system

Abstract

The system according to this embodiment aims to analyze the user's body shape information and propose an individualized muscle training menu. [Solution] The system according to the embodiment comprises a reception unit, a suggestion unit, and a goal presentation unit. The reception unit receives the user's body shape information. The suggestion unit analyzes the information received by the reception unit and proposes an individualized muscle training menu. The goal presentation unit presents an ideal body shape as a goal based on the muscle training menu proposed by the suggestion unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, information analysis for proposing individual exercise menus has not been sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the user's body type information and propose an individual exercise menu.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a proposal unit, and a target presentation unit. The reception unit inputs the user's body type information. The proposal unit analyzes the information input by the reception unit and proposes an individual exercise menu. The target presentation unit presents an ideal body type as a target based on the exercise menu proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's body shape information and suggest a personalized muscle training menu. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) The training support system according to an embodiment of the present invention is an application equipped with a generative AI agent for health-conscious individuals to easily perform training at home. This training support system allows users to input their body type information and take photos of their bodies, after which the generative AI analyzes this information and proposes a personalized muscle training menu. Furthermore, the generative AI presents an ideal body type as a goal to increase user motivation. This application utilizes multimodal AI, enabling simultaneous processing of image and text information. This aims to realize a society where everyone can easily receive personalized training. For example, a user inputs their body type information and takes photos of their bodies. At this time, the user inputs information such as their weight, height, and body fat percentage. For example, a user inputs "weight 70kg, height 175cm, body fat percentage 20%" and takes a photo of their body. This information is input to the generative AI. Next, the generative AI analyzes the input information and proposes a personalized muscle training menu. The generative AI generates an optimal training menu based on the user's body type information and photos. For example, if a user wants to train their abdominal muscles, the generative AI proposes an effective abdominal training menu. This allows the user to perform training that suits them. Furthermore, the generating AI presents an ideal physique as a goal. The generating AI compares the user's current physique information with their target physique information and presents a goal to be achieved. For example, if a user's goal is "15% body fat," the generating AI will suggest a training menu to achieve that goal and monitor their progress. This app makes it easy for users to train at home. It also encourages exercise through push notifications and boosts motivation through interactive training consultations. This allows even those who are reluctant to go to the gym or who don't know how to train to train effectively. As a result, the training support system can suggest individual strength training menus based on the user's physique information and present an ideal physique as a goal.

[0029] The training support system according to the embodiment comprises a reception unit, a suggestion unit, and a goal presentation unit. The reception unit inputs the user's body type information. The user's body type information includes, but is not limited to, height, weight, and body fat percentage. For example, the user can input information such as their weight, height, and body fat percentage into the reception unit. The suggestion unit analyzes the information input by the reception unit and proposes an individualized muscle training menu. The suggestion unit generates an optimal training menu based on the user's body type information and a photograph, for example, using a generation AI. For example, if the user wants to train their abdominal muscles, the generation AI can propose a training menu that is effective for abdominal muscles. Some or all of the above processing in the suggestion unit is performed using the generation AI. The goal presentation unit presents an ideal body type as a goal based on the muscle training menu proposed by the suggestion unit. For example, the goal presentation unit compares the user's current body type information with the target body type information using a generation AI and presents a goal to be achieved. The generating AI can, for example, suggest a training menu to achieve a target body fat percentage of 15% if the user sets that goal, and monitor the user's progress. Some or all of the above-mentioned processing in the goal presentation unit is performed using the generating AI. As a result, the training support system according to this embodiment can suggest an individualized muscle training menu based on the user's body shape information and present an ideal body shape as a target.

[0030] The reception desk inputs the user's body type information. This information includes, but is not limited to, height, weight, and body fat percentage. For example, the reception desk allows users to input their own weight, height, and body fat percentage. Specifically, the reception desk provides an interface for users to input body type information using a dedicated application or website via a device such as a smartphone, tablet, or PC. Users can input detailed information such as height, weight, body fat percentage, age, gender, and exercise experience according to the input form. Furthermore, the reception desk has a function to automatically save the information entered by the user and store it in a database for later reference. This eliminates the need for users to re-enter information they have already entered. The reception desk also has a check function to verify the accuracy of the information entered by the user. For example, if the height or weight range is abnormally outside the normal range, it can display a message prompting the user to reconfirm. This allows the reception desk to collect user body type information accurately and efficiently, improving the overall reliability of the system.

[0031] The suggestion department analyzes the information entered by the reception department and proposes individualized muscle training menus. For example, the suggestion department uses a generative AI to generate an optimal training menu based on the user's body type information and photos. If the user wants to train their abdominal muscles, the generative AI can propose an effective abdominal training menu. Specifically, the generative AI receives the user's body type information as input data and generates an optimal training menu based on past training data and expert knowledge. The generative AI uses deep learning technology to automatically customize the training menu according to the user's body type and goals. For example, if the user wants to train their abdominal muscles, the generative AI will propose a menu that combines exercises effective for the abdominal muscles, such as crunches, planks, and leg raises. The generative AI can also adjust the intensity and number of repetitions of the training according to the user's exercise experience and fitness level. Furthermore, the suggestion department has an interface to provide the user with the training menu proposed by the generative AI. The user can check and execute the proposed training menu via their smartphone or tablet. This allows the suggestion department to propose personalized muscle training menus based on the user's body type information, providing support to help users train effectively.

[0032] The goal-setting unit presents an ideal physique as a target based on the strength training menu proposed by the suggestion unit. For example, using a generating AI, the goal-setting unit compares the user's current physique information with their target physique information and presents a goal to be achieved. For instance, if a user aims for a "body fat percentage of 15%", the generating AI can propose a training menu to achieve that goal and monitor progress. Specifically, based on the user's current physique information, the generating AI presents concrete steps to reach the target physique. For example, if a user wants to reduce their body fat percentage, the generating AI will propose specific action plans, such as improving diet or adding aerobic exercise. The goal-setting unit also has a function to provide regular feedback so that users can check their progress towards their goals. Users can check their progress and maintain motivation towards their goals via their smartphones or tablets. Furthermore, the goal-setting unit also provides support for setting the next goal once the user has achieved their previous goal. In this way, the goal-setting unit can provide motivation for users to continue training towards their ideal physique, supporting the improvement of their health and fitness.

