system

Abstract

The system according to the embodiment aims to provide individual training suggestions and feedback based on the user's body type. [Solution] A system according to an embodiment includes a photographing unit, an input unit, a suggestion unit, an analysis unit, a form suggestion unit, and a feedback unit. The photographing unit photographs the user's body shape. The input unit inputs the body part or goal to be improved based on the body shape information photographed by the photographing unit. The suggestion unit suggests appropriate stretches or muscle training exercises based on the information input by the input unit. The analysis unit analyzes the exercise video suggested by the suggestion unit. The form suggestion unit suggests correct form or alternative movements based on the results of the analysis by the analysis unit. The feedback unit periodically photographs the movements suggested by the form suggestion unit and provides feedback.

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 technology has the drawback of making it difficult to provide individual training suggestions and feedback based on a user's body type.

[0005] The system according to the embodiment aims to provide individual training suggestions and feedback based on the user's body type. [Means for solving the problem]

[0006] The system according to the embodiment includes a photographing unit, an input unit, a suggestion unit, an analysis unit, a form suggestion unit, and a feedback unit. The photographing unit photographs the user's body shape. The input unit inputs the body part or goal to be improved based on the body shape information photographed by the photographing unit. The suggestion unit suggests appropriate stretches or muscle training based on the information input by the input unit. The analysis unit analyzes the exercise video suggested by the suggestion unit. The form suggestion unit suggests correct form or another movement based on the results of the analysis by the analysis unit. The feedback unit periodically photographs the movement suggested by the form suggestion unit and provides feedback. [Effects of the Invention]

[0007] In accordance with an embodiment, the system can provide personalized training suggestions and feedback based on the user's body type. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A personal training system according to an embodiment of the present invention provides personalized training tailored to individual needs based on a user's physique information, supporting dieting and building an ideal physique. The personal training system captures the user's physique, inputs the areas of improvement and goals, and a generation AI suggests appropriate stretches and muscle training exercises. The user films an actual exercise video, and the generation AI analyzes the video. Based on the analysis results, the system suggests correct form and alternative movements, and provides appropriate feedback by periodically filming the user's physique. For example, in a personal training system, a user films their physique and inputs the areas of improvement and goals. For example, the user inputs goals such as "I want to tighten my stomach" or "I want to strengthen my arms." This information is input into the generation AI. The generation AI then analyzes the input information and suggests appropriate stretches and muscle training exercises. The generation AI generates an optimal exercise plan based on the user's physique and goals. For example, the system suggests specific exercises such as "stretches to tighten my stomach" and "muscle training to strengthen my arms." The user films an actual exercise video, and the generation AI analyzes the video. The generation AI analyzes video footage of the user's exercises and suggests correct form or alternative movements. For example, when a user performs squats, the generation AI analyzes the user's form and suggests correct form and areas for improvement. Furthermore, by regularly taking photos of their body shape, the generation AI provides appropriate feedback. Users regularly take photos of their body shape and input the videos into the generation AI. The generation AI compares the images with past data and provides feedback on progress and areas for improvement. For example, it provides specific feedback such as "Your stomach is getting tighter" or "Your arm muscles are getting stronger." This allows the personal training system to provide personalized training tailored to the user's individual needs and support dieting and building their ideal physique. This allows the personal training system to provide personalized training tailored to the user's individual needs and support dieting and building their ideal physique based on the user's body shape information. For example, users can receive an exercise plan tailored to their body shape and goals, allowing them to exercise with the correct form.In addition, regular feedback helps you maintain motivation and progress with your training effectively.

[0029] A personal training system according to an embodiment includes a camera, an input unit, a suggestion unit, an analysis unit, a form suggestion unit, and a feedback unit. The camera captures a user's body shape. The user's body shape may include, but is not limited to, height, weight, body fat percentage, and body part dimensions. The camera captures the user's body shape using, for example, a smartphone camera. The camera can also capture the user's body shape in detail using 3D scanning technology. For example, the camera can capture the user's body shape as a 3D model, enabling detailed analysis. The input unit inputs the body part or goal the user wants to improve. For example, the user may input a goal such as "I want to tighten my stomach" or "I want to strengthen my arms." The input unit can input the goal using, for example, text input or voice input. The input unit can also estimate the user's emotions and customize the input interface based on the estimated user's emotions. For example, if the user is relaxed, detailed input options are provided and a customizable input method is suggested. The suggestion unit uses a generation AI to generate an optimal exercise plan based on the user's body type and goals. For example, the generation AI analyzes the user's body type information and suggests specific exercises such as "stretches to tighten the stomach" and "muscle training to strengthen the arms." The suggestion unit can also estimate the user's emotions and adjust the way the suggestions are presented based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit provides suggestions with detailed explanations. The analysis unit uses a generation AI to analyze video of the user's exercise. For example, if the user performs squats, the generation AI analyzes the form and suggests correct form and areas for improvement. The analysis unit can also analyze the user's exercise data in real time and provide immediate feedback. For example, it analyzes the user's exercise data in real time and provides effective feedback. The form suggestion unit suggests correct form or alternative movements based on the analysis results. For example, the generation AI analyzes the user's squat form and suggests correct form and areas for improvement. The form suggestion unit can also estimate the user's emotions and adjust the way the form suggestions are presented based on the estimated user's emotions.For example, if the user is relaxed, the feedback unit provides form suggestions with detailed explanations. The feedback unit periodically photographs the user's body shape and provides feedback on progress and areas for improvement. For example, the user periodically photographs their body shape and inputs the footage into the generation AI. The generation AI compares the footage with past data and provides specific feedback such as "your stomach is getting tighter" or "your arm muscles are getting stronger." The feedback unit can also estimate the user's emotions and adjust the feedback method based on the estimated user's emotions. For example, if the user is relaxed, the feedback unit provides detailed feedback. As a result, the personal training system according to the embodiment can provide personal training tailored to the user's individual needs based on the user's body shape information, supporting them in dieting and building their ideal body.

[0030] The personal training system further includes a photographing unit that analyzes the user's past body shape data and suggests the optimal photographing angle and distance. The photographing unit analyzes the user's past body shape data and suggests the optimal photographing angle and distance. For example, the photographing unit takes photographs at the most effective angle based on the user's past body shape data. The photographing unit can also take photographs at an appropriate distance taking into account changes in the user's body shape. The photographing unit can also analyze the user's body shape data and suggest a photographing angle that emphasizes specific body parts. This allows for effective photographing by suggesting optimal photographing conditions based on the past body shape data. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input past body shape data into a generation AI and have the generation AI suggest the optimal photographing angle and distance.

