system

Abstract

The system according to the embodiment aims to provide an optimal training menu based on the user's basic information and desired body shape, and to visualize the progress. [Solution] A system according to an embodiment includes an acquisition unit, a selection unit, a generation unit, a capture unit, and a visualization unit. The acquisition unit acquires basic information about the user. The selection unit selects a desired body type. The generation unit generates a training menu based on the information obtained by the acquisition unit and the selection unit. The capture unit captures an image of the user's body type. The visualization unit visualizes progress based on the body type image captured by the capture unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide optimal training menus for individual users or visualize their progress, so there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal training menu based on the user's basic information and desired body shape, and to visualize the progress. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a selection unit, a generation unit, a capture unit, and a visualization unit. The acquisition unit acquires basic information about the user. The selection unit selects a desired body type. The generation unit generates a training menu based on the information obtained by the acquisition unit and the selection unit. The capture unit captures an image of the user's body type. The visualization unit visualizes progress based on the image of the body type captured by the capture unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an optimal training menu based on the user's basic information and desired body shape, and visualize the progress. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A training support system according to an embodiment of the present invention allows a user to select a desired body type from a model and then use AI to present an optimal training menu based on basic information (height, weight). This training support system allows a user to select a desired body type from a model and input basic information (height, weight), and then AI presents the optimal training menu. The training menu is primarily composed of exercises that can be performed at home or outdoors, eliminating the hassle of going to the gym. Furthermore, users can import images of their body type on a weekly basis, and the AI ​​analyzes the imported images and presents training menus accordingly. Progress can also be visualized using body type match rates and other metrics. This allows users to continue training while checking their progress. This system is offered as a low-cost subscription service, differentiating it from expensive personal gyms and regular gyms. For example, when selecting a desired body type, users can choose from multiple models. Once the user inputs basic information, the AI ​​generates an optimal training menu based on that information. The training menu can include exercises such as bodyweight training, running, and yoga. Users take weekly images of their body shape, and the AI ​​analyzes the images to evaluate their progress. Progress is visualized, for example, through body shape match rates, graphs, and charts. This allows users to see the effectiveness of their training and maintain their motivation. Furthermore, since the system is offered as a low-cost subscription service, users can easily use it. Compared to expensive personal gyms or regular gyms, it allows users to train effectively while keeping costs down. This allows the training support system to generate an optimal training menu based on the user's basic information and desired body shape, and visualize their progress.

[0029] A training support system according to an embodiment includes an acquisition unit, a selection unit, a generation unit, a capture unit, and a visualization unit. The acquisition unit acquires basic information about a user. The basic information includes, but is not limited to, age, gender, height, weight, and health status. The acquisition unit acquires the basic information based on, for example, information input by the user. The acquisition unit can also analyze the user's past health data and select an optimal acquisition method. The selection unit selects a desired body type for the user. The desired body type includes, for example, a standard body type, an athletic body type, and the like, but is not limited to these examples. The selection unit allows the user to select from multiple models. The generation unit generates a training menu based on the information obtained by the acquisition unit and the selection unit. The generation unit generates an optimal training menu for the user using, for example, AI. The training menu includes, for example, exercises such as bodyweight training, running, and yoga, but is not limited to these examples. The generation unit can also customize the training menu based on the user's current physical condition and lifestyle. The capture unit captures an image of the user's body type. The capture unit allows the user to capture body shape images on a weekly basis, for example. The capture unit can also estimate the user's emotions and adjust the timing of capturing body shape images based on the estimated user emotions. The visualization unit visualizes progress based on the captured body shape images. The visualization unit displays, for example, a body shape match rate. The body shape match rate includes, but is not limited to, changes in body fat percentage and increases or decreases in muscle mass. The visualization unit can also display the user's progress in graphs or charts. As a result, the training support system according to the embodiment can generate an optimal training menu based on the user's basic information and desired body shape, and visualize the progress.

[0030] The generation unit can use AI to generate a training menu suitable for the user. The generation unit generates an optimal training menu for the user using AI technologies such as machine learning and deep learning. For example, the generation unit determines the type of exercise, number of repetitions, number of sets, etc. based on the user's basic information and desired body type. The generation unit can also generate an optimal menu by referring to the user's past training history. For example, the generation unit suggests the most effective training menu based on the user's past training history. Furthermore, the generation unit can customize the training menu based on the user's current physical condition and lifestyle. For example, the generation unit analyzes the user's physical condition and lifestyle to generate the most effective training menu. In this way, the use of AI can generate an optimal training menu for the user.

[0031] The generator can configure the training menu mainly based on exercises that can be done at home or outdoors. The generator configures the training menu mainly based on exercises that can be done at home or outdoors, such as bodyweight training, running, and yoga. For example, the generator can include push-ups, squats, planks, and the like as bodyweight training. The generator can also include aerobic exercises such as running and walking. Furthermore, the generator can also include exercises that increase flexibility, such as yoga and stretching. This can eliminate the hassle of going to the gym by providing a training menu that can be done at home or outdoors.

[0032] The capture unit can capture a body image of the user weekly. The capture unit is designed, for example, to allow the user to capture a body image weekly. For example, the capture unit can allow the user to take a body image using a smartphone camera and upload it to the system. The capture unit can also provide a reminder for the user to periodically capture body images. This allows the user to periodically check their progress by capturing body images weekly.

[0033] The visualization unit can display the degree of match of the body type based on the captured body type image. The visualization unit, for example, analyzes the captured body type image and displays the degree of match with the user's desired body type. The degree of match of the body type includes, but is not limited to, changes in body fat percentage, increases or decreases in muscle mass, etc. The visualization unit can display the degree of match of the body type as a percentage, for example. The visualization unit can also visually display the degree of match of the body type in a graph or chart. In this way, the user's progress can be visualized by displaying the body type match rate.