[0033] The notification unit can encourage exercise through push notifications. For example, the notification unit can send push notifications during the time when the user is likely to exercise, thereby encouraging exercise. This allows the system to encourage users to exercise through push notifications.

[0034] The consultation department can conduct training consultations in a conversational format. For example, the consultation department can use a chatbot to conduct training consultations with users in a conversational format. For example, the consultation department can use voice chat to conduct training consultations with users in a conversational format. For example, the consultation department can use video calls to conduct training consultations with users in a conversational format. This allows for increased user motivation through conversational training consultations.

[0035] The suggestion function can generate an optimal training menu based on the user's body shape information and photos. For example, the suggestion function uses a generation AI to generate an optimal training menu based on the user's body shape information and photos. For example, if the user wants to train their abdominal muscles, the generation AI can suggest an effective training menu for the abdominal muscles. For example, if the user wants to train their arm muscles, the generation AI can suggest an effective training menu for the arm muscles. For example, if the user wants to train their leg muscles, the generation AI can suggest an effective training menu for the leg muscles. In this way, an optimal training menu can be generated based on the user's body shape information and photos.

[0036] The goal-setting unit can compare the user's current body shape information with their target body shape information and present goals to be achieved. For example, the goal-setting unit uses a generation AI to compare the user's current body shape information with their target body shape information and present goals to be achieved. For example, if the user's goal is "15% body fat," the generation AI can suggest a training menu to achieve that goal and monitor the user's progress. For example, if the user's goal is "to increase muscle mass," the generation AI can suggest a training menu to achieve that goal and monitor the user's progress. For example, if the user's goal is "to lose weight," the generation AI can suggest a training menu to achieve that goal and monitor the user's progress. This allows the system to compare the user's current body shape information with their target body shape information and present goals to be achieved.

[0037] The reception desk can analyze the user's past body shape information input history and select the optimal input method. For example, the reception desk may prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk may analyze the time periods in which the user has previously entered information and suggest the optimal input timing. For example, the reception desk may predict and suggest an input method to be used at a specific time period based on the user's past input history. In this way, the optimal input method can be selected by analyzing the user's past body shape information input history. Some or all of the above processing in the reception desk may be performed using AI or not.

[0038] The reception unit can filter the input of body shape information based on the user's current health status and lifestyle. For example, the reception unit adjusts input fields based on the user's current health status (weight, body fat percentage, etc.). For example, the reception unit adjusts input fields based on the user's lifestyle (diet, exercise, etc.). For example, the reception unit determines the priority of input fields based on the user's health status and lifestyle. This allows for the input of more appropriate information by filtering based on the user's current health status and lifestyle. Some or all of the above processing in the reception unit may be performed using AI or not.

[0039] The reception desk can prioritize inputting highly relevant information based on the user's geographical location when entering body type information. For example, if the user is at a gym, the reception desk will prioritize inputting body type information related to gym training. For example, if the user is at home, the reception desk will prioritize inputting body type information related to home training. For example, if the user is out, the reception desk will prioritize inputting body type information related to training at their destination. By prioritizing the input of highly relevant information based on the user's geographical location, more appropriate information can be entered. Some or all of the above processing in the reception desk may be performed using AI, or it may be performed without using AI.

[0040] The reception desk can analyze the user's social media activity and input relevant information when the user enters body type information. For example, the reception desk may prompt the user to enter body type information based on training information shared on social media. For example, the reception desk may prompt the user to enter body type information based on information from training accounts followed on social media. For example, the reception desk may prompt the user to enter body type information based on information from training communities the user participates in on social media. This allows the reception desk to input relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not.

[0041] The proposal unit can adjust the level of detail in its proposals based on the importance of the training menus. For example, the proposal unit's generating AI will produce proposals with detailed explanations for important training menus. For example, the proposal unit's generating AI will produce proposals with concise explanations for less important training menus. The proposal unit can adjust the level of detail in its proposals in stages according to the importance of the training menus. This allows for more appropriate proposals to be made by adjusting the level of detail in the proposals based on the importance of the training menus. Some or all of the above processing in the proposal unit may be performed using the generating AI, or it may be performed without using the generating AI.

[0042] The suggestion unit can apply different suggestion algorithms depending on the category of the training menu when making suggestions. For example, for strength training menus, the suggestion unit's generating AI applies a suggestion algorithm specialized in improving strength. For example, for aerobic exercise menus, the suggestion unit's generating AI applies a suggestion algorithm specialized in improving endurance. For example, for flexibility improvement menus, the suggestion unit's generating AI applies a suggestion algorithm specialized in improving flexibility. By applying different suggestion algorithms depending on the category of the training menu, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using the generating AI, or it may be performed without using the generating AI.

[0043] The proposal department can determine the priority of proposals based on the submission timing of the training menus. For example, the proposal department can have the AI ​​generate proposals preferentially for training menus with earlier submission dates, and postpone proposals for training menus with later submission dates. The proposal department can adjust the priority of proposals in stages according to the submission dates. This allows for more appropriate proposals to be made by determining the priority of proposals based on the submission dates of the training menus. Some or all of the above processing in the proposal department may be performed using the AI, or it may be performed without the AI.

[0044] The proposal unit can adjust the order of proposals based on the relevance of the training menus. For example, the proposal unit's generating AI will prioritize proposals for highly relevant training menus. For example, the proposal unit's generating AI will postpone proposals for less relevant training menus. The proposal unit can adjust the order of proposals in stages according to the relevance of the training menus. By adjusting the order of proposals based on the relevance of the training menus, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using the generating AI, or it may be performed without using the generating AI.