[0031] Furthermore, the personal training system includes a photographing unit that displays changes in the user's body shape in real time during photography, thereby improving the accuracy of photography. The photographing unit displays changes in the user's body shape in real time during photography, thereby improving the accuracy of photography. For example, the photographing unit may display changes in the user's body shape in real time during photography, thereby enabling accurate photography. Furthermore, if the user moves, the changes in body shape can be displayed in real time, allowing photography to be performed at the appropriate timing. Furthermore, the photographing unit may display changes in the user's body shape during photography, and adjust the photography angle and distance as necessary. This allows accurate photography by displaying changes in body shape in real time. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without AI. For example, the photographing unit may input body shape data acquired in real time into a generation AI, causing the generation AI to execute a process to display changes in body shape.

[0032] Furthermore, the personal training system includes a camera unit that captures the user's body shape as a 3D model during photography, enabling detailed analysis. The camera unit captures the user's body shape as a 3D model during photography, enabling detailed analysis. For example, the camera unit captures the user's body shape as a 3D model and performs detailed analysis. Furthermore, the 3D model can be used to perform detailed analysis of changes in the user's body shape. Furthermore, the 3D model can be used to visually display changes in the user's body shape. This enables detailed body shape analysis using the 3D model. Some or all of the above-described processing in the camera unit may be performed using, for example, AI, or may be performed without AI. For example, the camera unit can input the acquired body shape data into a generation AI, causing the generation AI to generate and analyze a 3D model.

[0033] The personal training system further includes an input unit that analyzes the user's past input data and suggests the optimal input method. The input unit analyzes the user's past input data and suggests the optimal input method. For example, the input unit automatically displays as candidates targets and body parts that the user has frequently input in the past. The input unit can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. The system can also predict and suggest targets and body parts to use in a specific time period based on the user's past input data. This improves input efficiency by suggesting the optimal input method based on the past input data. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input the past input data into a generation AI and have the generation AI suggest the optimal input method.

[0034] The personal training system further includes an input unit that analyzes the user's voice input during input and combines it with text input to improve accuracy. The input unit analyzes the user's voice input during input and combines it with text input to improve accuracy. For example, the input unit converts the user's voice input into text to improve input accuracy. Combining voice input and text input can also achieve more accurate input. Furthermore, the user's voice input can be analyzed and erroneous input can be automatically corrected. Combining voice input and text input improves input accuracy. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input voice data to a generation AI and have the generation AI perform text conversion and accuracy improvement processing.

[0035] Furthermore, the personal training system includes an input unit that recognizes a user's gestures during input and provides an intuitive input method. The input unit recognizes a user's gestures during input and provides an intuitive input method. For example, a user can easily set a target or body part by performing a specific gesture. A more intuitive input method can also be provided by combining gesture input and voice input. The system can also recognize a user's gestures and automatically complete the input content. This enables intuitive input using gesture input. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input gesture data to a generation AI and have the generation AI perform gesture recognition and input completion processing.

[0036] The personal training system further includes a suggestion unit that analyzes the user's body shape data in real time at the time of suggestion and provides an optimal exercise plan. The suggestion unit uses a generation AI to analyze the user's body shape data in real time at the time of suggestion and provides an optimal exercise plan. For example, the suggestion unit may analyze the user's body shape data in real time and suggest optimal stretching and muscle training. It may also be possible to provide an exercise plan tailored to the user's goals based on the real-time body shape data. It may also be possible to analyze the user's body shape data in real time and suggest an effective exercise plan. This allows for the provision of an optimal exercise plan by analyzing body shape data in real time. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input body shape data acquired in real time into the generation AI and cause the generation AI to generate an exercise plan.

[0037] The personal training system further includes a suggestion unit that analyzes the user's past exercise history at the time of suggestion and generates an individually customized exercise plan. The suggestion unit uses a generation AI to analyze the user's past exercise history at the time of suggestion and generate an individually customized exercise plan. For example, the suggestion unit analyzes the user's past exercise history and proposes an effective exercise plan. The system can also generate an optimal exercise plan based on the results of the user's past exercises. The system can also provide an individually customized exercise plan taking the user's past exercise history into consideration. This enables effective training by providing a customized exercise plan based on the user's past exercise history. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past exercise history data into the generation AI and cause the generation AI to generate a customized exercise plan.

[0038] Furthermore, the personal training system includes a suggestion unit that suggests an optimal exercise time, taking into account the user's lifestyle rhythm when making a suggestion. The suggestion unit uses the generation AI to suggest an optimal exercise time, taking into account the user's lifestyle rhythm when making a suggestion. For example, the suggestion unit analyzes the user's lifestyle rhythm and suggests an optimal exercise time. It can also suggest an effective exercise time that matches the user's lifestyle rhythm. It can also suggest a reasonable exercise time, taking into account the user's lifestyle rhythm. This allows for a reasonable training by suggesting an exercise time that matches the user's lifestyle rhythm. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input lifestyle rhythm data into the generation AI and have the generation AI suggest an optimal exercise time.

[0039] Furthermore, the personal training system includes an analysis unit that compares the user's body shape data with past data during analysis and analyzes the progress in detail. The analysis unit uses the generation AI to compare the user's body shape data with past data during analysis and analyzes the progress in detail. For example, the analysis unit compares the user's body shape data with past data and analyzes the progress in detail. It is also possible to analyze changes in the user's body shape in detail based on the past data. It is also possible to compare the user's body shape data with past data and analyze areas for improvement in detail. This allows the progress to be understood in detail by comparing with past data. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit may input past body shape data into the generation AI and have the generation AI analyze the progress.

[0040] The personal training system further includes an analysis unit that analyzes the user's exercise data in real time during analysis and provides immediate feedback. The analysis unit uses a generation AI to analyze the user's exercise data in real time during analysis and provides immediate feedback. For example, the analysis unit may analyze the user's exercise data in real time and provide immediate feedback. Effective feedback may also be provided based on the real-time exercise data. The analysis unit may also analyze the user's exercise data in real time and provide immediate feedback on areas for improvement. This allows for immediate feedback by analyzing the exercise data in real time. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input exercise data acquired in real time to the generation AI and cause the generation AI to provide immediate feedback.

