system

Abstract

The system according to this embodiment aims to provide an optimal exercise plan based on the user's emotions. [Solution] The system according to the embodiment comprises an acquisition unit, an emotion estimation unit, a determination unit, and a provision unit. The acquisition unit acquires emotion estimation information, which is information used to estimate the user's emotions. The emotion estimation unit estimates the user's emotions based on the emotion estimation information acquired by the acquisition unit. The determination unit determines an exercise plan based on the emotions estimated by the emotion estimation unit. The provision unit provides the exercise plan determined by the determination unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including 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 prior art, it has not been fully carried out to provide an exercise plan based on the user's emotions, and there is room for improvement.

[0005] The system according to the embodiment aims to provide an optimal exercise plan based on the user's emotions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an emotion estimation unit, a determination unit, and a provision unit. The acquisition unit acquires emotion estimation information, which is information used to estimate the user's emotions. The emotion estimation unit estimates the user's emotions based on the emotion estimation information acquired by the acquisition unit. The determination unit determines an exercise plan based on the emotions estimated by the emotion estimation unit. The provision unit provides the exercise plan determined by the determination unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide an optimal exercise plan based on the user's emotions. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 mental support robot system according to an embodiment of the present invention is a system that estimates a user's emotions in a fitness center and provides exercise plans and encouraging messages to maintain motivation. This system estimates the user's emotions through an emotion engine and provides exercise plans and encouraging messages to maintain motivation. For example, if the user is feeling tired, it suggests appropriate rest times and recovery exercises to improve the effectiveness of the training. It also provides cooperative exercise plans according to the combination of emotions of the training pair or group. Furthermore, it creates a 3D avatar of the user and provides an experience of training while competing with oneself in virtual reality. First, emotion estimation information, which is information used to estimate the user's emotions, is acquired. For example, data such as the user's facial expressions, voice, and heart rate are collected. This information is input to the emotion estimation unit. Next, the emotion estimation unit estimates the user's emotions based on the acquired emotion estimation information. For example, it estimates whether the user is tired or if their motivation is low. This estimation result forms the basis for determining the exercise plan. Based on the emotions estimated by the emotion estimation unit, the exercise plan is determined. For example, if the user is tired, it suggests recovery exercises and rests. Also, if motivation is low, it provides encouraging messages. This exercise plan is provided to the user by the service provider. Furthermore, a 3D avatar of the user is created based on the determined exercise plan. The avatar creation unit generates the 3D avatar based on the user's physical data. This 3D avatar is used to support the user's training in a virtual reality environment. Additionally, an encouraging message is determined based on the emotions estimated by the emotion estimation unit. For example, if the user is tired, a message such as "Let's try a little harder!" is provided. This message is delivered to the user by the service provider. Furthermore, a cooperative exercise plan is determined based on the emotional combinations of the training pair or group.For example, if one partner is tired, the system suggests an exercise plan that allows the other partner to support them. In this way, the effectiveness of the training can be maximized. This allows the mental support robot system to provide optimal exercise plans and encouraging messages based on the user's emotions.

[0029] The mental support robot system according to the embodiment comprises an acquisition unit, an emotion estimation unit, a determination unit, and a provision unit. The acquisition unit acquires emotion estimation information, which is information used to estimate the user's emotions. Emotion estimation information includes, but is not limited to, facial expression data, voice data, and biosensor data. For example, the acquisition unit can acquire the user's facial expression data using a camera. The acquisition unit can also acquire the user's voice data using a microphone. Furthermore, the acquisition unit can acquire biosensor data such as the user's heart rate and skin electrical activity using a biosensor. For example, the acquisition unit captures the user's facial expressions in real time using a camera and saves them as facial expression data. It records the user's voice using a microphone and saves it as voice data. It measures the user's heart rate and skin electrical activity using a biosensor and saves it as biosensor data. The emotion estimation unit estimates the user's emotions based on the emotion estimation information acquired by the acquisition unit. The emotion estimation unit analyzes the emotion estimation information using, for example, AI and estimates the user's emotions. The emotion estimation unit analyzes the user's facial expression data using, for example, facial recognition technology to estimate the user's emotions. The emotion estimation unit can also analyze the user's voice data using voice analysis technology to estimate the user's emotions. Furthermore, the emotion estimation unit can analyze biosensor data to estimate the user's emotions. For example, the emotion estimation unit analyzes the user's facial expression data using facial recognition technology to estimate emotions such as whether the user is happy, sad, or angry. It analyzes the user's voice data using voice analysis technology to estimate emotions from the tone and speed of the user's voice. It analyzes biosensor data to estimate emotions from changes in the user's heart rate and skin electrical activity. The decision unit determines an exercise plan based on the emotions estimated by the emotion estimation unit. The decision unit determines the exercise plan using, for example, AI. For example, if the user is tired, the decision unit suggests recovery exercises or rest. The decision unit can also provide encouraging messages if the user's motivation is low.Furthermore, the decision unit can determine a cooperative exercise plan based on the emotional combination of the training pair or group. For example, if the user is tired, the decision unit suggests recovery exercises or rest. If motivation is low, it provides encouraging messages such as "Let's try a little harder!" The decision unit determines a cooperative exercise plan based on the emotional combination of the training pair or group. The provision unit provides the exercise plan determined by the decision unit. The provision unit provides the exercise plan, for example, using AI. The provision unit suggests recovery exercises or rest to the user. The provision unit can also provide encouraging messages. Furthermore, the provision unit can provide a cooperative exercise plan to the training pair or group. For example, the provision unit suggests recovery exercises or rest to the user. It provides encouraging messages. It provides a cooperative exercise plan to the training pair or group. As a result, the mental support robot system according to the embodiment can provide the optimal exercise plan and encouraging messages based on the user's emotions.