[0034] The visualization unit can display the user's progress in a graph or chart. The visualization unit can display the user's progress, for example, in a line graph, bar graph, pie chart, or the like. For example, the visualization unit can display the change in the user's weight in a line graph. The visualization unit can also display the increase or decrease in the user's muscle mass in a bar graph. Furthermore, the visualization unit can display the change in the user's body fat percentage in a pie chart. In this way, by displaying the progress in a graph or chart, the user can visually check the progress.

[0035] The acquisition unit can analyze the user's past health data and select an appropriate acquisition method. For example, the acquisition unit can suggest the most effective training menu based on the user's past health data. For example, the acquisition unit can analyze the user's past health data and acquire basic information for a specific time period. The acquisition unit can also customize the acquisition method based on the user's past health data. For example, the acquisition unit selects the optimal acquisition method based on the user's past health data. In this way, basic information can be acquired in the optimal method by analyzing the past health data.

[0036] When acquiring the basic information, the acquisition unit can perform filtering based on the user's current lifestyle habits and dietary details. The acquisition unit acquires appropriate basic information, for example, taking into account the user's current lifestyle habits. For example, the acquisition unit can filter necessary basic information based on the user's dietary details. The acquisition unit can also adjust the timing of acquiring the basic information to match the user's lifestyle rhythm. For example, the acquisition unit analyzes the user's lifestyle rhythm and acquires the basic information at the optimal timing. This allows more accurate basic information to be acquired by filtering the information based on the user's lifestyle habits and dietary details.

[0037] When acquiring basic information, the acquisition unit can prioritize acquiring highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the acquisition unit prioritizes acquiring basic information related to that area. For example, the acquisition unit can acquire optimal basic information based on the user's geographical location information. Furthermore, when the user is traveling, the acquisition unit can also prioritize acquiring basic information related to the travel destination. In this way, highly relevant information can be prioritized by taking geographical location information into consideration.

[0038] When acquiring the basic information, the acquisition unit can analyze the user's social media activity and collect related information. The acquisition unit, for example, acquires the related basic information based on the user's social media activity. For example, the acquisition unit can acquire the basic information by referring to information shared by the user on social media. The acquisition unit can also analyze the user's social media activity and acquire optimal basic information. In this way, related information can be acquired by analyzing social media activity.

[0039] When making a selection, the selection unit can suggest an appropriate model by referring to the user's past body shape change data. The selection unit, for example, suggests an optimal body shape model based on the user's past body shape change data. For example, the selection unit can analyze the user's past body shape change data and suggest the most effective body shape model. The selection unit can also suggest a customized body shape model by referring to the user's past body shape change data. This makes it possible to suggest an optimal body shape model by referring to the past body shape change data.

[0040] At the time of selection, the selection unit can customize the body type model based on the user's current health condition. For example, the selection unit can propose an optimal body type model taking into account the user's current health condition. For example, the selection unit can customize the body type model based on the user's health condition. The selection unit can also analyze the user's health condition and propose the most effective body type model. In this way, by customizing the body type model based on the user's current health condition, a more appropriate model can be proposed.

[0041] At the time of selection, the selection unit can suggest an appropriate body type model based on the user's geographical location information. For example, if the user is in a specific area, the selection unit can suggest a body type model related to that area. For example, the selection unit can suggest an optimal body type model based on the user's geographical location information. Furthermore, if the user is traveling, the selection unit can also suggest a body type model related to the user's travel destination. In this way, the optimal body type model can be suggested by taking the geographical location information into consideration.

[0042] At the time of selection, the selection unit can analyze the user's social media activity and suggest a related body model. The selection unit can suggest a related body model based on, for example, the user's social media activity. For example, the selection unit can suggest a body model based on information shared by the user on social media. The selection unit can also analyze the user's social media activity and suggest an optimal body model. In this way, related body models can be suggested by analyzing social media activity.

[0043] When generating the training menu, the generation unit can generate an appropriate menu by referring to the user's past training history. The generation unit generates an optimal training menu based on, for example, the user's past training history. For example, the generation unit can analyze the user's past training history and generate the most effective training menu. The generation unit can also generate a customized training menu by referring to the user's past training history. In this way, an optimal training menu can be generated by referring to the past training history.

[0044] The generation unit can customize the training menu based on the user's current physical condition and lifestyle rhythm when generating the menu. The generation unit generates an optimal training menu, for example, taking into account the user's current physical condition. For example, the generation unit can customize the training menu based on the user's lifestyle rhythm. The generation unit can also analyze the user's physical condition and lifestyle rhythm to generate the most effective training menu. This makes it possible to provide a more effective menu by customizing the training menu based on the user's current physical condition and lifestyle rhythm.

[0045] The generation unit can generate an appropriate training menu based on the user's geographical location information at the time of generation. For example, if the user is in a specific area, the generation unit generates a training menu related to that area. For example, the generation unit can generate an optimal training menu based on the user's geographical location information. Furthermore, if the user is traveling, the generation unit can also generate a training menu related to the user's travel destination. In this way, an optimal training menu can be generated by taking the geographical location information into consideration.

[0046] At the time of generation, the generation unit can analyze the user's social media activity and generate a related training menu. The generation unit generates the related training menu based on, for example, the user's social media activity. For example, the generation unit can generate the training menu by referring to information shared by the user on social media. The generation unit can also analyze the user's social media activity and generate an optimal training menu. In this way, a related training menu can be generated by analyzing social media activity.