[0041] Furthermore, the personal training system includes an analysis unit that analyzes the user's body shape data as a 3D model during analysis and provides detailed feedback. The analysis unit uses a generation AI to analyze the user's body shape data as a 3D model during analysis and provides detailed feedback. For example, the analysis unit analyzes the user's body shape data as a 3D model and provides detailed feedback. It is also possible to analyze changes in the user's body shape in detail based on the 3D model. It is also possible to analyze the user's body shape data as a 3D model and provide visual feedback. This enables detailed feedback using the 3D model. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the acquired body shape data into the generation AI and cause the generation AI to generate and analyze a 3D model.

[0042] Furthermore, the personal training system includes a form suggestion unit that analyzes the user's body shape data in real time when proposing a form and proposes an optimal form. The form suggestion unit uses a generation AI to analyze the user's body shape data in real time when proposing a form and proposes an optimal form. For example, the form suggestion unit analyzes the user's body shape data in real time and proposes an optimal form. It is also possible to propose a form that matches the user's goals based on the real-time body shape data. It is also possible to analyze the user's body shape data in real time and propose an effective form. In this way, it is possible to propose an optimal form by analyzing body shape data in real time. Some or all of the above-described processing in the form suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the form suggestion unit may input body shape data acquired in real time into the generation AI and have the generation AI execute form suggestions.

[0043] The personal training system further includes a form suggestion unit that analyzes the user's past exercise data when proposing a form and proposes an individually customized form. The form suggestion unit uses a generation AI to analyze the user's past exercise data when proposing a form and proposes an individually customized form. For example, the form suggestion unit analyzes the user's past exercise data and proposes an effective form. The system can also propose an optimal form based on the results of the user's past exercises. The system can also propose an individually customized form taking the user's past exercise data into consideration. This enables effective training by proposing a customized form based on past exercise data. Some or all of the above-described processing in the form suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the form suggestion unit can input past exercise data into the generation AI and have the generation AI propose a customized form.

[0044] Furthermore, the personal training system includes a form suggestion unit that considers the user's lifestyle when proposing a form and proposes an optimal form. The form suggestion unit uses a generation AI to consider the user's lifestyle when proposing a form and proposes an optimal form. For example, it analyzes the user's lifestyle and proposes an optimal form. It is also possible to propose an effective form that matches the user's lifestyle. It is also possible to consider the user's lifestyle and propose a reasonable form. In this way, by proposing a form that matches the user's lifestyle, a reasonable training is possible. Some or all of the above-described processing in the form suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the form suggestion unit may input lifestyle data into the generation AI and have the generation AI execute the proposal of an optimal form.

[0045] Furthermore, the personal training system includes a feedback unit that compares the user's body shape data with past data at the time of feedback and provides detailed feedback on the progress. The feedback unit uses the generation AI to compare the user's body shape data with past data at the time of feedback and provides detailed feedback on the progress. For example, the feedback unit compares the user's body shape data with past data and provides detailed feedback on the progress. It is also possible to provide detailed feedback on changes in the user's body shape based on past data. It is also possible to compare the user's body shape data with past data and provide detailed feedback on areas for improvement. This allows the progress to be understood in detail by comparing with past data. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit may input past body shape data into the generation AI and cause the generation AI to provide feedback on the progress.

[0046] The personal training system further includes a feedback unit that analyzes the user's exercise data in real time at the time of feedback and provides immediate feedback. The feedback unit uses a generation AI to analyze the user's exercise data in real time at the time of feedback and provides immediate feedback. For example, the feedback unit may analyze the user's exercise data in real time and provide immediate feedback. Effective feedback may also be provided based on the real-time exercise data. The user's exercise data may also be analyzed in real time and immediate feedback on areas for improvement may be provided. This allows for immediate feedback to be provided by analyzing the exercise data in real time. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input exercise data acquired in real time to the generation AI and cause the generation AI to provide immediate feedback.

[0047] The personal training system further includes a feedback unit that analyzes the user's body shape data as a 3D model and provides detailed feedback when providing feedback. The feedback unit uses a generation AI to analyze the user's body shape data as a 3D model and provide detailed feedback when providing feedback. For example, the feedback unit may analyze the user's body shape data as a 3D model and provide detailed feedback. Detailed feedback of changes in the user's body shape can also be provided based on the 3D model. The user's body shape data can also be analyzed as a 3D model and visual feedback can be provided. Using a 3D model enables detailed feedback. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input the acquired body shape data into the generation AI and cause the generation AI to generate and analyze a 3D model.

[0048] Furthermore, the personal training system includes a feedback unit that optimizes feedback by taking into account the user's environmental data when providing feedback. The feedback unit optimizes feedback by taking into account the user's environmental data using a generation AI. For example, optimal feedback is provided based on the user's environmental data. Effective feedback can also be provided by taking into account the user's environmental data. Highly visible feedback can also be provided based on the user's environmental data. This allows optimal feedback to be provided by taking into account the environmental data. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input environmental data into the generation AI and cause the generation AI to optimize the feedback.

[0049] Furthermore, the personal training system includes a feedback unit that adjusts the feedback method by reflecting the user's past feedback when providing feedback. The feedback unit uses a generation AI to adjust the feedback method by reflecting the user's past feedback when providing feedback. For example, the feedback unit provides an optimal feedback method based on the user's past feedback. It can also provide a feedback method that resolves problems previously pointed out by the user. It can also improve the feedback method by reflecting the user's past feedback. In this way, the feedback method can be improved by reflecting past feedback. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input past feedback data into the generation AI and have the generation AI adjust the feedback method.

[0050] The personal training system further includes a feedback unit that provides feedback in an optimal format by taking into account device information of the user when providing feedback. The feedback unit uses a generation AI to provide feedback in an optimal format by taking into account device information of the user when providing feedback. For example, if the user is using a smartphone, feedback tailored to the screen size can be provided. Also, if the user is using a tablet, feedback optimized for a large screen can be provided. Also, if the user is using a smartwatch, feedback that is concise and highly visible can be provided. This allows feedback to be provided in an optimal format by taking into account device information. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit may input device information into the generation AI and cause the generation AI to optimize the feedback.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The personal training system provides personalized training tailored to the user's individual needs based on their physique information. It can also collect the user's dietary data and propose nutritionally balanced meal plans. For example, when the user inputs their meal plan, the AI ​​analyzes the data and calculates the required nutrients and calories. It can also customize meal plans to suit the user's goals and suggest specific recipes and ingredients. This allows it to support the user's health in both training and diet.