[0030] The acquisition unit acquires emotion estimation information, which is information used to estimate the user's emotions. Emotion estimation information includes, but is not limited to, facial expression data, voice data, and biosensor data. For example, the acquisition unit can acquire the user's facial expression data using a camera. Specifically, the camera can capture the user's face at high resolution and capture subtle changes in facial expression. This provides data for more accurately estimating the user's emotions. The acquisition unit can also acquire the user's voice data using a microphone. The microphone can record the tone, pitch, and speed of the user's voice with high precision and capture changes in emotion. Furthermore, the acquisition unit can acquire biosensor data such as the user's heart rate and skin electrical activity using biosensors. Biosensors are attached to the user's body and monitor heart rate and skin electrical activity in real time. This allows the user's stress level and relaxation state to be understood. For example, the acquisition unit can capture the user's facial expressions in real time using a camera and save them as facial expression data. It can also record the user's voice using a microphone and save it as voice data. The system uses biosensors to measure the user's heart rate and skin electrical activity, and stores this data as biosensor data. This allows the acquisition unit to collect emotion estimation information from various data sources, providing a foundation for comprehensively evaluating the user's emotions.

[0031] The emotion estimation unit estimates the user's emotions based on emotion estimation information acquired by the acquisition unit. The emotion estimation unit analyzes the emotion estimation information using AI, for example, to estimate the user's emotions. Specifically, the emotion estimation unit analyzes the user's facial expression data using facial expression recognition technology to estimate the user's emotions. Facial expression recognition technology utilizes a deep learning model to extract facial feature points and classify emotions. For example, it captures features such as smiles and frown lines to estimate whether the user is happy, sad, or angry. The emotion estimation unit can also analyze the user's voice data using voice analysis technology to estimate the user's emotions. Voice analysis technology analyzes the tone, pitch, speed, and volume of the voice to capture changes in emotion. For example, a higher voice tone may indicate joy or excitement, while a lower tone may indicate sadness or depression. Furthermore, the emotion estimation unit can also analyze biosensor data to estimate the user's emotions. Biosensor data analyzes changes in heart rate and skin electrical activity to assess the user's stress level and relaxation state. For example, an increase in heart rate and increased skin electrical activity suggests the user is likely to be stressed. Based on this, the emotion estimation unit integrates multiple data sources to estimate the user's emotions with high accuracy, enabling appropriate responses in the next steps.

[0032] The decision unit determines the exercise plan based on the emotions estimated by the emotion estimation unit. The decision unit uses AI, for example, to determine the exercise plan. Specifically, the decision unit considers the user's emotional state and generates the optimal exercise plan. For example, if the user is tired, it suggests recovery exercises or rest. Recovery exercises include light stretching, deep breathing, and relaxation exercises. The decision unit can also provide encouraging messages if motivation is low. For example, it displays positive messages such as "Let's try a little harder!" or "You can do it!" Furthermore, the decision unit can determine a cooperative exercise plan depending on the emotional combination of the training pair or group. For example, if some members of a group are tired, it suggests exercises that reduce the overall load, maintaining motivation by encouraging everyone to cooperate. In this way, the decision unit can provide flexible exercise plans that are tailored to the user's emotional state and support the user's mental health.