[0045] The goal presentation unit can predict the current goal by referring to past goal data when presenting a goal. For example, the goal presentation unit predicts the current goal based on the user's past goal data. For example, the goal presentation unit predicts the current goal by analyzing the user's past goal achievement status. For example, the goal presentation unit presents the optimal goal by referring to the user's past goal data. In this way, the current goal can be predicted by referring to past goal data. Some or all of the above processing in the goal presentation unit may be performed using generative AI, or it may be performed without using generative AI.

[0046] The goal presentation unit can apply different goal presentation methods to each category of training menu when presenting goals. For example, for strength training menus, the goal presentation unit's generating AI applies a goal presentation method specialized for improving strength. For example, for aerobic exercise menus, the goal presentation unit's generating AI applies a goal presentation method specialized for improving endurance. For example, for flexibility improvement menus, the goal presentation unit's generating AI applies a goal presentation method specialized for improving flexibility. By applying different goal presentation methods to each category of training menus, more appropriate goals can be presented. Some or all of the above processing in the goal presentation unit may be performed using the generating AI, or it may be performed without using the generating AI.

[0047] The goal presentation unit can analyze changes in goals based on the submission timing of training menus when presenting goals. For example, the goal presentation unit prioritizes analyzing goal changes for training menus submitted earlier. For example, the goal presentation unit postpones analyzing goal changes for training menus submitted later. For example, the goal presentation unit analyzes goal changes in stages according to the submission timing. This allows for the presentation of more appropriate goals by analyzing goal changes based on the submission timing of training menus. Some or all of the above processing in the goal presentation unit may be performed using generative AI, or it may be performed without using generative AI.

[0048] The goal presentation unit can analyze goals by referring to relevant market data for the training menu when presenting goals. For example, the goal presentation unit analyzes goals based on relevant market data for the training menu. For example, the goal presentation unit analyzes goals by referring to market trends for the training menu. For example, the goal presentation unit presents optimal goals by referring to relevant market data for the training menu. This makes it possible to present more appropriate goals by referring to relevant market data for the training menu. Some or all of the above processing in the goal presentation unit may be performed using generative AI, or it may be performed without using generative AI.

[0049] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may prioritize suggesting notification methods that the user has previously preferred to receive (e.g., push notifications, email). For example, the notification unit may predict and suggest a notification method to send at a specific time based on the user's past notification history. For example, the notification unit may analyze the user's past notification history and select the most effective notification method. In this way, the optimal notification method can be selected by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI or not.

[0050] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit will prioritize sending push notifications. For example, if the user is using a tablet, the notification unit will prioritize sending email notifications. For example, if the user is using a smartwatch, the notification unit will prioritize sending vibration notifications. In this way, the optimal notification method can be selected by considering the user's device information. Some or all of the above processing in the notification unit may be performed using AI or not.

[0051] The consultation department can select the most suitable consultation method by referring to the user's past consultation history during a consultation. For example, the consultation department may prioritize suggesting consultation methods that the user has preferred in the past (chat, voice, etc.). For example, the consultation department may predict and suggest consultation methods to be used at a specific time based on the user's past consultation history. For example, the consultation department may analyze the user's past consultation history and select the most effective consultation method. In this way, the optimal consultation method can be selected by referring to the user's past consultation history. Some or all of the above processes in the consultation department may be performed using AI or not.

[0052] The consultation department can select the most suitable consultation method by considering the user's device information during a consultation. For example, if the user is using a smartphone, the consultation department will prioritize chat consultations. For example, if the user is using a tablet, the consultation department will prioritize video consultations. For example, if the user is using a smartwatch, the consultation department will prioritize voice consultations. In this way, the consultation department can select the most suitable consultation method by considering the user's device information. Some or all of the above processing in the consultation department may be performed using AI or not.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The training support system can also suggest dietary management based on the user's body type information. The suggestion unit can propose appropriate meal plans based on the user's body type information and goals. For example, if the user wants to increase muscle mass, it can suggest a meal plan high in protein. If the user wants to reduce body fat, it can suggest a low-calorie, nutritionally balanced meal plan. Furthermore, if the user has a specific allergy, it can suggest a meal plan that accommodates that allergy. This allows users to aim for a healthy physique through both training and diet.

[0055] The training support system can also suggest sleep management based on the user's body type information. The suggestion section can propose an appropriate sleep schedule based on the user's body type and goals. For example, if a user wants to increase muscle mass, it can suggest a schedule to ensure sufficient sleep. If a user wants to reduce body fat, it can provide advice on getting quality sleep. Furthermore, if a user has an irregular lifestyle, it can suggest a regular sleep schedule. This allows users to aim for a healthy physique through both training and sleep.

[0056] The training support system can also offer stress management suggestions based on the user's body type information. The suggestion function can propose appropriate stress relief methods based on the user's body type and goals. For example, if a user wants to increase muscle mass, it can suggest relaxing yoga or meditation routines. If a user wants to reduce body fat, it can suggest stress-reducing activities. Furthermore, if a user is experiencing stress at work or in their daily life, it can offer counseling and support for stress management. This allows users to aim for a healthy physique through both training and stress management.

[0057] The training support system can also suggest supplements to maximize the effects of exercise based on the user's body type information. The suggestion unit can recommend appropriate supplements based on the user's body type and goals. For example, if the user wants to increase muscle mass, it can suggest supplements such as protein and BCAAs. If the user wants to reduce body fat, it can suggest supplements that aid in fat burning. Furthermore, if the user feels low on energy, it can suggest supplements for energy replenishment. This allows users to aim for a healthy physique through both training and supplements.