[0047] The capture unit can select an appropriate capture method by referring to the user's past body images during capture. The capture unit can, for example, suggest an optimal capture method based on the user's past body images. For example, the capture unit can analyze the user's past body images and select the most effective capture method. The capture unit can also suggest a customized capture method by referring to the user's past body images. This allows the image to be captured in the optimal method by referring to the user's past body images.

[0048] The capture unit can customize the capture of body shape images based on the user's current living situation during capture. For example, the capture unit can consider the user's current living situation and propose an optimal body shape image capture method. For example, the capture unit can adjust the timing of body shape image capture to match the user's daily rhythm. The capture unit can also analyze the user's living situation and select the most effective body shape image capture method. This allows for more effective image capture by customizing image capture based on the user's current living situation.

[0049] The capture unit can capture an appropriate body type image based on the user's geographical location information during capture. For example, if the user is in a specific area, the capture unit prioritizes capturing body type images related to that area. For example, the capture unit can capture an optimal body type image based on the user's geographical location information. Furthermore, if the user is traveling, the capture unit can also prioritize capturing body type images related to the travel destination. This allows the optimal body type image to be captured by taking the geographical location information into consideration.

[0050] During the import process, the import unit can analyze the user's social media activity and import related body shape images. The import unit imports related body shape images based on, for example, the user's social media activity. For example, the import unit can import body shape images with reference to information shared by the user on social media. The import unit can also analyze the user's social media activity and import optimal body shape images. In this way, related body shape images can be imported by analyzing social media activity.

[0051] When visualizing, the visualization unit can select an appropriate display method by referring to the user's past progress data. The visualization unit, for example, proposes an optimal display method based on the user's past progress data. For example, the visualization unit can analyze the user's past progress data and select the most effective display method. The visualization unit can also propose a customized display method by referring to the user's past progress data. In this way, the optimal display method can be provided by referring to the past progress data.

[0052] The visualization unit can customize the progress display based on the user's current goal when visualizing. For example, the visualization unit considers the user's current goal and proposes an optimal progress display method. For example, the visualization unit can customize the progress display based on the user's goal. The visualization unit can also analyze the user's goal and select the most effective progress display method. In this way, customizing the progress display based on the current goal can provide a more effective display.

[0053] When visualizing, the visualization unit can display an appropriate progress display based on the user's geographical location information. For example, if the user is in a specific area, the visualization unit will prioritize displaying progress information related to that area. For example, the visualization unit can display an optimal progress display based on the user's geographical location information. Furthermore, if the user is traveling, the visualization unit can also prioritize displaying progress information related to the user's travel destination. In this way, the optimal progress display can be provided by taking the geographical location information into consideration.

[0054] During visualization, the visualization unit can analyze the user's social media activity and display related progress. The visualization unit, for example, displays related progress information based on the user's social media activity. For example, the visualization unit can display progress by referring to information shared by the user on social media. The visualization unit can also analyze the user's social media activity and display an optimal progress display. In this way, a related progress display can be provided by analyzing social media activity.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The training support system further includes a reward unit. The reward unit can provide a reward when the user achieves a set goal. For example, the reward unit can award a badge or points when the user approaches a target body shape. The reward unit can also provide benefits or coupons when the user continues training for a certain period of time. Furthermore, the reward unit can provide a special reward when the user competes with other users and ranks highly. This allows the user to maintain motivation by receiving rewards and continue training.

[0057] The generation unit can acquire the user's dietary data and reflect it in the training menu. For example, the generation unit can analyze the calorie intake and nutritional balance by having the user input the details of their meals, and adjust the training menu accordingly. The generation unit can also automatically recognize the details of meals using image analysis technology by having the user upload photos of their meals, and reflect the details in the training menu. Furthermore, the generation unit can also suggest an optimal meal and training schedule by having the user input the timing of meals. This allows the user to achieve a good balance between meals and training.

[0058] The generation unit can acquire the user's sleep data and reflect it in the training menu. For example, the generation unit can adjust the training menu by having the user input the amount of sleep and the quality of sleep. The generation unit can also automatically acquire sleep data using a wearable device by the user and reflect it in the training menu. Furthermore, the generation unit can also suggest the optimal training time by having the user input their sleep rhythm. This allows the user to achieve a good balance between sleep and training.

[0059] The capture unit can capture a user's body image using 3D scanning technology. For example, the capture unit can allow the user to scan their entire body using a smartphone camera and generate a 3D model. The capture unit can also allow the user to capture body images using a dedicated 3D scanner. Furthermore, by allowing the user to perform 3D scans periodically, the capture unit can record changes in body shape in detail and reflect them in training menus. This allows the user to train based on more accurate body shape data.

[0060] The visualization unit can display the user's progress using AR (augmented reality) technology. For example, the visualization unit can allow the user to take a photo of their own body shape using a smartphone camera and then use AR technology to display a comparison with the target body shape in real time. The visualization unit can also allow the user to check their progress using AR glasses. Furthermore, the visualization unit can also use AR technology to confirm the correct form while the user is training. This allows the user to train while visually checking their progress.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The acquisition unit acquires basic information about the user. The basic information includes, for example, age, gender, height, weight, and health condition. The acquisition unit acquires the basic information based on information entered by the user. The acquisition unit can also analyze the user's past health data and select the optimal acquisition method. Step 2: The selection unit allows the user to select a desired body type. The desired body type includes, for example, a standard body type, an athletic body type, etc. The selection unit allows the user to select from a plurality of models. Step 3: The generation unit generates a training menu based on the information obtained by the acquisition unit and the selection unit. The generation unit uses AI to generate an optimal training menu for the user. The training menu includes exercises such as bodyweight training, running, and yoga. The generation unit can also customize the training menu based on the user's current physical condition and lifestyle. Step 4: The capture unit captures the user's body shape image. The capture unit can capture the user's body shape image on a weekly basis. The capture unit can also estimate the user's emotion and adjust the timing of capturing the body shape image based on the estimated user's emotion. Step 5: The visualization unit visualizes the progress based on the captured body shape image. The visualization unit displays the body shape match rate. The body shape match rate includes changes in body fat percentage, gains and losses in muscle mass, etc. The visualization unit can also display the user's progress in graphs and charts.