[0053] The personal training system provides personalized training tailored to the user's individual needs based on their physique information. It can also collect the user's sleep data and provide advice on improving sleep quality. For example, when the user inputs their sleep duration and quality, the AI ​​analyzes the data and suggests areas for improvement. It can also suggest optimal sleep duration and relaxation methods based on the user's training plan. This can support the user's health in terms of both training and sleep.

[0054] The personal training system provides personalized training tailored to the user's individual needs based on the user's physique information. It can also monitor the user's stress level and provide advice on stress reduction. For example, if the user is feeling stressed, it can suggest relaxing breathing techniques or meditation. It can also adjust the intensity and frequency of training according to the user's stress level. This allows the system to support the user's health in terms of both training and stress management.

[0055] The personal training system provides personalized training tailored to the user's individual needs based on the user's physique information. It can also analyze the user's exercise history and suggest optimal rest times. For example, it can calculate appropriate rest times based on the user's past exercise data. It can also customize rest times and suggest specific rest methods based on the user's goals. This helps support effective training by balancing training and rest.

[0056] The personal training system provides personalized training tailored to the user's individual needs based on the user's physique information, and can also visually display the results of the training based on the user's exercise data. For example, the system can display the user's exercise data in graphs or charts, allowing the user to visually check their progress. It can also display the user's progress toward their goals, increasing motivation. This allows the user to maintain their motivation by visually checking the results of their training.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The camera captures the user's body shape. The user's body shape includes, for example, height, weight, body fat percentage, and dimensions of each body part. The camera can capture the user's body shape in detail using a smartphone camera or 3D scanning technology. Step 2: The input unit inputs the area or goal the user wants to improve. For example, the user can input goals such as "I want to tighten my stomach" or "I want to strengthen my arms" using text input or voice input. The input unit can also estimate the user's emotions and customize the input interface based on the estimated emotions. Step 3: The suggestion unit uses generative AI to generate an optimal exercise plan based on the user's body type and goals. For example, it suggests specific exercises such as "stretches to tighten the stomach" or "muscle training to strengthen the arms." The suggestion unit can also estimate the user's emotions and adjust the way the suggestions are presented based on the estimated emotions. Step 4: The analysis unit uses the generative AI to analyze the video of the user's exercise. For example, if the user performs a squat, the analysis unit analyzes their form and suggests correct form and areas for improvement. The analysis unit can also analyze the user's exercise data in real time and provide immediate feedback. Step 5: The form suggestion unit suggests correct form or alternative movements based on the analysis results. For example, it analyzes the user's squat form and suggests correct form and areas for improvement. The form suggestion unit can also estimate the user's emotions and adjust the method of form suggestions based on the estimated emotions. Step 6: The feedback unit periodically photographs the user's body and provides feedback on progress and areas for improvement. For example, the user can periodically photograph their body and input the footage into the generation AI. The generation AI compares it with past data and provides specific feedback such as "Your stomach is getting tighter" or "Your arm muscles are getting stronger." The feedback unit can also estimate the user's emotions and adjust the method of feedback based on the estimated emotions.

[0059] (Example 2) A personal training system according to an embodiment of the present invention provides personalized training tailored to individual needs based on a user's physique information, supporting dieting and building an ideal physique. The personal training system captures the user's physique, inputs the areas of improvement and goals, and a generation AI suggests appropriate stretches and muscle training exercises. The user films an actual exercise video, and the generation AI analyzes the video. Based on the analysis results, the system suggests correct form and alternative movements, and provides appropriate feedback by periodically filming the user's physique. For example, in a personal training system, a user films their physique and inputs the areas of improvement and goals. For example, the user inputs goals such as "I want to tighten my stomach" or "I want to strengthen my arms." This information is input into the generation AI. The generation AI then analyzes the input information and suggests appropriate stretches and muscle training exercises. The generation AI generates an optimal exercise plan based on the user's physique and goals. For example, the system suggests specific exercises such as "stretches to tighten my stomach" and "muscle training to strengthen my arms." The user films an actual exercise video, and the generation AI analyzes the video. The generation AI analyzes video footage of the user's exercises and suggests correct form or alternative movements. For example, when a user performs squats, the generation AI analyzes the user's form and suggests correct form and areas for improvement. Furthermore, by regularly taking photos of their body shape, the generation AI provides appropriate feedback. Users regularly take photos of their body shape and input the videos into the generation AI. The generation AI compares the images with past data and provides feedback on progress and areas for improvement. For example, it provides specific feedback such as "Your stomach is getting tighter" or "Your arm muscles are getting stronger." This allows the personal training system to provide personalized training tailored to the user's individual needs and support dieting and building their ideal physique. This allows the personal training system to provide personalized training tailored to the user's individual needs and support dieting and building their ideal physique based on the user's body shape information. For example, users can receive an exercise plan tailored to their body shape and goals, allowing them to exercise with the correct form.In addition, regular feedback helps you maintain motivation and progress with your training effectively.

[0060] A personal training system according to an embodiment includes a camera, an input unit, a suggestion unit, an analysis unit, a form suggestion unit, and a feedback unit. The camera captures a user's body shape. The user's body shape may include, but is not limited to, height, weight, body fat percentage, and body part dimensions. The camera captures the user's body shape using, for example, a smartphone camera. The camera can also capture the user's body shape in detail using 3D scanning technology. For example, the camera can capture the user's body shape as a 3D model, enabling detailed analysis. The input unit inputs the body part or goal the user wants to improve. For example, the user may input a goal such as "I want to tighten my stomach" or "I want to strengthen my arms." The input unit can input the goal using, for example, text input or voice input. The input unit can also estimate the user's emotions and customize the input interface based on the estimated user's emotions. For example, if the user is relaxed, detailed input options are provided and a customizable input method is suggested. The suggestion unit uses a generation AI to generate an optimal exercise plan based on the user's body type and goals. For example, the generation AI analyzes the user's body type information and suggests specific exercises such as "stretches to tighten the stomach" and "muscle training to strengthen the arms." The suggestion unit can also estimate the user's emotions and adjust the way the suggestions are presented based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit provides suggestions with detailed explanations. The analysis unit uses a generation AI to analyze video of the user's exercise. For example, if the user performs squats, the generation AI analyzes the form and suggests correct form and areas for improvement. The analysis unit can also analyze the user's exercise data in real time and provide immediate feedback. For example, it analyzes the user's exercise data in real time and provides effective feedback. The form suggestion unit suggests correct form or alternative movements based on the analysis results. For example, the generation AI analyzes the user's squat form and suggests correct form and areas for improvement. The form suggestion unit can also estimate the user's emotions and adjust the way the form suggestions are presented based on the estimated user's emotions.For example, if the user is relaxed, the feedback unit provides form suggestions with detailed explanations. The feedback unit periodically photographs the user's body shape and provides feedback on progress and areas for improvement. For example, the user periodically photographs their body shape and inputs the footage into the generation AI. The generation AI compares the footage with past data and provides specific feedback such as "your stomach is getting tighter" or "your arm muscles are getting stronger." The feedback unit can also estimate the user's emotions and adjust the feedback method based on the estimated user's emotions. For example, if the user is relaxed, the feedback unit provides detailed feedback. As a result, the personal training system according to the embodiment can provide personal training tailored to the user's individual needs based on the user's body shape information, supporting them in dieting and building their ideal body.