[0058] The training support system can provide a function to visualize training progress based on the user's body type information. The goal presentation unit can collect the user's training data and visualize progress using graphs and charts. For example, if the user wants to increase muscle mass, a graph showing the increase in muscle mass can be displayed. Similarly, if the user wants to reduce body fat, a chart showing the decrease in body fat percentage can be displayed. Furthermore, if the user achieves a specific training goal, the achievement status can be visually displayed. This allows the user to check their training progress at a glance and maintain their motivation.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk enters the user's body type information. This information includes, for example, height, weight, and body fat percentage. Users can enter their own weight, height, body fat percentage, and other information. Step 2: The suggestion department analyzes the information entered by the reception department and proposes a personalized muscle training menu. The suggestion department uses a generation AI to generate the optimal training menu based on the user's body type information and photos. For example, if the user wants to train their abdominal muscles, the suggestion department can propose an effective abdominal training menu. Step 3: The goal presentation unit presents an ideal physique as a target based on the muscle training menu proposed by the suggestion unit. The goal presentation unit uses a generating AI to compare the user's current physique information with the target physique information and presents a goal to be achieved. For example, if the user's goal is "15% body fat," the unit can propose a training menu to achieve that goal and monitor the user's progress.

[0061] (Example of form 2) The training support system according to an embodiment of the present invention is an application equipped with a generative AI agent for health-conscious individuals to easily perform training at home. This training support system allows users to input their body type information and take photos of their bodies, after which the generative AI analyzes this information and proposes a personalized muscle training menu. Furthermore, the generative AI presents an ideal body type as a goal to increase user motivation. This application utilizes multimodal AI, enabling simultaneous processing of image and text information. This aims to realize a society where everyone can easily receive personalized training. For example, a user inputs their body type information and takes photos of their bodies. At this time, the user inputs information such as their weight, height, and body fat percentage. For example, a user inputs "weight 70kg, height 175cm, body fat percentage 20%" and takes a photo of their body. This information is input to the generative AI. Next, the generative AI analyzes the input information and proposes a personalized muscle training menu. The generative AI generates an optimal training menu based on the user's body type information and photos. For example, if a user wants to train their abdominal muscles, the generative AI proposes an effective abdominal training menu. This allows the user to perform training that suits them. Furthermore, the generating AI presents an ideal physique as a goal. The generating AI compares the user's current physique information with their target physique information and presents a goal to be achieved. For example, if a user's goal is "15% body fat," the generating AI will suggest a training menu to achieve that goal and monitor their progress. This app makes it easy for users to train at home. It also encourages exercise through push notifications and boosts motivation through interactive training consultations. This allows even those who are reluctant to go to the gym or who don't know how to train to train effectively. As a result, the training support system can suggest individual strength training menus based on the user's physique information and present an ideal physique as a goal.

[0062] The training support system according to the embodiment comprises a reception unit, a suggestion unit, and a goal presentation unit. The reception unit inputs the user's body type information. The user's body type information includes, but is not limited to, height, weight, and body fat percentage. For example, the user can input information such as their weight, height, and body fat percentage into the reception unit. The suggestion unit analyzes the information input by the reception unit and proposes an individualized muscle training menu. The suggestion unit generates an optimal training menu based on the user's body type information and a photograph, for example, using a generation AI. For example, if the user wants to train their abdominal muscles, the generation AI can propose a training menu that is effective for abdominal muscles. Some or all of the above processing in the suggestion unit is performed using the generation AI. The goal presentation unit presents an ideal body type as a goal based on the muscle training menu proposed by the suggestion unit. For example, the goal presentation unit compares the user's current body type information with the target body type information using a generation AI and presents a goal to be achieved. The generating AI can, for example, suggest a training menu to achieve a target body fat percentage of 15% if the user sets that goal, and monitor the user's progress. Some or all of the above-mentioned processing in the goal presentation unit is performed using the generating AI. As a result, the training support system according to this embodiment can suggest an individualized muscle training menu based on the user's body shape information and present an ideal body shape as a target.

[0063] The reception desk inputs the user's body type information. This information includes, but is not limited to, height, weight, and body fat percentage. For example, the reception desk allows users to input their own weight, height, and body fat percentage. Specifically, the reception desk provides an interface for users to input body type information using a dedicated application or website via a device such as a smartphone, tablet, or PC. Users can input detailed information such as height, weight, body fat percentage, age, gender, and exercise experience according to the input form. Furthermore, the reception desk has a function to automatically save the information entered by the user and store it in a database for later reference. This eliminates the need for users to re-enter information they have already entered. The reception desk also has a check function to verify the accuracy of the information entered by the user. For example, if the height or weight range is abnormally outside the normal range, it can display a message prompting the user to reconfirm. This allows the reception desk to collect user body type information accurately and efficiently, improving the overall reliability of the system.

[0064] The suggestion department analyzes the information entered by the reception department and proposes individualized muscle training menus. For example, the suggestion department uses a generative AI to generate an optimal training menu based on the user's body type information and photos. If the user wants to train their abdominal muscles, the generative AI can propose an effective abdominal training menu. Specifically, the generative AI receives the user's body type information as input data and generates an optimal training menu based on past training data and expert knowledge. The generative AI uses deep learning technology to automatically customize the training menu according to the user's body type and goals. For example, if the user wants to train their abdominal muscles, the generative AI will propose a menu that combines exercises effective for the abdominal muscles, such as crunches, planks, and leg raises. The generative AI can also adjust the intensity and number of repetitions of the training according to the user's exercise experience and fitness level. Furthermore, the suggestion department has an interface to provide the user with the training menu proposed by the generative AI. The user can check and execute the proposed training menu via their smartphone or tablet. This allows the suggestion department to propose personalized muscle training menus based on the user's body type information, providing support to help users train effectively.

[0065] The goal-setting unit presents an ideal physique as a target based on the strength training menu proposed by the suggestion unit. For example, using a generating AI, the goal-setting unit compares the user's current physique information with their target physique information and presents a goal to be achieved. For instance, if a user aims for a "body fat percentage of 15%", the generating AI can propose a training menu to achieve that goal and monitor progress. Specifically, based on the user's current physique information, the generating AI presents concrete steps to reach the target physique. For example, if a user wants to reduce their body fat percentage, the generating AI will propose specific action plans, such as improving diet or adding aerobic exercise. The goal-setting unit also has a function to provide regular feedback so that users can check their progress towards their goals. Users can check their progress and maintain motivation towards their goals via their smartphones or tablets. Furthermore, the goal-setting unit also provides support for setting the next goal once the user has achieved their previous goal. In this way, the goal-setting unit can provide motivation for users to continue training towards their ideal physique, supporting the improvement of their health and fitness.

