In this way, by customizing the training suggestions to suit the user's preferences, it is possible to provide a plan that meets individual needs.

[0070] The improvement point identification unit can adjust the training suggestions according to different fitness levels and health conditions, making it possible to accommodate a wide range of users. In the improvement point identification unit, for example, AI analyzes the user's fitness level and suggests appropriate training suggestions. For example, it suggests exercises for beginners. In addition, the improvement point identification unit adjusts the training suggestions by taking the user's health condition into consideration. For example, it suggests exercises that do not put strain on the joints. In addition, the improvement point identification unit provides training plans according to different fitness levels and health conditions. For example, it suggests high-intensity training for advanced users. In this way, the training suggestions can be adjusted according to different fitness levels and health conditions, making it possible to accommodate a wide range of users.

[0071] The improvement point identification unit can use the emotion estimation function to analyze the emotions the user feels when training and suggest a training method that elicits positive emotions. The improvement point identification unit, for example, uses the emotion estimation function to analyze the emotions the user feels when training in real time. For example, it analyzes the emotion score during training. The improvement point identification unit also uses the emotion estimation function to suggest a training method that corresponds to the user's emotional state. For example, it preferentially suggests exercises that evoke strong positive emotions. The improvement point identification unit also uses the emotion estimation function to suggest a training method that elicits the user's emotions. For example, it suggests optimal exercises based on the emotion score. In this way, suggesting a training method that elicits positive emotions improves the user's motivation.

[0072] The video linking unit can perform a detailed analysis of the content of videos collected by AI from SNS and select the video that best suits the user's training needs. For example, the video linking unit can analyze the content of videos collected by AI from SNS and select the video that best suits the user's training needs. For example, it can prioritize and suggest specific exercise videos. The video linking unit can also use AI to analyze the user's training needs and select appropriate videos from SNS based on that analysis. For example, it can suggest videos that match the training method desired by the user. The video linking unit can also perform a detailed analysis of videos collected by AI from SNS and select the video that best suits the user's training needs. For example, it can prioritize and suggest videos that train specific muscles. This supports effective training by selecting the video that best suits the user's training needs.

[0073] The video linking unit uses AI to learn the user's viewing history and preferences, and can provide an individually customized video list. For example, the video linking unit uses AI to analyze the user's viewing history and provide a video list customized based on the user's preferences. For example, it may suggest new videos based on the trends of videos viewed in the past. The video linking unit also uses AI to learn the user's preferences and provide an individually customized video list based on that. For example, it may preferentially suggest videos by specific trainers. The video linking unit also uses AI to learn the user's viewing history and preferences and provide an individually customized video list. For example, it may suggest videos based on the type of exercise the user prefers. This improves user satisfaction by providing a video list customized based on the user's viewing history and preferences.

[0074] The video linking unit can use the emotion estimation function to monitor the emotions of the user when watching videos and preferentially suggest videos that elicit positive emotions. The video linking unit, for example, uses the emotion estimation function to monitor the emotions of the user when watching videos in real time. For example, it analyzes the emotion score during viewing. The video linking unit also uses the emotion estimation function to preferentially suggest videos that correspond to the user's emotional state. For example, it preferentially displays videos with strong positive emotions. The video linking unit also uses the emotion estimation function to suggest videos that elicit the user's emotions. For example, it selects the optimal video based on the emotion score. This improves the user's viewing experience by preferentially suggesting videos that elicit positive emotions.

[0075] The video linking unit can support effective learning by providing videos collected by AI from SNS in stages according to the user's training progress. For example, the video linking unit uses AI to analyze the user's training progress and provide videos collected from SNS in stages accordingly. For example, it sequentially suggests videos for beginners to advanced users. The video linking unit also provides videos at the appropriate time based on the user's training progress. For example, it suggests the next step video after completing a specific exercise. The video linking unit also supports effective learning by providing videos collected by AI from SNS in stages according to the user's training progress. For example, it customizes videos according to the training progress. This supports effective learning by providing videos in stages according to the training progress.