[0061] The personal training system further includes a capture unit that estimates the user's emotions and adjusts the timing of capturing images based on the estimated user emotions. The capture unit estimates the user's emotions and adjusts the timing of capturing images based on the estimated user emotions. For example, if the user is relaxed, capturing images can be started at a relaxed timing to capture natural facial expressions and postures. If the user is tense, an interaction can be performed to relieve tension, and capturing images can be started after the user has relaxed. If the user is in a hurry, capturing images can be done quickly to obtain the minimum necessary information. This allows capturing natural facial expressions and postures by capturing images at the optimal timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0062] The personal training system further includes a photographing unit that analyzes the user's past body shape data and suggests the optimal photographing angle and distance. The photographing unit analyzes the user's past body shape data and suggests the optimal photographing angle and distance. For example, the photographing unit takes photographs at the most effective angle based on the user's past body shape data. The photographing unit can also take photographs at an appropriate distance taking into account changes in the user's body shape. The photographing unit can also analyze the user's body shape data and suggest a photographing angle that emphasizes specific body parts. This allows for effective photographing by suggesting optimal photographing conditions based on the past body shape data. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without using AI. For example, the photographing unit can input past body shape data into a generation AI and have the generation AI suggest the optimal photographing angle and distance.

[0063] Furthermore, the personal training system includes a photographing unit that displays changes in the user's body shape in real time during photography, thereby improving the accuracy of photography. The photographing unit displays changes in the user's body shape in real time during photography, thereby improving the accuracy of photography. For example, the photographing unit may display changes in the user's body shape in real time during photography, thereby enabling accurate photography. Furthermore, if the user moves, the changes in body shape can be displayed in real time, allowing photography to be performed at the appropriate timing. Furthermore, the photographing unit may display changes in the user's body shape during photography, and adjust the photography angle and distance as necessary. This allows accurate photography by displaying changes in body shape in real time. Some or all of the above-described processing in the photographing unit may be performed using, for example, AI, or may be performed without AI. For example, the photographing unit may input body shape data acquired in real time into a generation AI, causing the generation AI to execute a process to display changes in body shape.

[0064] Furthermore, the personal training system includes a camera unit that captures the user's body shape as a 3D model during photography, enabling detailed analysis. The camera unit captures the user's body shape as a 3D model during photography, enabling detailed analysis. For example, the camera unit captures the user's body shape as a 3D model and performs detailed analysis. Furthermore, the 3D model can be used to perform detailed analysis of changes in the user's body shape. Furthermore, the 3D model can be used to visually display changes in the user's body shape. This enables detailed body shape analysis using the 3D model. Some or all of the above-described processing in the camera unit may be performed using, for example, AI, or may be performed without AI. For example, the camera unit can input the acquired body shape data into a generation AI, causing the generation AI to generate and analyze a 3D model.

[0065] The personal training system further includes an input unit that estimates the user's emotions and customizes the input interface based on the estimated user emotions. The input unit estimates the user's emotions and customizes the input interface based on the estimated user emotions. For example, if the user is relaxed, detailed input options are provided and a customizable input method is suggested. Alternatively, if the user is nervous, a simple interface can be provided to minimize input steps. Alternatively, if the user is in a hurry, voice input can be prioritized to enable quick input. This improves usability by providing an input interface that corresponds to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0066] The personal training system further includes an input unit that analyzes the user's past input data and suggests the optimal input method. The input unit analyzes the user's past input data and suggests the optimal input method. For example, the input unit automatically displays as candidates targets and body parts that the user has frequently input in the past. The input unit can also prioritize suggestions of input methods (voice, text, etc.) that the user has used in the past. The system can also predict and suggest targets and body parts to use in a specific time period based on the user's past input data. This improves input efficiency by suggesting the optimal input method based on the past input data. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input the past input data into a generation AI and have the generation AI suggest the optimal input method.

[0067] The personal training system further includes an input unit that analyzes the user's voice input during input and combines it with text input to improve accuracy. The input unit analyzes the user's voice input during input and combines it with text input to improve accuracy. For example, the input unit converts the user's voice input into text to improve input accuracy. Combining voice input and text input can also achieve more accurate input. Furthermore, the user's voice input can be analyzed and erroneous input can be automatically corrected. Combining voice input and text input improves input accuracy. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit can input voice data to a generation AI and have the generation AI perform text conversion and accuracy improvement processing.

[0068] Furthermore, the personal training system includes an input unit that recognizes a user's gestures during input and provides an intuitive input method. The input unit recognizes a user's gestures during input and provides an intuitive input method. For example, a user can easily set a target or body part by performing a specific gesture. A more intuitive input method can also be provided by combining gesture input and voice input. The system can also recognize a user's gestures and automatically complete the input content. This enables intuitive input using gesture input. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit can input gesture data to a generation AI and have the generation AI perform gesture recognition and input completion processing.

[0069] The personal training system further includes a suggestion unit that estimates the user's emotions and adjusts the way suggestions are presented based on the estimated user emotions. The suggestion unit uses a generation AI to estimate the user's emotions and adjusts the way suggestions are presented based on the estimated user emotions. For example, if the user is relaxed, the suggestion unit may provide a suggestion with detailed explanations. If the user is nervous, the suggestion unit may provide a simple, highly visible suggestion. If the user is in a hurry, the suggestion unit may provide a quick, to-the-point suggestion. This makes the suggestion more likely to be accepted by the user by providing a suggestion that is tailored to the user's emotions. The estimation of emotions is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0070] The personal training system further includes a suggestion unit that analyzes the user's body shape data in real time at the time of suggestion and provides an optimal exercise plan. The suggestion unit uses a generation AI to analyze the user's body shape data in real time at the time of suggestion and provides an optimal exercise plan. For example, the suggestion unit may analyze the user's body shape data in real time and suggest optimal stretching and muscle training. It may also be possible to provide an exercise plan tailored to the user's goals based on the real-time body shape data. It may also be possible to analyze the user's body shape data in real time and suggest an effective exercise plan. This allows for the provision of an optimal exercise plan by analyzing body shape data in real time. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input body shape data acquired in real time into the generation AI and cause the generation AI to generate an exercise plan.