[0066] The notification unit can encourage exercise through push notifications. For example, the notification unit can send push notifications during the time when the user is likely to exercise, thereby encouraging exercise. This allows the system to encourage users to exercise through push notifications.

[0067] The consultation department can conduct training consultations in a conversational format. For example, the consultation department can use a chatbot to conduct training consultations with users in a conversational format. For example, the consultation department can use voice chat to conduct training consultations with users in a conversational format. For example, the consultation department can use video calls to conduct training consultations with users in a conversational format. This allows for increased user motivation through conversational training consultations.

[0068] The suggestion function can generate an optimal training menu based on the user's body shape information and photos. For example, the suggestion function uses a generation AI to generate an optimal training menu based on the user's body shape information and photos. For example, if the user wants to train their abdominal muscles, the generation AI can suggest an effective training menu for the abdominal muscles. For example, if the user wants to train their arm muscles, the generation AI can suggest an effective training menu for the arm muscles. For example, if the user wants to train their leg muscles, the generation AI can suggest an effective training menu for the leg muscles. In this way, an optimal training menu can be generated based on the user's body shape information and photos.

[0069] The goal-setting unit can compare the user's current body shape information with their target body shape information and present goals to be achieved. For example, the goal-setting unit uses a generation AI to compare the user's current body shape information with their target body shape information and present goals to be achieved. For example, if the user's goal is "15% body fat," the generation AI can suggest a training menu to achieve that goal and monitor the user's progress. For example, if the user's goal is "to increase muscle mass," the generation AI can suggest a training menu to achieve that goal and monitor the user's progress. For example, if the user's goal is "to lose weight," the generation AI can suggest a training menu to achieve that goal and monitor the user's progress. This allows the system to compare the user's current body shape information with their target body shape information and present goals to be achieved.

[0070] The reception unit can estimate the user's emotions and adjust the timing of body shape information input based on the estimated emotions. For example, if the user is feeling stressed, the reception unit sends a notification prompting them to input body shape information during a time when they can relax. For example, if the user is relaxed, the reception unit sends a notification prompting them to input body shape information immediately. For example, if the user is busy, the reception unit sends a notification prompting them to input body shape information during a time when they have free time. By adjusting the timing of body shape information input based on the user's emotions, information can be entered at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not using AI.

[0071] The reception desk can analyze the user's past body shape information input history and select the optimal input method. For example, the reception desk may prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk may analyze the time periods in which the user has previously entered information and suggest the optimal input timing. For example, the reception desk may predict and suggest an input method to be used at a specific time period based on the user's past input history. In this way, the optimal input method can be selected by analyzing the user's past body shape information input history. Some or all of the above processing in the reception desk may be performed using AI or not.

[0072] The reception unit can filter the input of body shape information based on the user's current health status and lifestyle. For example, the reception unit adjusts input fields based on the user's current health status (weight, body fat percentage, etc.). For example, the reception unit adjusts input fields based on the user's lifestyle (diet, exercise, etc.). For example, the reception unit determines the priority of input fields based on the user's health status and lifestyle. This allows for the input of more appropriate information by filtering based on the user's current health status and lifestyle. Some or all of the above processing in the reception unit may be performed using AI or not.

[0073] The reception unit can estimate the user's emotions and determine the priority of body type information to be entered based on the estimated emotions. For example, if the user is stressed, the reception unit will prioritize the input of only important body type information. For example, if the user is relaxed, the reception unit will prompt for detailed body type information. For example, if the user is in a hurry, the reception unit will prompt for only the most important body type information. This allows for the input of more appropriate information by prioritizing body type information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not.

[0074] The reception desk can prioritize inputting highly relevant information based on the user's geographical location when entering body type information. For example, if the user is at a gym, the reception desk will prioritize inputting body type information related to gym training. For example, if the user is at home, the reception desk will prioritize inputting body type information related to home training. For example, if the user is out, the reception desk will prioritize inputting body type information related to training at their destination. By prioritizing the input of highly relevant information based on the user's geographical location, more appropriate information can be entered. Some or all of the above processing in the reception desk may be performed using AI, or it may be performed without using AI.

[0075] The reception desk can analyze the user's social media activity and input relevant information when the user enters body type information. For example, the reception desk may prompt the user to enter body type information based on training information shared on social media. For example, the reception desk may prompt the user to enter body type information based on information from training accounts followed on social media. For example, the reception desk may prompt the user to enter body type information based on information from training communities the user participates in on social media. This allows the reception desk to input relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not.

[0076] The suggestion section can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion section's generating AI will provide suggestions that include detailed explanations. If the user is in a hurry, the suggestion section's generating AI will provide concise and to-the-point suggestions. If the user is excited, the suggestion section's generating AI will provide suggestions with visually stimulating effects. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion section may be performed using a generating AI or not.

[0077] The proposal unit can adjust the level of detail in its proposals based on the importance of the training menus. For example, the proposal unit's generating AI will produce proposals with detailed explanations for important training menus. For example, the proposal unit's generating AI will produce proposals with concise explanations for less important training menus. The proposal unit can adjust the level of detail in its proposals in stages according to the importance of the training menus. This allows for more appropriate proposals to be made by adjusting the level of detail in the proposals based on the importance of the training menus. Some or all of the above processing in the proposal unit may be performed using the generating AI, or it may be performed without using the generating AI.