[0033] The service provider delivers the exercise plan determined by the decision-making unit. The service provider, for example, uses AI to deliver the exercise plan. Specifically, the service provider suggests recovery exercises and rest periods to the user. For example, if the user is tired, it displays the steps for recovery exercises on the screen and provides voice guidance on how to perform them. The service provider can also provide encouraging messages. For example, if the user is losing motivation, it displays messages such as "Let's try a little harder!" or "You can do it!" and provides voice encouragement. Furthermore, the service provider can provide collaborative exercise plans for training pairs or groups. For example, if some members of a group are tired, it suggests exercises that reduce the overall load, maintaining motivation by encouraging everyone to cooperate. The service provider can also collect user feedback and evaluate the effectiveness of the exercise plan. For example, after the user completes an exercise, they can input their impressions and changes in their physical condition, which the service provider can then incorporate into the next plan. This allows the service provider to provide the user with the optimal exercise plan and support the improvement of their mental health.

[0034] The decision unit includes an avatar creation unit that creates a 3D avatar of the user based on the exercise plan. The avatar creation unit creates the user's 3D avatar using, for example, modeling software. The avatar creation unit generates a 3D avatar based on the user's physical data. For example, the avatar creation unit receives physical data such as the user's height, weight, and body fat percentage as input and creates a 3D avatar based on it. The avatar creation unit can also display the 3D avatar in real time using rendering technology. For example, the avatar creation unit creates a 3D avatar based on the user's physical data and displays it in real time using rendering technology. This makes it possible to improve the user's training experience by creating a 3D avatar based on the exercise plan.

[0035] The avatar creation unit includes a support unit that assists user training using the created 3D avatar. The support unit provides, for example, real-time feedback. When the user is training, the support unit provides real-time feedback using the 3D avatar. For example, the support unit tracks the user's movements and reflects them in the 3D avatar to correct the user's form and posture in real time. The support unit can also provide guided exercises. For example, the support unit uses the 3D avatar to guide the user through the exercise procedures and movements. This allows for improved training effectiveness by supporting training using 3D avatars.

[0036] The emotion estimation unit determines an encouraging message based on the estimated emotion, and the delivery unit delivers that message. For example, if the user is tired, the emotion estimation unit might determine an encouraging message such as, "Let's keep going!" The delivery unit then delivers the determined encouraging message to the user. For example, the delivery unit displays the message on the user's device. The delivery unit can also deliver the message via voice. For example, the delivery unit delivers the encouraging message audibly through a speaker. This allows the user to maintain motivation by providing encouraging messages based on their emotions.

[0037] The decision-making unit determines a cooperative exercise plan based on the emotional combination of the training pair or group. For example, if one partner is tired, the unit will determine an exercise plan that allows the other partner to provide support. The decision-making unit can also determine a cooperative exercise plan based on the emotional combination of the group. For example, the unit will determine an exercise plan that allows the most motivated member of the group to take on a leadership role. This maximizes the effectiveness of training by providing exercise plans tailored to the emotional needs of the pair or group.

[0038] The data acquisition unit can estimate the user's emotions and adjust the timing of acquiring emotion estimation information based on the estimated emotions. For example, if the user is stressed, the data acquisition unit increases the frequency of acquiring emotion estimation information to track emotional changes in real time. If the user is relaxed, the data acquisition unit can also decrease the frequency of acquiring emotion estimation information to acquire only the minimum necessary information. Furthermore, if the user is exercising, the data acquisition unit can acquire emotion estimation information in accordance with the exercise intervals to evaluate the effects of exercise. For example, if the user is stressed, the data acquisition unit frequently acquires heart rate and facial expression data to track emotional changes in real time. If the user is relaxed, it reduces the frequency of acquiring voice data and ambient sounds to acquire only the minimum necessary information. If the user is exercising, it acquires emotion estimation information in accordance with the exercise intervals to evaluate the effects of exercise. This allows for more accurate emotion estimation by adjusting the timing of acquiring emotion estimation information based on the user's emotions.

[0039] The data acquisition unit can analyze the user's past emotional data and select the optimal acquisition method. For example, the acquisition unit can identify time periods in the user's past when they experienced high stress and prioritize acquiring emotional estimation information during those time periods. The acquisition unit can also analyze environmental conditions in which the user was relaxed in the past and prioritize acquiring emotional estimation information under those conditions. Furthermore, the acquisition unit can analyze the emotional response to specific triggers (e.g., music, exercise) from the user's past emotional data and acquire emotional estimation information when those triggers occur. For example, the acquisition unit can identify time periods in the user's past when they experienced high stress and prioritize acquiring emotional estimation information during those time periods. It can analyze environmental conditions in which the user was relaxed in the past and prioritize acquiring emotional estimation information under those conditions. It can analyze the emotional response to specific triggers (e.g., music, exercise) from the user's past emotional data and acquire emotional estimation information when those triggers occur. In this way, by analyzing past emotional data, the optimal method for acquiring emotional estimation information can be selected.