[0063] (Example 2) A training support system according to an embodiment of the present invention allows a user to select a desired body type from a model and then use AI to present an optimal training menu based on basic information (height, weight). This training support system allows a user to select a desired body type from a model and input basic information (height, weight), and then AI presents the optimal training menu. The training menu is primarily composed of exercises that can be performed at home or outdoors, eliminating the hassle of going to the gym. Furthermore, users can import images of their body type on a weekly basis, and the AI ​​analyzes the imported images and presents training menus accordingly. Progress can also be visualized using body type match rates and other metrics. This allows users to continue training while checking their progress. This system is offered as a low-cost subscription service, differentiating it from expensive personal gyms and regular gyms. For example, when selecting a desired body type, users can choose from multiple models. Once the user inputs basic information, the AI ​​generates an optimal training menu based on that information. The training menu can include exercises such as bodyweight training, running, and yoga. Users take weekly images of their body shape, and the AI ​​analyzes the images to evaluate their progress. Progress is visualized, for example, through body shape match rates, graphs, and charts. This allows users to see the effectiveness of their training and maintain their motivation. Furthermore, since the system is offered as a low-cost subscription service, users can easily use it. Compared to expensive personal gyms or regular gyms, it allows users to train effectively while keeping costs down. This allows the training support system to generate an optimal training menu based on the user's basic information and desired body shape, and visualize their progress.

[0064] A training support system according to an embodiment includes an acquisition unit, a selection unit, a generation unit, a capture unit, and a visualization unit. The acquisition unit acquires basic information about a user. The basic information includes, but is not limited to, age, gender, height, weight, and health status. The acquisition unit acquires the basic information based on, for example, information input by the user. The acquisition unit can also analyze the user's past health data and select an optimal acquisition method. The selection unit selects a desired body type for the user. The desired body type includes, for example, a standard body type, an athletic body type, and the like, but is not limited to these examples. The selection unit allows the user to select from multiple models. The generation unit generates a training menu based on the information obtained by the acquisition unit and the selection unit. The generation unit generates an optimal training menu for the user using, for example, AI. The training menu includes, for example, exercises such as bodyweight training, running, and yoga, but is not limited to these examples. The generation unit can also customize the training menu based on the user's current physical condition and lifestyle. The capture unit captures an image of the user's body type. The capture unit allows the user to capture body shape images on a weekly basis, for example. The capture unit can also estimate the user's emotions and adjust the timing of capturing body shape images based on the estimated user emotions. The visualization unit visualizes progress based on the captured body shape images. The visualization unit displays, for example, a body shape match rate. The body shape match rate includes, but is not limited to, changes in body fat percentage and increases or decreases in muscle mass. The visualization unit can also display the user's progress in graphs or charts. As a result, the training support system according to the embodiment can generate an optimal training menu based on the user's basic information and desired body shape, and visualize the progress.

[0065] The generation unit can use AI to generate a training menu suitable for the user. The generation unit generates an optimal training menu for the user using AI technologies such as machine learning and deep learning. For example, the generation unit determines the type of exercise, number of repetitions, number of sets, etc. based on the user's basic information and desired body type. The generation unit can also generate an optimal menu by referring to the user's past training history. For example, the generation unit suggests the most effective training menu based on the user's past training history. Furthermore, the generation unit can customize the training menu based on the user's current physical condition and lifestyle. For example, the generation unit analyzes the user's physical condition and lifestyle to generate the most effective training menu. In this way, the use of AI can generate an optimal training menu for the user.

[0066] The generator can configure the training menu mainly based on exercises that can be done at home or outdoors. The generator configures the training menu mainly based on exercises that can be done at home or outdoors, such as bodyweight training, running, and yoga. For example, the generator can include push-ups, squats, planks, and the like as bodyweight training. The generator can also include aerobic exercises such as running and walking. Furthermore, the generator can also include exercises that increase flexibility, such as yoga and stretching. This can eliminate the hassle of going to the gym by providing a training menu that can be done at home or outdoors.

[0067] The capture unit can capture a body image of the user weekly. The capture unit is designed, for example, to allow the user to capture a body image weekly. For example, the capture unit can allow the user to take a body image using a smartphone camera and upload it to the system. The capture unit can also provide a reminder for the user to periodically capture body images. This allows the user to periodically check their progress by capturing body images weekly.

[0068] The visualization unit can display the degree of match of the body type based on the captured body type image. The visualization unit, for example, analyzes the captured body type image and displays the degree of match with the user's desired body type. The degree of match of the body type includes, but is not limited to, changes in body fat percentage, increases or decreases in muscle mass, etc. The visualization unit can display the degree of match of the body type as a percentage, for example. The visualization unit can also visually display the degree of match of the body type in a graph or chart. In this way, the user's progress can be visualized by displaying the body type match rate.