[0078] The suggestion unit can apply different suggestion algorithms depending on the category of the training menu when making suggestions. For example, for strength training menus, the suggestion unit's generating AI applies a suggestion algorithm specialized in improving strength. For example, for aerobic exercise menus, the suggestion unit's generating AI applies a suggestion algorithm specialized in improving endurance. For example, for flexibility improvement menus, the suggestion unit's generating AI applies a suggestion algorithm specialized in improving flexibility. By applying different suggestion algorithms depending on the category of the training menu, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using the generating AI, or it may be performed without using the generating AI.

[0079] The suggestion section can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion section will generate short, concise suggestions using a generative AI. If the user is relaxed, the suggestion section will generate longer suggestions with detailed explanations using a generative AI. If the user is excited, the suggestion section will generate suggestions with visually stimulating effects using a generative AI. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion section may be performed using a generative AI or not.

[0080] The proposal department can determine the priority of proposals based on the submission timing of the training menus. For example, the proposal department can have the AI ​​generate proposals preferentially for training menus with earlier submission dates, and postpone proposals for training menus with later submission dates. The proposal department can adjust the priority of proposals in stages according to the submission dates. This allows for more appropriate proposals to be made by determining the priority of proposals based on the submission dates of the training menus. Some or all of the above processing in the proposal department may be performed using the AI, or it may be performed without the AI.

[0081] The proposal unit can adjust the order of proposals based on the relevance of the training menus. For example, the proposal unit's generating AI will prioritize proposals for highly relevant training menus. For example, the proposal unit's generating AI will postpone proposals for less relevant training menus. The proposal unit can adjust the order of proposals in stages according to the relevance of the training menus. By adjusting the order of proposals based on the relevance of the training menus, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using the generating AI, or it may be performed without using the generating AI.

[0082] The goal presentation unit can estimate the user's emotions and adjust the way goals are displayed based on the estimated emotions. For example, if the user is nervous, the goal presentation unit provides a simple and highly visible display method. For example, if the user is relaxed, the goal presentation unit provides a display method that includes detailed information. For example, if the user is in a hurry, the goal presentation unit provides a display method that gets straight to the point. By adjusting the way goals are displayed based on the user's emotions, more appropriate goals can be presented. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the goal presentation unit may be performed using generative AI or not.

[0083] The goal presentation unit can predict the current goal by referring to past goal data when presenting a goal. For example, the goal presentation unit predicts the current goal based on the user's past goal data. For example, the goal presentation unit predicts the current goal by analyzing the user's past goal achievement status. For example, the goal presentation unit presents the optimal goal by referring to the user's past goal data. In this way, the current goal can be predicted by referring to past goal data. Some or all of the above processing in the goal presentation unit may be performed using generative AI, or it may be performed without using generative AI.

[0084] The goal presentation unit can apply different goal presentation methods to each category of training menu when presenting goals. For example, for strength training menus, the goal presentation unit's generating AI applies a goal presentation method specialized for improving strength. For example, for aerobic exercise menus, the goal presentation unit's generating AI applies a goal presentation method specialized for improving endurance. For example, for flexibility improvement menus, the goal presentation unit's generating AI applies a goal presentation method specialized for improving flexibility. By applying different goal presentation methods to each category of training menus, more appropriate goals can be presented. Some or all of the above processing in the goal presentation unit may be performed using the generating AI, or it may be performed without using the generating AI.

[0085] The goal presentation unit can estimate the user's emotions and adjust the importance of goals based on the estimated emotions. For example, if the user is nervous, the goal presentation unit will prioritize and present only important goals. For example, if the user is relaxed, the goal presentation unit will present detailed goals. For example, if the user is in a hurry, the goal presentation unit will present only the most important goals. This allows for the presentation of more appropriate goals by adjusting the importance of goals based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the goal presentation unit may be performed using generative AI or not.

[0086] The goal presentation unit can analyze changes in goals based on the submission timing of training menus when presenting goals. For example, the goal presentation unit prioritizes analyzing goal changes for training menus submitted earlier. For example, the goal presentation unit postpones analyzing goal changes for training menus submitted later. For example, the goal presentation unit analyzes goal changes in stages according to the submission timing. This allows for the presentation of more appropriate goals by analyzing goal changes based on the submission timing of training menus. Some or all of the above processing in the goal presentation unit may be performed using generative AI, or it may be performed without using generative AI.

[0087] The goal presentation unit can analyze goals by referring to relevant market data for the training menu when presenting goals. For example, the goal presentation unit analyzes goals based on relevant market data for the training menu. For example, the goal presentation unit analyzes goals by referring to market trends for the training menu. For example, the goal presentation unit presents optimal goals by referring to relevant market data for the training menu. This makes it possible to present more appropriate goals by referring to relevant market data for the training menu. Some or all of the above processing in the goal presentation unit may be performed using generative AI, or it may be performed without using generative AI.

[0088] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is feeling stressed, the notification unit will send a notification during a time when the user can relax. For example, if the user is relaxed, the notification unit will send a notification immediately. For example, if the user is busy, the notification unit will send a notification during a time when the user is free. By adjusting the timing of notifications based on the user's emotions, notifications can be sent at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not using AI.

[0089] The notification unit can select the optimal notification method by referring to the user's past notification history when sending a notification. For example, the notification unit may prioritize suggesting notification methods that the user has previously preferred to receive (e.g., push notifications, email). For example, the notification unit may predict and suggest a notification method to send at a specific time based on the user's past notification history. For example, the notification unit may analyze the user's past notification history and select the most effective notification method. In this way, the optimal notification method can be selected by referring to the user's past notification history. Some or all of the above processing in the notification unit may be performed using AI or not.

[0090] The notification unit can estimate the user's emotions and determine notification priorities based on the estimated emotions. For example, if the user is stressed, the notification unit will prioritize sending only important notifications. For example, if the user is relaxed, the notification unit will send detailed notifications. For example, if the user is in a hurry, the notification unit will send only the most important notifications. This allows for more appropriate notifications to be sent by prioritizing notifications based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the notification unit may be performed using AI or not.