[0071] The personal training system further includes a suggestion unit that analyzes the user's past exercise history at the time of suggestion and generates an individually customized exercise plan. The suggestion unit uses a generation AI to analyze the user's past exercise history at the time of suggestion and generate an individually customized exercise plan. For example, the suggestion unit analyzes the user's past exercise history and proposes an effective exercise plan. The system can also generate an optimal exercise plan based on the results of the user's past exercises. The system can also provide an individually customized exercise plan taking the user's past exercise history into consideration. This enables effective training by providing a customized exercise plan based on the user's past exercise history. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past exercise history data into the generation AI and cause the generation AI to generate a customized exercise plan.

[0072] Furthermore, the personal training system includes a suggestion unit that suggests an optimal exercise time, taking into account the user's lifestyle rhythm when making a suggestion. The suggestion unit uses the generation AI to suggest an optimal exercise time, taking into account the user's lifestyle rhythm when making a suggestion. For example, the suggestion unit analyzes the user's lifestyle rhythm and suggests an optimal exercise time. It can also suggest an effective exercise time that matches the user's lifestyle rhythm. It can also suggest a reasonable exercise time, taking into account the user's lifestyle rhythm. This allows for a reasonable training by suggesting an exercise time that matches the user's lifestyle rhythm. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input lifestyle rhythm data into the generation AI and have the generation AI suggest an optimal exercise time.

[0073] The personal training system further includes an analysis unit that estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. The analysis unit uses a generation AI to estimate the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, detailed analysis results can be displayed. If the user is nervous, simple, highly visible analysis results can be displayed. If the user is in a hurry, analysis results that focus on the main points can be displayed. This allows the analysis results to be displayed according to the user's emotions, thereby deepening understanding of the analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] Furthermore, the personal training system includes an analysis unit that compares the user's body shape data with past data during analysis and analyzes the progress in detail. The analysis unit uses the generation AI to compare the user's body shape data with past data during analysis and analyzes the progress in detail. For example, the analysis unit compares the user's body shape data with past data and analyzes the progress in detail. It is also possible to analyze changes in the user's body shape in detail based on the past data. It is also possible to compare the user's body shape data with past data and analyze areas for improvement in detail. This allows the progress to be understood in detail by comparing with past data. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit may input past body shape data into the generation AI and have the generation AI analyze the progress.

[0075] The personal training system further includes an analysis unit that analyzes the user's exercise data in real time during analysis and provides immediate feedback. The analysis unit uses a generation AI to analyze the user's exercise data in real time during analysis and provides immediate feedback. For example, the analysis unit may analyze the user's exercise data in real time and provide immediate feedback. Effective feedback may also be provided based on the real-time exercise data. The analysis unit may also analyze the user's exercise data in real time and provide immediate feedback on areas for improvement. This allows for immediate feedback by analyzing the exercise data in real time. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input exercise data acquired in real time to the generation AI and cause the generation AI to provide immediate feedback.

[0076] Furthermore, the personal training system includes an analysis unit that analyzes the user's body shape data as a 3D model during analysis and provides detailed feedback. The analysis unit uses a generation AI to analyze the user's body shape data as a 3D model during analysis and provides detailed feedback. For example, the analysis unit analyzes the user's body shape data as a 3D model and provides detailed feedback. It is also possible to analyze changes in the user's body shape in detail based on the 3D model. It is also possible to analyze the user's body shape data as a 3D model and provide visual feedback. This enables detailed feedback using the 3D model. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the acquired body shape data into the generation AI and cause the generation AI to generate and analyze a 3D model.

[0077] The personal training system further includes a form suggestion unit that estimates the user's emotions and adjusts the form suggestion method based on the estimated user emotions. The form suggestion unit uses a generation AI to estimate the user's emotions and adjusts the form suggestion method based on the estimated user emotions. For example, if the user is relaxed, the form suggestion unit can provide a form suggestion that includes detailed explanations. If the user is nervous, the form suggestion unit can provide a simple, highly visible form suggestion. If the user is in a hurry, the form suggestion unit can provide a quick, concise form suggestion. This improves the ease with which the suggestion can be accepted by providing a form suggestion that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0078] Furthermore, the personal training system includes a form suggestion unit that analyzes the user's body shape data in real time when proposing a form and proposes an optimal form. The form suggestion unit uses a generation AI to analyze the user's body shape data in real time when proposing a form and proposes an optimal form. For example, the form suggestion unit analyzes the user's body shape data in real time and proposes an optimal form. It is also possible to propose a form that matches the user's goals based on the real-time body shape data. It is also possible to analyze the user's body shape data in real time and propose an effective form. In this way, it is possible to propose an optimal form by analyzing body shape data in real time. Some or all of the above-described processing in the form suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the form suggestion unit may input body shape data acquired in real time into the generation AI and have the generation AI execute form suggestions.

[0079] The personal training system further includes a form suggestion unit that analyzes the user's past exercise data when proposing a form and proposes an individually customized form. The form suggestion unit uses a generation AI to analyze the user's past exercise data when proposing a form and proposes an individually customized form. For example, the form suggestion unit analyzes the user's past exercise data and proposes an effective form. The system can also propose an optimal form based on the results of the user's past exercises. The system can also propose an individually customized form taking the user's past exercise data into consideration. This enables effective training by proposing a customized form based on past exercise data. Some or all of the above-described processing in the form suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the form suggestion unit can input past exercise data into the generation AI and have the generation AI propose a customized form.

[0080] Furthermore, the personal training system includes a form suggestion unit that considers the user's lifestyle when proposing a form and proposes an optimal form. The form suggestion unit uses a generation AI to consider the user's lifestyle when proposing a form and proposes an optimal form. For example, it analyzes the user's lifestyle and proposes an optimal form. It is also possible to propose an effective form that matches the user's lifestyle. It is also possible to consider the user's lifestyle and propose a reasonable form. In this way, by proposing a form that matches the user's lifestyle, a reasonable training is possible. Some or all of the above-described processing in the form suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the form suggestion unit may input lifestyle data into the generation AI and have the generation AI execute the proposal of an optimal form.