[0069] The visualization unit can display the user's progress in a graph or chart. The visualization unit can display the user's progress, for example, in a line graph, bar graph, pie chart, or the like. For example, the visualization unit can display the change in the user's weight in a line graph. The visualization unit can also display the increase or decrease in the user's muscle mass in a bar graph. Furthermore, the visualization unit can display the change in the user's body fat percentage in a pie chart. In this way, by displaying the progress in a graph or chart, the user can visually check the progress.

[0070] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring the basic information based on the estimated user's emotion. The acquisition unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the acquisition unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The acquisition unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the acquisition unit can analyze the user's text input and estimate the emotion. The acquisition unit adjusts the timing of acquiring the basic information based on the estimated user's emotion. For example, if the user is feeling stressed, the acquisition of basic information can be prompted at a time when the user is able to relax. Furthermore, if the user is relaxed, the acquisition of basic information can be performed immediately. Furthermore, if the user is in a hurry, a simplified input form can be provided to quickly acquire the basic information. In this way, by adjusting the timing of acquiring basic information according to the user's emotion, information can be acquired at a more appropriate time.

[0071] The acquisition unit can analyze the user's past health data and select an appropriate acquisition method. For example, the acquisition unit can suggest the most effective training menu based on the user's past health data. For example, the acquisition unit can analyze the user's past health data and acquire basic information for a specific time period. The acquisition unit can also customize the acquisition method based on the user's past health data. For example, the acquisition unit selects the optimal acquisition method based on the user's past health data. In this way, basic information can be acquired in the optimal method by analyzing the past health data.

[0072] When acquiring the basic information, the acquisition unit can perform filtering based on the user's current lifestyle habits and dietary details. The acquisition unit acquires appropriate basic information, for example, taking into account the user's current lifestyle habits. For example, the acquisition unit can filter necessary basic information based on the user's dietary details. The acquisition unit can also adjust the timing of acquiring the basic information to match the user's lifestyle rhythm. For example, the acquisition unit analyzes the user's lifestyle rhythm and acquires the basic information at the optimal timing. This allows more accurate basic information to be acquired by filtering the information based on the user's lifestyle habits and dietary details.

[0073] The acquisition unit can estimate the user's emotion and determine the priority of the basic information to be acquired based on the estimated user's emotion. The acquisition unit estimates the user's emotion using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the acquisition unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The acquisition unit can also record the user's voice and estimate the emotion using voice analysis technology. Furthermore, the acquisition unit can analyze the user's text input and estimate the emotion. The acquisition unit determines the priority of the basic information to be acquired based on the estimated user's emotion. For example, if the user is feeling stressed, important basic information can be acquired with priority. Furthermore, if the user is relaxed, detailed basic information can be acquired. Furthermore, if the user is in a hurry, the most necessary basic information can be acquired with priority. In this way, by determining the priority of the basic information according to the user's emotion, important information can be acquired with priority.

[0074] When acquiring basic information, the acquisition unit can prioritize acquiring highly relevant information based on the user's geographical location information. For example, when the user is in a specific area, the acquisition unit prioritizes acquiring basic information related to that area. For example, the acquisition unit can acquire optimal basic information based on the user's geographical location information. Furthermore, when the user is traveling, the acquisition unit can also prioritize acquiring basic information related to the travel destination. In this way, highly relevant information can be prioritized by taking geographical location information into consideration.

[0075] When acquiring the basic information, the acquisition unit can analyze the user's social media activity and collect related information. The acquisition unit, for example, acquires the related basic information based on the user's social media activity. For example, the acquisition unit can acquire the basic information by referring to information shared by the user on social media. The acquisition unit can also analyze the user's social media activity and acquire optimal basic information. In this way, related information can be acquired by analyzing social media activity.

[0076] The selection unit can estimate the user's emotions and adjust the body model selection method based on the estimated user's emotions. The selection unit can estimate the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the selection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The selection unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the selection unit can analyze the user's text input and estimate the emotions. The selection unit adjusts the body model selection method based on the estimated user's emotions. For example, if the user is feeling stressed, a simple body model can be suggested. If the user is relaxed, a detailed body model can be suggested. Furthermore, if the user is in a hurry, the most popular body model can be suggested preferentially. In this way, by adjusting the body model selection method according to the user's emotions, a more appropriate model can be suggested.

[0077] When making a selection, the selection unit can suggest an appropriate model by referring to the user's past body shape change data. The selection unit, for example, suggests an optimal body shape model based on the user's past body shape change data. For example, the selection unit can analyze the user's past body shape change data and suggest the most effective body shape model. The selection unit can also suggest a customized body shape model by referring to the user's past body shape change data. This makes it possible to suggest an optimal body shape model by referring to the past body shape change data.

[0078] At the time of selection, the selection unit can customize the body type model based on the user's current health condition. For example, the selection unit can propose an optimal body type model taking into account the user's current health condition. For example, the selection unit can customize the body type model based on the user's health condition. The selection unit can also analyze the user's health condition and propose the most effective body type model. In this way, by customizing the body type model based on the user's current health condition, a more appropriate model can be proposed.

[0079] The selection unit can estimate the user's emotions and prioritize body models based on the estimated user emotions. The selection unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the selection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The selection unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the selection unit can analyze the user's text input and estimate the emotions. The selection unit determines the priority of body models based on the estimated user emotions. For example, if the user is feeling stressed, a simple body model can be preferentially suggested. If the user is relaxed, a detailed body model can be preferentially suggested. Furthermore, if the user is in a hurry, the most popular body model can be preferentially suggested. In this way, by prioritizing body models according to the user's emotions, important models can be preferentially suggested.