[0076] The video linking unit can integrate videos from different social media platforms to provide users with training methods from various perspectives. For example, the video linking unit uses AI to collect videos from different social media platforms, integrate them, and provide them to users. For example, it can combine videos from YouTube and Instagram into a single list. The video linking unit can also analyze videos collected from different social media platforms to provide training methods from various perspectives. For example, it can combine and suggest videos from different trainers. The video linking unit can also integrate videos from different social media platforms to provide users with training methods from various perspectives. For example, it can suggest different exercise variations. In this way, by integrating videos from different social media platforms, it is possible to provide training methods from various perspectives.

[0077] The video linking unit can use the emotion estimation function to analyze the emotions of the user when watching a video in real time and optimize the viewing experience. The video linking unit, for example, uses the emotion estimation function to analyze the emotions of the user when watching a video in real time. For example, it analyzes the emotion score during viewing and optimizes the viewing experience. The video linking unit also uses the emotion estimation function to provide feedback according to the user's emotional state. For example, it suggests related videos when strong positive emotions are felt during viewing. The video linking unit also uses the emotion estimation function to analyze the user's emotions in real time and optimize the viewing experience. For example, it selects the optimal video based on the emotion score and improves the viewing experience. In this way, emotions are analyzed in real time and the viewing experience is optimized, thereby improving user satisfaction.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The posture improvement system can also be equipped with a nutritional analysis unit that analyzes the user's eating habits and makes suggestions for improving nutritional balance. For example, the system records the user's daily diet and uses AI to analyze whether there are any nutrient deficiencies or excesses. The nutritional analysis unit also suggests ingredients and recipes containing specific nutrients to maximize the user's training effects. For example, it suggests high-protein foods to promote muscle repair and foods containing omega-3 fatty acids to maintain joint health. This allows users to improve their health through both diet and training.

[0080] The posture analysis unit can also analyze the user's sleep patterns and make suggestions to improve the quality of their sleep. For example, the AI ​​can record the user's sleep duration and depth and analyze them. The sleep analysis unit also provides specific advice to improve the user's sleep environment. For example, it can suggest adjusting the temperature and humidity in the bedroom, or selecting appropriate bedding. This allows the user to ensure good quality sleep and enhance the effectiveness of posture improvement.

[0081] The posture improvement system can also include a stress analysis unit that analyzes the user's stress level and suggests relaxation methods. For example, the stress factors in the user's daily life are recorded and analyzed by AI. The stress analysis unit also suggests specific relaxation methods to reduce the user's stress. For example, it suggests relaxation exercises such as deep breathing, meditation, and yoga. This allows the user to reduce stress and improve their posture.

[0082] The posture improvement system can also be equipped with a motion analysis unit that analyzes the user's exercise history and proposes an optimal training plan based on past training data. For example, the type and frequency of training the user has done in the past can be recorded and analyzed by AI. The motion analysis unit can also propose an effective training plan based on the user's exercise history. For example, it can re-propose exercises that were effective in the past or suggest new training methods. This allows the user to utilize past data to conduct more effective training.

[0083] The posture improvement system can also be equipped with a lifestyle rhythm analysis unit that analyzes the user's lifestyle rhythm and suggests timing for posture improvement in daily life. For example, the user's wake-up time, bedtime, and meal timings are recorded and analyzed by AI. The lifestyle rhythm analysis unit also suggests timing for posture improvement that matches the user's lifestyle rhythm. For example, it suggests morning stretching, lunchtime exercises, and evening relaxation exercises. This allows the user to continue improving their posture without straining themselves in their daily lives.

[0084] The posture analysis unit uses the emotion estimation function to analyze the relationship between the user's emotional state and posture or walking style, and can identify the impact of stress or fatigue on posture. For example, the emotion estimation function is used to analyze the relationship between the user's emotional state and posture. For example, it is possible to identify a tendency for shoulders to be pushed forward when stress is high. The posture analysis unit also uses the emotion estimation function to analyze the relationship between the user's emotional state and walking style. For example, it is possible to identify a tendency for walking balance to be disrupted when fatigue accumulates. The posture analysis unit also uses the emotion estimation function to integrate the user's emotional state with data on posture and walking style and identify the impact of stress or fatigue on posture. For example, it compares the emotion score with posture data. In this way, the impact of stress or fatigue on posture can be understood by analyzing the relationship between the emotional state and posture or walking style.