[0081] The personal training system further includes a feedback unit that estimates the user's emotions and adjusts the feedback method based on the estimated user emotions. The feedback unit uses a generation AI to estimate the user's emotions and adjusts the feedback method based on the estimated user emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is nervous, simple, highly visible feedback can be provided. If the user is in a hurry, quick feedback that focuses on the main points can be provided. This provides feedback according to the user's emotions, improving the ease with which the feedback is accepted. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] Furthermore, the personal training system includes a feedback unit that compares the user's body shape data with past data at the time of feedback and provides detailed feedback on the progress. The feedback unit uses the generation AI to compare the user's body shape data with past data at the time of feedback and provides detailed feedback on the progress. For example, the feedback unit compares the user's body shape data with past data and provides detailed feedback on the progress. It is also possible to provide detailed feedback on changes in the user's body shape based on past data. It is also possible to compare the user's body shape data with past data and provide detailed feedback on areas for improvement. This allows the progress to be understood in detail by comparing with past data. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit may input past body shape data into the generation AI and cause the generation AI to provide feedback on the progress.

[0083] The personal training system further includes a feedback unit that analyzes the user's exercise data in real time at the time of feedback and provides immediate feedback. The feedback unit uses a generation AI to analyze the user's exercise data in real time at the time of feedback and provides immediate feedback. For example, the feedback unit may analyze the user's exercise data in real time and provide immediate feedback. Effective feedback may also be provided based on the real-time exercise data. The user's exercise data may also be analyzed in real time and immediate feedback on areas for improvement may be provided. This allows for immediate feedback to be provided by analyzing the exercise data in real time. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input exercise data acquired in real time to the generation AI and cause the generation AI to provide immediate feedback.

[0084] The personal training system further includes a feedback unit that analyzes the user's body shape data as a 3D model and provides detailed feedback when providing feedback. The feedback unit uses a generation AI to analyze the user's body shape data as a 3D model and provide detailed feedback when providing feedback. For example, the feedback unit may analyze the user's body shape data as a 3D model and provide detailed feedback. Detailed feedback of changes in the user's body shape can also be provided based on the 3D model. The user's body shape data can also be analyzed as a 3D model and visual feedback can be provided. Using a 3D model enables detailed feedback. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input the acquired body shape data into the generation AI and cause the generation AI to generate and analyze a 3D model.

[0085] The personal training system further includes a feedback unit that estimates the user's emotions and prioritizes feedback based on the estimated user emotions. The feedback unit uses a generation AI to estimate the user's emotions and prioritizes feedback based on the estimated user emotions. For example, if the user is very interested in specific feedback, that feedback can be provided preferentially. Also, if the user is relaxed, general feedback can be provided. Also, if the user is tense, specific feedback can be provided preferentially to relieve tension. In this way, by prioritizing feedback according to the user's emotions, important feedback can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0086] Furthermore, the personal training system includes a feedback unit that optimizes feedback by taking into account the user's environmental data when providing feedback. The feedback unit optimizes feedback by taking into account the user's environmental data using a generation AI. For example, optimal feedback is provided based on the user's environmental data. Effective feedback can also be provided by taking into account the user's environmental data. Highly visible feedback can also be provided based on the user's environmental data. This allows optimal feedback to be provided by taking into account the environmental data. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input environmental data into the generation AI and cause the generation AI to optimize the feedback.

[0087] Furthermore, the personal training system includes a feedback unit that adjusts the feedback method by reflecting the user's past feedback when providing feedback. The feedback unit uses a generation AI to adjust the feedback method by reflecting the user's past feedback when providing feedback. For example, the feedback unit provides an optimal feedback method based on the user's past feedback. It can also provide a feedback method that resolves problems previously pointed out by the user. It can also improve the feedback method by reflecting the user's past feedback. In this way, the feedback method can be improved by reflecting past feedback. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input past feedback data into the generation AI and have the generation AI adjust the feedback method.

[0088] The personal training system further includes a feedback unit that provides feedback in an optimal format by taking into account device information of the user when providing feedback. The feedback unit uses a generation AI to provide feedback in an optimal format by taking into account device information of the user when providing feedback. For example, if the user is using a smartphone, feedback tailored to the screen size can be provided. Also, if the user is using a tablet, feedback optimized for a large screen can be provided. Also, if the user is using a smartwatch, feedback that is concise and highly visible can be provided. This allows feedback to be provided in an optimal format by taking into account device information. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or may be performed without using AI. For example, the feedback unit may input device information into the generation AI and cause the generation AI to optimize the feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned photographing unit, input unit, suggestion unit, analysis unit, form suggestion unit, and feedback unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the photographing unit photographs the user's body shape using the camera 42 of the smart device 14. The input unit inputs the user's goals using the reception device 38 of the smart device 14. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal exercise plan using a generation AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's exercise video. The form suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests a correct form based on the analysis results. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback based on periodic body shape photographs. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned photographing unit, input unit, suggestion unit, analysis unit, form suggestion unit, and feedback unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the photographing unit photographs the user's body shape using the camera 42 of the smart glasses 214. The input unit inputs the user's goals using the microphone 238 of the smart glasses 214. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal exercise plan using a generative AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's exercise video. The form suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests a correct form based on the analysis results. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback based on periodic body shape photographs. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned photographing unit, input unit, suggestion unit, analysis unit, form suggestion unit, and feedback unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the photographing unit photographs the user's body shape using the camera 42 of the headset-type terminal 314. The input unit inputs the user's goals using the microphone 238 of the headset-type terminal 314. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal exercise plan using a generation AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's exercise video. The form suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests a correct form based on the analysis results. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback based on periodic body shape photographs. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned photographing unit, input unit, suggestion unit, analysis unit, form suggestion unit, and feedback unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the photographing unit photographs the user's body shape using the camera 42 of the robot 414. The input unit inputs the user's goals using the microphone 238 of the robot 414. The suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and generates an optimal exercise plan using a generative AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's exercise video. The form suggestion unit is realized by the specific processing unit 290 of the data processing device 12 and suggests a correct form based on the analysis results. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides feedback based on periodic body shape photographs.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The personal training system provides personalized training tailored to the user's individual needs based on their physique information. It can also collect the user's dietary data and propose nutritionally balanced meal plans. For example, when the user inputs their meal plan, the AI ​​analyzes the data and calculates the required nutrients and calories. It can also customize meal plans to suit the user's goals and suggest specific recipes and ingredients. This allows it to support the user's health in both training and diet.