[0080] At the time of selection, the selection unit can suggest an appropriate body type model based on the user's geographical location information. For example, if the user is in a specific area, the selection unit can suggest a body type model related to that area. For example, the selection unit can suggest an optimal body type model based on the user's geographical location information. Furthermore, if the user is traveling, the selection unit can also suggest a body type model related to the user's travel destination. In this way, the optimal body type model can be suggested by taking the geographical location information into consideration.

[0081] At the time of selection, the selection unit can analyze the user's social media activity and suggest a related body model. The selection unit can suggest a related body model based on, for example, the user's social media activity. For example, the selection unit can suggest a body model based on information shared by the user on social media. The selection unit can also analyze the user's social media activity and suggest an optimal body model. In this way, related body models can be suggested by analyzing social media activity.

[0082] The generation unit can estimate the user's emotions and adjust a training menu generation method based on the estimated user's emotions. The generation unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the generation unit can analyze the user's text input and estimate the emotions. The generation unit adjusts the training menu generation method based on the estimated user's emotions. For example, if the user is relaxed, a training menu that progresses at a leisurely pace can be generated. If the user is in a hurry, a training menu that is effective in a short amount of time can be generated. Furthermore, if the user is excited, a training menu that includes visually stimulating effects can be generated. In this way, by adjusting the training menu generation method according to the user's emotions, a more appropriate menu can be provided.

[0083] When generating the training menu, the generation unit can generate an appropriate menu by referring to the user's past training history. The generation unit generates an optimal training menu based on, for example, the user's past training history. For example, the generation unit can analyze the user's past training history and generate the most effective training menu. The generation unit can also generate a customized training menu by referring to the user's past training history. In this way, an optimal training menu can be generated by referring to the past training history.

[0084] The generation unit can customize the training menu based on the user's current physical condition and lifestyle rhythm when generating the menu. The generation unit generates an optimal training menu, for example, taking into account the user's current physical condition. For example, the generation unit can customize the training menu based on the user's lifestyle rhythm. The generation unit can also analyze the user's physical condition and lifestyle rhythm to generate the most effective training menu. This makes it possible to provide a more effective menu by customizing the training menu based on the user's current physical condition and lifestyle rhythm.

[0085] The generation unit can estimate the user's emotions and determine the priority of training menus based on the estimated user's emotions. The generation unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the generation unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the generation unit can analyze the user's text input and estimate the emotions. The generation unit determines the priority of training menus based on the estimated user's emotions. For example, if the user is feeling stressed, a relaxing training menu can be preferentially suggested. Furthermore, if the user is relaxed, a detailed training menu can be preferentially suggested. Furthermore, if the user is in a hurry, a short and effective training menu can be preferentially suggested. In this way, by determining the priority of training menus according to the user's emotions, important menus can be provided preferentially.

[0086] The generation unit can generate an appropriate training menu based on the user's geographical location information at the time of generation. For example, if the user is in a specific area, the generation unit generates a training menu related to that area. For example, the generation unit can generate an optimal training menu based on the user's geographical location information. Furthermore, if the user is traveling, the generation unit can also generate a training menu related to the user's travel destination. In this way, an optimal training menu can be generated by taking the geographical location information into consideration.

[0087] At the time of generation, the generation unit can analyze the user's social media activity and generate a related training menu. The generation unit generates the related training menu based on, for example, the user's social media activity. For example, the generation unit can generate the training menu by referring to information shared by the user on social media. The generation unit can also analyze the user's social media activity and generate an optimal training menu. In this way, a related training menu can be generated by analyzing social media activity.

[0088] The capture unit can estimate the user's emotions and adjust the timing of capturing body shape images based on the estimated user emotions. The capture unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the capture unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The capture unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the capture unit can analyze the user's text input and estimate the emotions. The capture unit adjusts the timing of capturing body shape images based on the estimated user emotions. For example, if the user is feeling stressed, the capture unit can prompt the user to capture body shape images at a time when the user is relaxed. Furthermore, if the user is relaxed, the body shape images can be captured immediately. Furthermore, if the user is in a hurry, the body shape images can be captured using a simplified procedure. As a result, by adjusting the timing of capturing body shape images according to the user's emotions, the images can be captured at a more appropriate time.

[0089] The capture unit can select an appropriate capture method by referring to the user's past body images during capture. The capture unit can, for example, suggest an optimal capture method based on the user's past body images. For example, the capture unit can analyze the user's past body images and select the most effective capture method. The capture unit can also suggest a customized capture method by referring to the user's past body images. This allows the image to be captured in the optimal method by referring to the user's past body images.

[0090] The capture unit can customize the capture of body shape images based on the user's current living situation during capture. For example, the capture unit can consider the user's current living situation and propose an optimal body shape image capture method. For example, the capture unit can adjust the timing of body shape image capture to match the user's daily rhythm. The capture unit can also analyze the user's living situation and select the most effective body shape image capture method. This allows for more effective image capture by customizing image capture based on the user's current living situation.

[0091] The capture unit can estimate the user's emotions and determine the priority of body type images to be captured based on the estimated user's emotions. The capture unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the capture unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The capture unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the capture unit can analyze the user's text input and estimate the emotions. The capture unit determines the priority of body type images to be captured based on the estimated user's emotions. For example, if the user is feeling stressed, important body type images can be captured with priority. Furthermore, if the user is relaxed, detailed body type images can be captured with priority. Furthermore, if the user is in a hurry, the most necessary body type images can be captured with priority. In this way, by determining the priority of body type images according to the user's emotions, important images can be captured with priority.