[0040] The data acquisition unit can filter emotion estimation information based on the user's current activity status and environment. For example, if the user is exercising, the unit filters emotion estimation information based on the type and intensity of the exercise to obtain appropriate data. If the user is resting, the unit can also filter emotion estimation information considering ambient sounds and surrounding conditions. Furthermore, if the user is in a stressful environment, the unit can filter out stressors specific to that environment and acquire emotion estimation information. For example, if the user is exercising, the unit filters emotion estimation information based on the type and intensity of the exercise to obtain appropriate data. If the user is resting, it filters emotion estimation information considering ambient sounds and surrounding conditions. If the user is in a stressful environment, it filters out stressors specific to that environment and acquires emotion estimation information. This allows for the acquisition of appropriate data by filtering emotion estimation information based on the user's activity status and environment.

[0041] The data acquisition unit can estimate the user's emotions and determine the priority of emotion estimation information to acquire based on the estimated emotions. For example, if the user is stressed, the data acquisition unit will prioritize acquiring heart rate and facial expression data. If the user is relaxed, the data acquisition unit can also prioritize acquiring voice data and ambient sounds. Furthermore, if the user is exercising, the data acquisition unit can also prioritize acquiring exercise data and respiratory data. For example, if the user is stressed, the data acquisition unit will prioritize acquiring heart rate and facial expression data. If the user is relaxed, it will prioritize acquiring voice data and ambient sounds. If the user is exercising, it will prioritize acquiring exercise data and respiratory data. By determining the priority of emotion estimation information based on the user's emotions, important information can be acquired preferentially.

[0042] The data acquisition unit can prioritize acquiring highly relevant information when acquiring emotion estimation information, taking into account the user's geographical location. For example, if the user is in a park, the data acquisition unit will prioritize acquiring emotion estimation information related to the natural environment. If the user is in a gym, the data acquisition unit can also prioritize acquiring emotion estimation information related to exercise. Furthermore, if the user is at home, the data acquisition unit can also prioritize acquiring emotion estimation information related to a relaxing environment. In this way, by taking the user's geographical location into consideration, the data acquisition unit can prioritize acquiring highly relevant emotion estimation information.

[0043] The acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring information for emotion estimation. For example, if a user posts on social media expressing stress, the acquisition unit can acquire the content of that post as emotion estimation information. The acquisition unit can also acquire the content of a user's social media post expressing relaxation as emotion estimation information. Furthermore, if a user posts on social media about exercise, the acquisition unit can also acquire the content of that post as emotion estimation information. For example, if a user posts on social media expressing stress, the acquisition unit can acquire the content of that post as emotion estimation information. If a user posts on social media expressing relaxation, the acquisition unit can acquire the content of that post as emotion estimation information. If a user posts on social media about exercise, the acquisition unit can acquire the content of that post as emotion estimation information. In this way, by analyzing social media activity, information related to the user's emotions can be acquired.

[0044] The emotion estimation unit can estimate the user's emotions and adjust the emotion estimation algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the emotion estimation unit can adjust the emotion estimation algorithm to prioritize stress-related data. If the user is relaxed, the emotion estimation unit can also adjust the emotion estimation algorithm to prioritize relaxation-related data. Furthermore, if the user is exercising, the emotion estimation unit can adjust the emotion estimation algorithm to prioritize exercise-related data. For example, if the user is feeling stressed, the emotion estimation unit adjusts the emotion estimation algorithm to prioritize stress-related data. If the user is relaxed, the emotion estimation algorithm adjusts the emotion estimation algorithm to prioritize relaxation-related data. If the user is exercising, the emotion estimation algorithm adjusts the emotion estimation algorithm to prioritize exercise-related data. By adjusting the emotion estimation algorithm based on the user's emotions, the estimation accuracy is improved.

[0045] The emotion estimation unit can improve its estimation accuracy by referring to the user's past emotion data during emotion estimation. For example, the emotion estimation unit can refer to the user's past emotion data and reflect it in the current emotion estimation. The emotion estimation unit can also extract specific patterns from the user's past emotion data to improve estimation accuracy. Furthermore, the emotion estimation unit can analyze the user's past emotion data and feed the results back into the emotion estimation algorithm. For example, the emotion estimation unit can refer to the user's past emotion data and reflect it in the current emotion estimation. It can extract specific patterns from the user's past emotion data to improve estimation accuracy. It can analyze the user's past emotion data and feed the results back into the emotion estimation algorithm. As a result, the accuracy of emotion estimation is improved by referring to past emotion data.