[0091] The personal training system can also estimate the user's emotions and adjust the difficulty of the training based on the estimated user's emotions. For example, if the user is feeling stressed, it can suggest light exercise that has a relaxing effect. Alternatively, if the user is highly motivated, it can suggest challenging training. Furthermore, it can adjust the frequency and duration of training depending on the user's emotions. This makes it possible to provide a flexible training plan that matches the user's emotional state.

[0092] The personal training system provides personalized training tailored to the user's individual needs based on their physique information. It can also collect the user's sleep data and provide advice on improving sleep quality. For example, when the user inputs their sleep duration and quality, the AI ​​analyzes the data and suggests areas for improvement. It can also suggest optimal sleep duration and relaxation methods based on the user's training plan. This can support the user's health in terms of both training and sleep.

[0093] The personal training system can also estimate the user's emotions and select training music based on the estimated user's emotions. For example, if the user is relaxed, it can play relaxing music. If the user wants to increase motivation, it can suggest energetic music. It can also adjust the tempo and genre of the music depending on the user's emotions. This can improve the effectiveness of training by providing music that matches the user's emotional state.

[0094] The personal training system provides personalized training tailored to the user's individual needs based on the user's physique information. It can also monitor the user's stress level and provide advice on stress reduction. For example, if the user is feeling stressed, it can suggest relaxing breathing techniques or meditation. It can also adjust the intensity and frequency of training according to the user's stress level. This allows the system to support the user's health in terms of both training and stress management.

[0095] The personal training system can also estimate the user's emotions and customize the training interface based on the estimated user's emotions. For example, if the user is relaxed, an interface with detailed explanations can be provided. If the user is tense, a simple, highly visible interface can be provided. Furthermore, if the user is in a hurry, a quick interface that focuses on the main points can be provided. This makes it possible to provide an easy-to-use interface that matches the user's emotional state.

[0096] The personal training system provides personalized training tailored to the user's individual needs based on the user's physique information. It can also analyze the user's exercise history and suggest optimal rest times. For example, it can calculate appropriate rest times based on the user's past exercise data. It can also customize rest times and suggest specific rest methods based on the user's goals. This helps support effective training by balancing training and rest.

[0097] The personal training system can also estimate the user's emotions and provide messages to maintain motivation for training based on the estimated user's emotions. For example, if the user is losing motivation, an encouraging message can be displayed. Alternatively, if the user is approaching a goal, a message that gives the user a sense of accomplishment can be provided. Furthermore, the content and timing of the message can be adjusted depending on the user's emotions. This allows for support in maintaining motivation tailored to the user's emotional state.

[0098] The personal training system provides personalized training tailored to the user's individual needs based on the user's physique information, and can also visually display the results of the training based on the user's exercise data. For example, the system can display the user's exercise data in graphs or charts, allowing the user to visually check their progress. It can also display the user's progress toward their goals, increasing motivation. This allows the user to maintain their motivation by visually checking the results of their training.

[0099] The personal training system can also estimate the user's emotions and customize training feedback based on the estimated user's emotions. For example, if the user is relaxed, detailed feedback can be provided. If the user is tense, simple, highly visible feedback can be provided. Furthermore, if the user is in a hurry, quick feedback that focuses on the key points can be provided. This can improve the effectiveness of training by providing feedback that matches the user's emotional state.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The camera captures the user's body shape. The user's body shape includes, for example, height, weight, body fat percentage, and dimensions of each body part. The camera can capture the user's body shape in detail using a smartphone camera or 3D scanning technology. Step 2: The input unit inputs the area or goal the user wants to improve. For example, the user can input goals such as "I want to tighten my stomach" or "I want to strengthen my arms" using text input or voice input. The input unit can also estimate the user's emotions and customize the input interface based on the estimated emotions. Step 3: The suggestion unit uses generative AI to generate an optimal exercise plan based on the user's body type and goals. For example, it suggests specific exercises such as "stretches to tighten the stomach" or "muscle training to strengthen the arms." The suggestion unit can also estimate the user's emotions and adjust the way the suggestions are presented based on the estimated emotions. Step 4: The analysis unit uses the generative AI to analyze the video of the user's exercise. For example, if the user performs a squat, the analysis unit analyzes their form and suggests correct form and areas for improvement. The analysis unit can also analyze the user's exercise data in real time and provide immediate feedback. Step 5: The form suggestion unit suggests correct form or alternative movements based on the analysis results. For example, it analyzes the user's squat form and suggests correct form and areas for improvement. The form suggestion unit can also estimate the user's emotions and adjust the method of form suggestions based on the estimated emotions. Step 6: The feedback unit periodically photographs the user's body and provides feedback on progress and areas for improvement. For example, the user can periodically photograph their body and input the footage into the generation AI. The generation AI compares it with past data and provides specific feedback such as "Your stomach is getting tighter" or "Your arm muscles are getting stronger." The feedback unit can also estimate the user's emotions and adjust the method of feedback based on the estimated emotions.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 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. an imaging unit that captures an image of the user's body shape; an input unit for inputting a part or goal to be improved based on the body shape information photographed by the photographing unit; a suggestion unit that suggests appropriate stretching or muscle training based on the information input by the input unit; an analysis unit that analyzes the exercise video proposed by the proposal unit; a form suggestion unit that suggests a correct form or another action based on the result of the analysis by the analysis unit; a feedback unit that periodically captures the actions suggested by the form suggestion unit and provides feedback. A system characterized by:

2. The imaging unit is Estimate the user's emotions and adjust the timing of taking photos based on the estimated user emotions.

2. The system of claim 1.

3. The imaging unit is Analyzes the user's past body shape data and suggests the optimal shooting angle or distance 2. The system of claim 1.

4. The imaging unit is Displays changes in the user's body shape in real time while taking a photo, improving the accuracy of the photo.

2. The system of claim 1.

5. The imaging unit is Captures the user's body shape as a 3D model during photography, enabling detailed analysis 2. The system of claim 1.

6. The input unit Estimate user emotions and customize the input interface based on the estimated user emotions 2. The system of claim 1.

7. The input unit Analyzes the user's past input data and suggests the appropriate input method 2. The system of claim 1.

8. The input unit Analyzes user voice input as they type and combines it with text input to improve accuracy 2. The system of claim 1.

Images