[0091] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit will prioritize sending push notifications. For example, if the user is using a tablet, the notification unit will prioritize sending email notifications. For example, if the user is using a smartwatch, the notification unit will prioritize sending vibration notifications. In this way, the optimal notification method can be selected by considering the user's device information. Some or all of the above processing in the notification unit may be performed using AI or not.

[0092] The consultation unit can estimate the user's emotions and adjust the content of the consultation based on the estimated emotions. For example, if the user is feeling stressed, the consultation unit will provide relaxing advice. For example, if the user is relaxed, the consultation unit will provide detailed advice. For example, if the user is in a hurry, the consultation unit will provide concise and to-the-point advice. In this way, by adjusting the content of the consultation based on the user's emotions, more appropriate consultations can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the consultation unit may be performed using AI or not using AI.

[0093] The consultation department can select the most suitable consultation method by referring to the user's past consultation history during a consultation. For example, the consultation department may prioritize suggesting consultation methods that the user has preferred in the past (chat, voice, etc.). For example, the consultation department may predict and suggest consultation methods to be used at a specific time based on the user's past consultation history. For example, the consultation department may analyze the user's past consultation history and select the most effective consultation method. In this way, the optimal consultation method can be selected by referring to the user's past consultation history. Some or all of the above processes in the consultation department may be performed using AI or not.

[0094] The consultation unit can estimate the user's emotions and determine the priority of consultations based on the estimated emotions. For example, if the user is stressed, the consultation unit will prioritize only important consultations. For example, if the user is relaxed, the consultation unit will conduct detailed consultations. For example, if the user is in a hurry, the consultation unit will conduct only the most important consultations. This allows for more appropriate consultations by prioritizing consultations based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the consultation unit may be performed using AI or not.

[0095] The consultation department can select the most suitable consultation method by considering the user's device information during a consultation. For example, if the user is using a smartphone, the consultation department will prioritize chat consultations. For example, if the user is using a tablet, the consultation department will prioritize video consultations. For example, if the user is using a smartwatch, the consultation department will prioritize voice consultations. In this way, the consultation department can select the most suitable consultation method by considering the user's device information. Some or all of the above processing in the consultation department may be performed using AI or not.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The training support system can also suggest dietary management based on the user's body type information. The suggestion unit can propose appropriate meal plans based on the user's body type information and goals. For example, if the user wants to increase muscle mass, it can suggest a meal plan high in protein. If the user wants to reduce body fat, it can suggest a low-calorie, nutritionally balanced meal plan. Furthermore, if the user has a specific allergy, it can suggest a meal plan that accommodates that allergy. This allows users to aim for a healthy physique through both training and diet.

[0098] The training support system can also suggest sleep management based on the user's body type information. The suggestion section can propose an appropriate sleep schedule based on the user's body type and goals. For example, if a user wants to increase muscle mass, it can suggest a schedule to ensure sufficient sleep. If a user wants to reduce body fat, it can provide advice on getting quality sleep. Furthermore, if a user has an irregular lifestyle, it can suggest a regular sleep schedule. This allows users to aim for a healthy physique through both training and sleep.

[0099] The training support system can also offer stress management suggestions based on the user's body type information. The suggestion function can propose appropriate stress relief methods based on the user's body type and goals. For example, if a user wants to increase muscle mass, it can suggest relaxing yoga or meditation routines. If a user wants to reduce body fat, it can suggest stress-reducing activities. Furthermore, if a user is experiencing stress at work or in their daily life, it can offer counseling and support for stress management. This allows users to aim for a healthy physique through both training and stress management.

[0100] The training support system can also suggest supplements to maximize the effects of exercise based on the user's body type information. The suggestion unit can recommend appropriate supplements based on the user's body type and goals. For example, if the user wants to increase muscle mass, it can suggest supplements such as protein and BCAAs. If the user wants to reduce body fat, it can suggest supplements that aid in fat burning. Furthermore, if the user feels low on energy, it can suggest supplements for energy replenishment. This allows users to aim for a healthy physique through both training and supplements.

[0101] The training support system can provide a function to visualize training progress based on the user's body type information. The goal presentation unit can collect the user's training data and visualize progress using graphs and charts. For example, if the user wants to increase muscle mass, a graph showing the increase in muscle mass can be displayed. Similarly, if the user wants to reduce body fat, a chart showing the decrease in body fat percentage can be displayed. Furthermore, if the user achieves a specific training goal, the achievement status can be visually displayed. This allows the user to check their training progress at a glance and maintain their motivation.

[0102] The training support system can estimate the user's emotions and adjust the difficulty level of the training menu based on those emotions. The suggestion unit can suggest a low-difficulty training menu if the user is feeling stressed. For example, if the user is tired, it can suggest relaxing stretches or light exercises. Conversely, if the user is relaxed, it can suggest a high-difficulty training menu. Furthermore, if the user is excited, it can suggest a high-intensity training menu to help them release energy. This allows for more effective training by providing training menus tailored to the user's emotions.

[0103] The training support system can estimate the user's emotions and adjust training feedback based on those emotions. The goal presentation unit can provide positive feedback if the user is feeling stressed. For example, if the user is feeling anxious about training, it can send an encouraging message. If the user is relaxed, it can provide detailed feedback. Furthermore, if the user is excited, it can provide motivational feedback. This maximizes the effectiveness of training by providing feedback tailored to the user's emotions.

[0104] The training support system can estimate the user's emotions and adjust training progress reports based on those emotions. The goal presentation section can provide concise, positive progress reports if the user is feeling stressed. For example, if the user is feeling anxious about training, the system can highlight small, achieved goals. Conversely, if the user is relaxed, it can provide detailed progress reports. Furthermore, if the user is excited, it can provide visually stimulating progress reports. This allows the system to maintain training motivation by providing progress reports tailored to the user's emotions.

[0105] The training support system can estimate the user's emotions and adjust training goals based on those emotions. The goal-setting unit can set realistic and achievable goals if the user is feeling stressed. For example, if the user is anxious about training, it can set goals that can be achieved in small steps. Conversely, if the user is relaxed, it can set challenging goals. Furthermore, if the user is excited, it can set goals that can be achieved in a short period. This allows for goal setting tailored to the user's emotions, maximizing the effectiveness of training.