[0046] The emotion estimation unit can correct the estimation results based on the user's current activity status and environment during emotion estimation. For example, if the user is exercising, the emotion estimation unit corrects the emotion estimation results based on the type and intensity of the exercise. If the user is resting, the emotion estimation unit can also correct the emotion estimation results by considering ambient sounds and surrounding conditions. Furthermore, if the user is in a stressful environment, the emotion estimation unit can also correct the emotion estimation results by considering stress factors specific to that environment. For example, if the user is exercising, the emotion estimation unit corrects the emotion estimation results based on the type and intensity of the exercise. If the user is resting, it corrects the emotion estimation results by considering ambient sounds and surrounding conditions. If the user is in a stressful environment, it corrects the emotion estimation results by considering stress factors specific to that environment. By correcting the estimation results based on the user's activity status and environment, more accurate emotion estimation becomes possible.

[0047] The emotion estimation unit can estimate the user's emotions and adjust the display method of the emotion estimation results based on the estimated user emotions. For example, if the user is feeling stressed, the emotion estimation unit provides a simple and highly visible display method. If the user is relaxed, the emotion estimation unit can also provide a display method that includes detailed information. Furthermore, if the user is exercising, the emotion estimation unit can provide a display method that integrates exercise data and emotion data. For example, if the user is feeling stressed, the emotion estimation unit provides a simple and highly visible display method. If the user is relaxed, it provides a display method that includes detailed information. If the user is exercising, it provides a display method that integrates exercise data and emotion data. This allows for a highly visible display by adjusting the display method based on the user's emotions.

[0048] The emotion estimation unit can correct the estimation results by considering the user's geographical location information during emotion estimation. For example, if the user is in a park, the emotion estimation unit can correct the emotion estimation results related to the natural environment. If the user is in a gym, the emotion estimation unit can also correct the emotion estimation results related to exercise. Furthermore, if the user is at home, the emotion estimation unit can also correct the emotion estimation results related to a relaxing environment. For example, if the user is in a park, the emotion estimation unit can correct the emotion estimation results related to the natural environment. If the user is in a gym, it can correct the emotion estimation results related to exercise. If the user is at home, it can correct the emotion estimation results related to a relaxing environment. This makes it possible to estimate emotions more accurately by considering geographical location information.

[0049] The emotion estimation unit can analyze the user's social media activity and adjust the estimation results during emotion estimation. For example, if a user posts on social media expressing stress, the emotion estimation unit will reflect the content of that post in the emotion estimation result. The emotion estimation unit can also reflect the content of a user's posts expressing relaxation in the emotion estimation result. Furthermore, if a user posts on social media about exercise, the emotion estimation unit can also reflect the content of that post in the emotion estimation result. For example, if a user posts on social media expressing stress, the emotion estimation unit will reflect the content of that post in the emotion estimation result. If a user posts on social media expressing relaxation, the content of that post will be reflected in the emotion estimation result. If a user posts on social media about exercise, the content of that post will be reflected in the emotion estimation result. This allows for more accurate emotion estimation by analyzing social media activity.

[0050] The decision-making unit can estimate the user's emotions and adjust how it determines the exercise plan based on those emotions. For example, if the user is feeling stressed, the decision-making unit will prioritize exercise plans with a relaxing effect. If the user is relaxed, the decision-making unit may also prioritize challenging exercise plans. Furthermore, if the user is exercising, the decision-making unit can adjust the exercise plan based on the type and intensity of the exercise. For example, if the user is feeling stressed, the decision-making unit will prioritize exercise plans with a relaxing effect. If the user is relaxed, it will prioritize challenging exercise plans. If the user is exercising, it will adjust the exercise plan based on the type and intensity of the exercise. This allows the system to provide the optimal plan by adjusting how it determines the exercise plan based on the user's emotions.

[0051] The decision-making unit can select the optimal exercise plan by referring to the user's past training data. For example, the decision-making unit can refer to the user's past training data and select an exercise plan that matches the user's current physical condition and goals. The decision-making unit can also analyze the effects of specific exercises from the user's past training data and select the optimal plan. Furthermore, the decision-making unit can analyze the user's past training data and select an exercise plan that matches the user's training progress. For example, the decision-making unit can refer to the user's past training data and select an exercise plan that matches the user's current physical condition and goals. It can analyze the effects of specific exercises from the user's past training data and select the optimal plan. It can analyze the user's past training data and select an exercise plan that matches the user's training progress. In this way, by referring to past training data, the system can provide the user with the most suitable exercise plan.

[0052] The decision-making unit can customize the exercise plan based on the user's current physical condition and environment. For example, if the user is tired, the decision-making unit can customize a plan that includes recovery exercises and rest. If the user is healthy, the decision-making unit can also customize a challenging exercise plan. Furthermore, if the user is in a specific environment (e.g., outdoors, gym), the decision-making unit can also customize an exercise plan that is suitable for that environment. For example, if the user is tired, the decision-making unit can customize a plan that includes recovery exercises and rest. If the user is healthy, it can customize a challenging exercise plan. If the user is in a specific environment (e.g., outdoors, gym), it can customize an exercise plan that is suitable for that environment. By customizing the plan based on the user's physical condition and environment, more effective exercise becomes possible.