[0085] The posture improvement system can also be equipped with an emotion analysis unit that analyzes the user's emotional state and suggests training methods according to the emotion. For example, the user's emotional state can be monitored in real time and analyzed by AI. The emotion analysis unit can also suggest training methods according to the user's emotional state. For example, relaxation exercises can be suggested when stress levels are high, and high-intensity training can be suggested when positive emotions are strong. This allows the user to perform optimal training according to their emotional state.

[0086] The posture improvement system can also include an emotional feedback unit that analyzes the user's emotional state and provides feedback according to the emotion. For example, the user's emotional state can be monitored in real time and analyzed by AI. The emotional feedback unit also provides feedback according to the user's emotional state. For example, it can provide advice on how to relax when stress is high, or display an encouraging message when positive emotions are strong. This allows the user to receive appropriate feedback according to their emotional state.

[0087] The posture improvement system can also be equipped with an emotional training unit that analyzes the user's emotional state and provides a training plan based on that emotion. For example, the user's emotional state can be monitored in real time and analyzed by AI. The emotional training unit can also provide a training plan based on the user's emotional state. For example, relaxation exercises can be suggested when stress levels are high, and high-intensity training can be suggested when positive emotions are strong. This allows the user to receive the optimal training plan based on their emotional state.

[0088] The posture improvement system can also be equipped with an emotional environment unit that analyzes the user's emotional state and provides a training environment that corresponds to that emotion. For example, the user's emotional state can be monitored in real time and analyzed by AI. The emotional environment unit can also provide a training environment that corresponds to the user's emotional state. For example, it can suggest a relaxing environment when stress levels are high, and an energetic environment when positive emotions are strong. This allows the user to create the optimal training environment according to their emotional state.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The posture analysis unit analyzes an individual's posture and walking style. For example, it can record the individual's movements in real time using a smartphone camera or a dedicated wearable device. Based on the posture and walking style data analyzed by AI, it then performs a detailed analysis of the movements of individual muscles and joints to identify subtle deviations in movement. Step 2: The improvement point identification unit identifies areas for improvement based on the results of the analysis by the posture analysis unit. For example, if the shoulders are positioned forward, it will suggest training to pull the shoulder blades in. It also compares this with the user's past posture and walking style data to analyze long-term changes and trends in improvement. Step 3: The training suggestion unit proposes specific training plans based on the improvement points identified by the improvement point identification unit, such as exercises for pulling the shoulder blades together or exercises for improving the stability of the knee joint. Step 4: The video linking unit searches social media for videos related to the training plan proposed by the training plan suggestion unit and provides them. For example, it automatically selects appropriate training videos from platforms such as YouTube and Instagram and encourages users to watch them. Step 5: The goods suggestion unit suggests goods related to the training proposed by the training plan suggestion unit, such as posture correction belts, training mats, walking shoes, etc.

[0091] 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.

[0092] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0135] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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, in order to avoid confusion and to 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.

[0157] 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. [Explanation of symbols]

[0158] 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 posture analysis section that analyzes an individual's posture and walking style, an improvement point identifying unit that identifies an improvement point based on the analysis result by the posture analysis unit; a training plan suggesting unit that proposes specific training plans based on the improvement points identified by the improvement point identifying unit; a video linking unit that searches for and provides videos related to the training plan proposed by the training plan proposing unit from an SNS; a goods suggestion unit that suggests goods related to the training proposed by the training plan suggestion unit. A system characterized by:

2. The posture analysis unit Based on the data analyzed by the AI, the user's posture and walking style are reproduced in the 3D model, making it easier to understand visually.

2. The system of claim 1.

3. The improvement point identifying unit Based on the analysis results, the AI ​​proposes specific training methods for each muscle and joint and generates a detailed exercise plan.

2. The system of claim 1.

4. The video linking unit The AI ​​analyzes the content of the videos collected from the SNS in detail and selects the videos that best suit the user's training needs.

2. The system of claim 1.

5. The posture analysis unit Analyzing the relationship between the user's emotional state and the posture and walking style to identify the effects of stress and fatigue on the posture.

2. The system of claim 1.

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