[0092] The capture unit can capture an appropriate body type image based on the user's geographical location information during capture. For example, if the user is in a specific area, the capture unit prioritizes capturing body type images related to that area. For example, the capture unit can capture an optimal body type image based on the user's geographical location information. Furthermore, if the user is traveling, the capture unit can also prioritize capturing body type images related to the travel destination. This allows the optimal body type image to be captured by taking the geographical location information into consideration.

[0093] During the import process, the import unit can analyze the user's social media activity and import related body shape images. The import unit imports related body shape images based on, for example, the user's social media activity. For example, the import unit can import body shape images with reference to information shared by the user on social media. The import unit can also analyze the user's social media activity and import optimal body shape images. In this way, related body shape images can be imported by analyzing social media activity.

[0094] The visualization unit can estimate the user's emotions and adjust the progress display method based on the estimated user's emotions. The visualization unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the visualization unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The visualization unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the visualization unit can analyze the user's text input and estimate the emotions. The visualization unit adjusts the progress display method based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, by adjusting the progress display method according to the user's emotions, a more appropriate display method can be provided.

[0095] When visualizing, the visualization unit can select an appropriate display method by referring to the user's past progress data. The visualization unit, for example, proposes an optimal display method based on the user's past progress data. For example, the visualization unit can analyze the user's past progress data and select the most effective display method. The visualization unit can also propose a customized display method by referring to the user's past progress data. In this way, the optimal display method can be provided by referring to the past progress data.

[0096] The visualization unit can customize the progress display based on the user's current goal when visualizing. For example, the visualization unit considers the user's current goal and proposes an optimal progress display method. For example, the visualization unit can customize the progress display based on the user's goal. The visualization unit can also analyze the user's goal and select the most effective progress display method. In this way, customizing the progress display based on the current goal can provide a more effective display.

[0097] The visualization unit can estimate the user's emotions and determine the priority of progress based on the estimated user's emotions. The visualization unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, the visualization unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The visualization unit can also record the user's voice and estimate the emotions using voice analysis technology. Furthermore, the visualization unit can analyze the user's text input and estimate the emotions. The visualization unit determines the priority of progress based on the estimated user's emotions. For example, if the user is feeling stressed, important progress information can be displayed with priority. If the user is relaxed, detailed progress information can be displayed. Furthermore, if the user is in a hurry, the most necessary progress information can be displayed with priority. In this way, by determining the priority of progress based on the user's emotions, important information can be displayed with priority.

[0098] When visualizing, the visualization unit can display an appropriate progress display based on the user's geographical location information. For example, if the user is in a specific area, the visualization unit will prioritize displaying progress information related to that area. For example, the visualization unit can display an optimal progress display based on the user's geographical location information. Furthermore, if the user is traveling, the visualization unit can also prioritize displaying progress information related to the user's travel destination. In this way, the optimal progress display can be provided by taking the geographical location information into consideration.

[0099] During visualization, the visualization unit can analyze the user's social media activity and display related progress. The visualization unit, for example, displays related progress information based on the user's social media activity. For example, the visualization unit can display progress by referring to information shared by the user on social media. The visualization unit can also analyze the user's social media activity and display an optimal progress display. In this way, a related progress display can be provided by analyzing social media activity. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned acquisition unit, selection unit, generation unit, capture unit, and visualization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires basic information about the user using the reception device 38 of the smart device 14. The selection unit selects the user's desired body type using the display 40A of the smart device 14. The generation unit generates a training menu using the specific processing unit 290 of the data processing device 12. The capture unit captures an image of the user's body type using the camera 42 of the smart device 14. The visualization unit visualizes progress using the display 40A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned acquisition unit, selection unit, generation unit, capture unit, and visualization unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires basic information about the user using the microphone 238 of the smart glasses 214. The selection unit selects the user's desired body type using the display of the smart glasses 214. The generation unit generates a training menu using the specific processing unit 290 of the data processing device 12. The capture unit captures an image of the user's body type using the camera 42 of the smart glasses 214. The visualization unit visualizes the progress using the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, selection unit, generation unit, capture unit, and visualization unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit acquires basic information about the user using the microphone 238 of the headset type terminal 314. The selection unit selects the body type the user is aiming for using the display 343 of the headset type terminal 314. The generation unit generates a training menu using the specific processing unit 290 of the data processing device 12. The capture unit captures an image of the user's body type using the camera 42 of the headset type terminal 314. The visualization unit visualizes progress using the display 343 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned acquisition unit, selection unit, generation unit, capture unit, and visualization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires basic information about the user using the microphone 238 of the robot 414. The selection unit selects the body type the user is aiming for using the display of the robot 414. The generation unit generates a training menu using the specific processing unit 290 of the data processing device 12. The capture unit captures an image of the user's body type using the camera 42 of the robot 414. The visualization unit visualizes progress using the display of the robot 414.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The training support system further includes a reward unit. The reward unit can provide a reward when the user achieves a set goal. For example, the reward unit can award a badge or points when the user approaches a target body shape. The reward unit can also provide benefits or coupons when the user continues training for a certain period of time. Furthermore, the reward unit can provide a special reward when the user competes with other users and ranks highly. This allows the user to maintain motivation by receiving rewards and continue training.

[0102] The generation unit can acquire the user's dietary data and reflect it in the training menu. For example, the generation unit can analyze the calorie intake and nutritional balance by having the user input the details of their meals, and adjust the training menu accordingly. The generation unit can also automatically recognize the details of meals using image analysis technology by having the user upload photos of their meals, and reflect the details in the training menu. Furthermore, the generation unit can also suggest an optimal meal and training schedule by having the user input the timing of meals. This allows the user to achieve a good balance between meals and training.