[0053] The decision-making unit can estimate the user's emotions and prioritize exercise plans based on those emotions. For example, if the user is feeling stressed, the decision-making unit will prioritize exercise plans with a relaxing effect. If the user is relaxed, the decision-making unit can also prioritize challenging exercise plans. Furthermore, if the user is exercising, the decision-making unit can prioritize exercise plans based on the type and intensity of the exercise. For example, if the user is feeling stressed, the decision-making unit will prioritize exercise plans with a relaxing effect. If the user is relaxed, it will prioritize challenging exercise plans. If the user is exercising, it will prioritize exercise plans based on the type and intensity of the exercise. This allows the system to provide exercise plans in the optimal order by prioritizing them based on the user's emotions.

[0054] The decision-making unit can select the optimal exercise plan by considering the user's geographical location. For example, if the user is in a park, the decision-making unit will select an exercise plan that utilizes the natural environment. If the user is in a gym, the decision-making unit can also select an exercise plan that utilizes the gym's equipment. Furthermore, if the user is at home, the decision-making unit can also select an exercise plan that can be done at home. In this way, by considering geographical location information, the system can provide the user with the most suitable exercise plan.

[0055] The decision-making unit can analyze the user's social media activity and customize the exercise plan when determining the plan. For example, the decision-making unit can customize the exercise plan based on the fitness goals the user has shared on social media. The decision-making unit can also customize the plan by referencing the training methods of fitness influencers the user follows on social media. Furthermore, the decision-making unit can also customize the exercise plan based on the fitness challenges the user is participating in on social media. For example, the decision-making unit can customize the exercise plan based on the fitness goals the user has shared on social media. It can customize the plan by referencing the training methods of fitness influencers the user follows on social media. It can customize the exercise plan based on the fitness challenges the user is participating in on social media. This allows the system to provide the user with the most suitable exercise plan by analyzing their social media activity.

[0056] The service provider can estimate the user's emotions and adjust how the exercise plan is delivered based on those emotions. For example, if the user is feeling stressed, the service provider can provide an exercise plan accompanied by relaxing music. If the user is relaxed, the service provider can also provide an exercise plan with detailed explanations. Furthermore, if the user is exercising, the service provider can adjust the exercise plan to match the intervals of the exercise. In this way, by adjusting the delivery method based on the user's emotions, the service provider can provide the optimal exercise plan.

[0057] The service provider can select the optimal delivery method when providing exercise plans by referring to the user's past feedback. For example, the service provider can prioritize the delivery method of exercise plans that the user has preferred in the past. The service provider can also select a specific delivery method (e.g., audio guide, video guide) based on the user's past feedback. Furthermore, the service provider can analyze the user's past feedback and customize the delivery method. For example, the service provider can prioritize the delivery method of exercise plans that the user has preferred in the past. They can select a specific delivery method (e.g., audio guide, video guide) based on the user's past feedback. They can analyze the user's past feedback and customize the delivery method. This allows the service provider to select the most suitable delivery method for the user by referring to past feedback.

[0058] The service provider can customize the content of the exercise plan based on the user's current physical condition and environment. For example, if the user is tired, the service provider can provide a plan that includes recovery exercises and rest. If the user is in good health, the service provider can also provide a challenging exercise plan. Furthermore, if the user is in a specific environment (e.g., outdoors, gym), the service provider can provide an exercise plan suited to that environment. For example, if the user is tired, the service provider can provide a plan that includes recovery exercises and rest. If the user is in good health, the service provider can provide a challenging exercise plan. If the user is in a specific environment (e.g., outdoors, gym), the service provider can provide an exercise plan suited to that environment. This allows the service provider to provide the optimal exercise plan by customizing the content based on the user's physical condition and environment.

[0059] The service provider can estimate the user's emotions and determine the order in which exercise plans are presented based on those emotions. For example, if the user is feeling stressed, the service provider can first provide a relaxing exercise plan. If the user is relaxed, the service provider can also first provide a challenging exercise plan. Furthermore, if the user is exercising, the service provider can determine the order of exercise plans to match the exercise intervals. For example, if the user is feeling stressed, the service provider can first provide a relaxing exercise plan. If the user is relaxed, it can first provide a challenging exercise plan. If the user is exercising, the service provider can determine the order of exercise plans to match the exercise intervals. This allows the service provider to deliver exercise plans in the optimal order by determining the order based on the user's emotions.