[0106] The training support system can estimate the user's emotions and adjust training advice based on those emotions. The consultation function can provide relaxing advice if the user is feeling stressed. For example, if the user is feeling anxious about training, it can suggest simple and effective training methods. If the user is relaxed, it can provide detailed advice. Furthermore, if the user is excited, it can provide advice to boost their motivation. This maximizes the effectiveness of training by providing advice tailored to the user's emotions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk enters the user's body type information. This information includes, for example, height, weight, and body fat percentage. Users can enter their own weight, height, body fat percentage, and other information. Step 2: The suggestion department analyzes the information entered by the reception department and proposes a personalized muscle training menu. The suggestion department uses a generation AI to generate the optimal training menu based on the user's body type information and photos. For example, if the user wants to train their abdominal muscles, the suggestion department can propose an effective abdominal training menu. Step 3: The goal presentation unit presents an ideal physique as a target based on the muscle training menu proposed by the suggestion unit. The goal presentation unit uses a generating AI to compare the user's current physique information with the target physique information and presents a goal to be achieved. For example, if the user's goal is "15% body fat," the unit can propose a training menu to achieve that goal and monitor the user's progress.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] Each of the multiple elements described above, including the reception unit, proposal unit, goal presentation unit, notification unit, and consultation unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and inputs the user's body shape information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal training menu based on the user's body shape information and photographs. The goal presentation unit is implemented by the specific processing unit 290 of the data processing unit 12 and presents an ideal body shape as a goal. The notification unit is implemented by the control unit 46A of the smart device 14 and encourages exercise through push notifications. The consultation unit is implemented by the control unit 46A of the smart device 14 and allows for training consultation in a dialogue format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reception unit, proposal unit, goal presentation unit, notification unit, and consultation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and inputs the user's body shape information. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an optimal training menu based on the user's body shape information and photographs. The goal presentation unit is implemented by the specific processing unit 290 of the data processing unit 12 and presents an ideal body shape as a goal. The notification unit is implemented by the control unit 46A of the smart glasses 214 and encourages exercise through push notifications. The consultation unit is implemented by the control unit 46A of the smart glasses 214 and allows for training consultation in a dialogue format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reception unit, proposal unit, goal presentation unit, notification unit, and consultation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and inputs the user's body shape information. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an optimal training menu based on the user's body shape information and photographs. The goal presentation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and presents an ideal body shape as a goal. The notification unit is implemented by, for example, the control unit 46A of the headset terminal 314 and encourages exercise through push notifications. The consultation unit is implemented by, for example, the control unit 46A of the headset terminal 314 and allows for training consultation in a dialogue format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the reception unit, proposal unit, goal presentation unit, notification unit, and consultation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and inputs the user's body shape information. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates an optimal training menu based on the user's body shape information and photographs. The goal presentation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and presents an ideal body shape as a goal. The notification unit is implemented by, for example, the control unit 46A of the robot 414 and prompts exercise through push notifications. The consultation unit is implemented by, for example, the control unit 46A of the robot 414 and allows for training consultation in an interactive format. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A reception area where users input their body shape information, The reception unit analyzes the information entered and proposes individual muscle training menus, The system includes a target presentation unit that presents an ideal body shape as a goal based on the muscle training menu proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) It features a notification unit that encourages exercise via push notifications. The system described in Appendix 1, characterized by the features described herein. (Note 3) The facility includes a consultation department that provides training advice in a dialogue format. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, The system generates an optimal training menu based on the user's body type information and photos. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned target presentation unit is The system compares the user's current body shape information with their target body shape information and suggests goals to be achieved. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of body shape information input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system analyzes the user's past body shape information input history and selects the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering body type information, filtering is performed based on the user's current health status and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of body shape information to be entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering body type information, the system prioritizes inputting highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter their body type information, the system analyzes their social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the training menu. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the training menu. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the training menus are submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the training menu. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned target presentation unit is It estimates the user's emotions and adjusts how goals are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned target presentation unit is When setting goals, refer to past goal data to predict current goals. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned target presentation unit is When setting goals, different goal-setting methods will be applied to each category of the training menu. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned target presentation unit is It estimates the user's emotions and adjusts the importance of goals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned target presentation unit is When setting goals, analyze how those goals change based on when the training menu was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned target presentation unit is When setting goals, analyze them by referring to relevant market data for the training menu. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned notification unit, When sending a notification, the system will refer to the user's past notification history to select the most suitable notification method. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned notification unit, When sending notifications, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned consultation department, The system estimates the user's emotions and adjusts the content of the consultation based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 29) The aforementioned consultation department, During a consultation, the system will refer to the user's past consultation history to select the most appropriate consultation method. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned consultation department, It estimates the user's emotions and determines the priority of consultations based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned consultation department, During the consultation, the most suitable consultation method will be selected, taking into account the user's device information. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users input their body shape information, The reception unit analyzes the information entered and proposes individual muscle training menus, The system includes a target presentation unit that presents an ideal physique as a goal based on the muscle training menu proposed by the aforementioned proposal unit. A system characterized by the following features.

2. It features a notification unit that encourages exercise via push notifications. The system according to feature 1.

3. The facility includes a consultation department that provides training consultations in a dialogue format. The system according to feature 1.

4. The aforementioned proposal section is, The system generates an optimal training menu based on the user's body type information and photos. The system according to feature 1.

5. The aforementioned target presentation unit is, The system compares the user's current body shape information with their target body shape information and suggests goals to achieve. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of body shape information input based on the estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is The system analyzes the user's past body shape information input history and selects the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When entering body type information, filtering is performed based on the user's current health status and lifestyle. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and determines the priority of body shape information to be entered based on those estimated emotions. The system according to feature 1.

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