[0103] The generation unit can acquire the user's sleep data and reflect it in the training menu. For example, the generation unit can adjust the training menu by having the user input the amount of sleep and the quality of sleep. The generation unit can also automatically acquire sleep data using a wearable device by the user and reflect it in the training menu. Furthermore, the generation unit can also suggest the optimal training time by having the user input their sleep rhythm. This allows the user to achieve a good balance between sleep and training.

[0104] The capture unit can capture a user's body image using 3D scanning technology. For example, the capture unit can allow the user to scan their entire body using a smartphone camera and generate a 3D model. The capture unit can also allow the user to capture body images using a dedicated 3D scanner. Furthermore, by allowing the user to perform 3D scans periodically, the capture unit can record changes in body shape in detail and reflect them in training menus. This allows the user to train based on more accurate body shape data.

[0105] The visualization unit can display the user's progress using AR (augmented reality) technology. For example, the visualization unit can allow the user to take a photo of their own body shape using a smartphone camera and then use AR technology to display a comparison with the target body shape in real time. The visualization unit can also allow the user to check their progress using AR glasses. Furthermore, the visualization unit can also use AR technology to confirm the correct form while the user is training. This allows the user to train while visually checking their progress.

[0106] The acquisition unit can estimate the user's emotions and adjust the difficulty level of the training menu based on the estimated user's emotions. For example, the acquisition unit can lower the difficulty level of the training menu when the user is feeling stressed. Furthermore, the acquisition unit can increase the difficulty level of the training menu when the user is relaxed. Furthermore, the acquisition unit can add a new exercise to the training menu when the user is excited. In this way, by adjusting the difficulty level of the training menu according to the user's emotions, more effective training can be provided.

[0107] The selection unit can estimate the user's emotions and adjust the body model suggestion method based on the estimated user's emotions. For example, the selection unit can suggest a simple body model when the user is feeling stressed. Alternatively, the selection unit can suggest a detailed body model when the user is relaxed. Furthermore, the selection unit can preferentially suggest the most popular body model when the user is in a hurry. In this way, by adjusting the body model suggestion method according to the user's emotions, more appropriate models can be suggested.

[0108] The generation unit can estimate the user's emotions and provide training menu feedback based on the estimated user's emotions. For example, the generation unit can display an encouraging message when the user is feeling stressed. The generation unit can also provide detailed feedback when the user is relaxed. Furthermore, the generation unit can also provide challenging feedback when the user is excited. In this way, by providing training menu feedback according to the user's emotions, motivation can be maintained.

[0109] The capture unit can estimate the user's emotions and adjust the body image capture method based on the estimated user's emotions. For example, if the user is feeling stressed, the capture unit can capture body images using a simplified procedure. Alternatively, if the user is relaxed, the capture unit can capture body images using a detailed procedure. Furthermore, if the user is in a hurry, the capture unit can prioritize capturing the most necessary body images. In this way, by adjusting the body image capture method according to the user's emotions, images can be captured at a more appropriate time.

[0110] The visualization unit can estimate the user's emotions and adjust the progress display method based on the estimated user emotions. For example, if the user is nervous, the visualization unit can provide a simple, highly visible display method. If the user is relaxed, the visualization unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the visualization unit can also provide a display method that focuses on the main points. In this way, by adjusting the progress display method according to the user's emotions, a more appropriate display method can be provided.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The acquisition unit acquires basic information about the user. The basic information includes, for example, age, gender, height, weight, and health condition. The acquisition unit acquires the basic information based on information entered by the user. The acquisition unit can also analyze the user's past health data and select the optimal acquisition method. Step 2: The selection unit allows the user to select a desired body type. The desired body type includes, for example, a standard body type, an athletic body type, etc. The selection unit allows the user to select from a plurality of models. Step 3: The generation unit generates a training menu based on the information obtained by the acquisition unit and the selection unit. The generation unit uses AI to generate an optimal training menu for the user. The training menu includes exercises such as bodyweight training, running, and yoga. The generation unit can also customize the training menu based on the user's current physical condition and lifestyle. Step 4: The capture unit captures the user's body shape image. The capture unit can capture the user's body shape image on a weekly basis. The capture unit can also estimate the user's emotion and adjust the timing of capturing the body shape image based on the estimated user's emotion. Step 5: The visualization unit visualizes the progress based on the captured body shape image. The visualization unit displays the body shape match rate. The body shape match rate includes changes in body fat percentage, gains and losses in muscle mass, etc. The visualization unit can also display the user's progress in graphs and charts.

[0113] 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.

[0114] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 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 acquisition unit for acquiring basic information of a user; A selection section for selecting the desired body type; a generation unit that generates a training menu based on the information obtained by the acquisition unit and the selection unit; an acquisition unit that acquires a body shape image of a user; a visualization unit that visualizes progress based on the body shape image captured by the capture unit; A system characterized by:

2. The generation unit Generate a training menu suited to the user using AI 2. The system of claim 1.

3. The generation unit Focus on training that can be done at home or outdoors 2. The system of claim 1.

4. The capture unit is Capture user body images weekly 2. The system of claim 1.

5. The visualization unit Displaying the degree of body shape matching based on the captured body shape image 2. The system of claim 1.

6. The visualization unit View user progress in graphs or charts 2. The system of claim 1.

7. The acquisition unit To estimate a user's emotions and adjust the timing of acquiring basic information based on the estimated user's emotions.

2. The system of claim 1.

8. The acquisition unit Analyze the user's past health data and select the appropriate acquisition method 2. The system of claim 1.

9. The acquisition unit When acquiring basic information, filtering is performed based on the user's current lifestyle and dietary habits.

2. The system of claim 1.

Images