[0060] The service provider can select the optimal delivery method when providing exercise plans, taking into account the user's geographical location. For example, if the user is in a park, the service provider can provide an exercise plan that utilizes the natural environment. If the user is in a gym, the service provider can also provide an exercise plan that utilizes the gym's equipment. Furthermore, if the user is at home, the service provider can provide an exercise plan that can be done at home. In this way, by considering geographical location information, the service provider can provide the user with the most suitable exercise plan.

[0061] The service provider can analyze users' social media activity and customize the content of their exercise plans. For example, they can customize exercise plans based on fitness goals shared by users on social media. They can also customize plans by referencing training methods of fitness influencers that users follow on social media. Furthermore, they can customize exercise plans based on fitness challenges that users are participating in on social media. For example, they can customize exercise plans based on fitness goals shared by users on social media. They can customize plans by referencing training methods of fitness influencers that users follow on social media. They can customize exercise plans based on fitness challenges that users are participating in on social media. This allows them to provide users with the most suitable exercise plans by analyzing their social media activity.

[0062] The avatar creation unit can estimate the user's emotions and adjust the 3D avatar creation method based on those emotions. For example, if the user is feeling stressed, the avatar creation unit will create a relaxing avatar. If the user is relaxed, the avatar creation unit can also create a challenging avatar. Furthermore, if the user is exercising, the avatar creation unit can customize the avatar based on the type and intensity of the exercise. For example, if the user is feeling stressed, the avatar creation unit will create a relaxing avatar. If the user is relaxed, it will create a challenging avatar. If the user is exercising, it will customize the avatar based on the type and intensity of the exercise. This allows the system to generate the optimal avatar by adjusting the 3D avatar creation method based on the user's emotions.

[0063] The avatar creation unit can generate an optimal avatar by referencing the user's past physical data when creating a 3D avatar. For example, the avatar creation unit can refer to the user's past physical data to generate an avatar that matches their current physical condition and goals. The avatar creation unit can also generate an avatar that reflects the effects of a specific exercise based on the user's past physical data. Furthermore, the avatar creation unit can analyze the user's past physical data and generate an avatar that matches their training progress. For example, the avatar creation unit can refer to the user's past physical data to generate an avatar that matches their current physical condition and goals. It can generate an avatar that reflects the effects of a specific exercise based on the user's past physical data. It can analyze the user's past physical data and generate an avatar that matches their training progress. In this way, by referencing past physical data, it is possible to generate an optimal 3D avatar for the user.

[0064] The avatar creation function can customize 3D avatars based on the user's current physical condition and environment. For example, if the user is tired, the avatar creation function will generate an avatar that reflects recovery exercises or rest. If the user is healthy, the avatar creation function can also generate a challenging avatar. Furthermore, if the user is in a specific environment (e.g., outdoors, gym), the avatar creation function can generate an avatar suitable for that environment. For example, if the user is tired, the avatar creation function will generate an avatar that reflects recovery exercises or rest. If the user is healthy, it will generate a challenging avatar. If the user is in a specific environment (e.g., outdoors, gym), it will generate an avatar suitable for that environment. This allows for the creation of an optimal 3D avatar by customizing it based on the user's physical condition and environment.

[0065] The avatar creation unit can estimate the user's emotions and adjust the display method of the 3D avatar based on the estimated emotions. For example, if the user is feeling stressed, the avatar creation unit can provide a display method that has a relaxing effect. If the user is relaxed, the avatar creation unit can also provide a challenging display method. Furthermore, if the user is exercising, the avatar creation unit can adjust the display method based on the type and intensity of the exercise. For example, if the user is feeling stressed, the avatar creation unit can provide a display method that has a relaxing effect. If the user is relaxed, it can provide a challenging display method. If the user is exercising, it can adjust the display method based on the type and intensity of the exercise. In this way, by adjusting the display method based on the user's emotions, the optimal 3D avatar can be provided.

[0066] The avatar creation unit can generate an optimal avatar by considering the user's geographical location information when creating a 3D avatar. For example, if the user is in a park, the avatar creation unit will generate an avatar that reflects the natural environment. If the user is in a gym, the avatar creation unit can also generate an avatar that reflects the gym's equipment. Furthermore, if the user is at home, the avatar creation unit can generate an avatar that reflects the user's home environment. For example, if the user is in a park, the avatar creation unit will generate an avatar that reflects the natural environment. If the user is in a gym, the avatar will generate an avatar that reflects the gym's equipment. If the user is at home, the avatar will generate an avatar that reflects the user's home environment. In this way, by considering geographical location information, it is possible to generate a 3D avatar that is optimal for the user.

[0067] The avatar creation function can analyze a user's social media activity and customize the avatar during 3D avatar creation. For example, the avatar creation function can customize the avatar based on the fitness goals the user has shared on social media. It can also customize the avatar by referencing the training methods of fitness influencers the user follows on social media. Furthermore, the avatar creation function can customize the avatar based on the fitness challenges the user is participating in on social media. For example, the avatar creation function can customize the avatar based on the fitness goals the user has shared on social media. It can customize the avatar by referencing the training methods of fitness influencers the user follows on social media. It can customize the avatar based on the fitness challenges the user is participating in on social media. This allows for the generation of the most suitable 3D avatar for each user by analyzing their social media activity.

[0068] The support unit can estimate the user's emotions and adjust the training support method based on those emotions. For example, if the user is feeling stressed, the support unit can provide a training support method with a relaxing effect. If the user is relaxed, the support unit can also provide a challenging training support method. Furthermore, if the user is exercising, the support unit can adjust the training support method based on the type and intensity of the exercise. For example, if the user is feeling stressed, the support unit can provide a training support method with a relaxing effect. If the user is relaxed, it can provide a challenging training support method. If the user is exercising, the support unit can adjust the training support method based on the type and intensity of the exercise. This allows the system to provide optimal support by adjusting the training support method based on the user's emotions.

[0069] The support department can select the optimal support method by referring to the user's past training data during training support. For example, the support department can refer to the user's past training data and select a training support method that suits the user's current physical condition and goals. The support department can also analyze the effects of specific exercises from the user's past training data and select the optimal support method. Furthermore, the support department can analyze the user's past training data and select a support method that matches the user's training progress. For example, the support department can refer to the user's past training data and select a training support method that suits the user's current physical condition and goals. It can analyze the effects of specific exercises from the user's past training data and select the optimal support method. It can analyze the user's past training data and select a support method that matches the user's training progress. In this way, the support department can select the optimal support method for the user by referring to past training data.

[0070] The support team can customize training support based on the user's current physical condition and environment. For example, if the user is tired, the support team can provide support that includes recovery exercises and rest. If the user is healthy, the support team can also provide challenging support. Furthermore, if the user is in a specific environment (e.g., outdoors, gym), the support team can provide support that is appropriate for that environment. For example, if the user is tired, the support team can provide support that includes recovery exercises and rest. If the user is healthy, the support team can provide challenging support. If the user is in a specific environment (e.g., outdoors, gym), the support team can provide support that is appropriate for that environment. This allows for optimal training support by customizing support based on the user's physical condition and environment.

[0071] The support unit can estimate the user's emotions and determine the priority of training support based on those emotions. For example, if the user is feeling stressed, the support unit will prioritize providing training support with a relaxing effect. If the user is relaxed, the support unit may also prioritize providing challenging training support. Furthermore, if the user is exercising, the support unit can also determine the priority of training support based on the type and intensity of the exercise. For example, if the user is feeling stressed, the support unit will prioritize providing training support with a relaxing effect. If the user is relaxed, it will prioritize providing challenging training support. If the user is exercising, the support unit will determine the priority of training support based on the type and intensity of the exercise. This allows the support to be provided in the optimal order by determining the priority of training support based on the user's emotions.

[0072] The support unit can select the optimal support method during training by considering the user's geographical location. For example, if the user is in a park, the support unit can provide training support methods that utilize the natural environment. If the user is in a gym, the support unit can also provide training support methods that utilize the gym's equipment. Furthermore, if the user is at home, the support unit can provide training support methods that can be done at home. For example, if the user is in a park, the support unit can provide training support methods that utilize the natural environment. If the user is in a gym, the support unit can provide training support methods that utilize the gym's equipment. If the user is at home, the support unit can provide training support methods that can be done at home. In this way, by considering geographical location information, the support unit can provide the user with the most suitable training support method.

[0073] The support team can analyze users' social media activity during training support to customize the support provided. For example, the support team can customize training support based on fitness goals shared by users on social media. The support team can also customize support by referencing the training methods of fitness influencers that users follow on social media. Furthermore, the support team can customize training support based on fitness challenges that users participate in on social media. For example, the support team can customize training support based on fitness goals shared by users on social media. The support team can customize support by referencing the training methods of fitness influencers that users follow on social media. The support team can customize training support based on fitness challenges that users participate in on social media. This allows the support team to provide users with the most suitable training support by analyzing their social media activity.

[0074] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0075] The mental support robot system can also acquire the user's exercise history data and use it to optimize exercise plans. The acquisition unit records the type, frequency, and intensity of exercises the user has performed in the past and provides this data to the decision unit. For example, based on the user's past exercise data, the decision unit can propose an exercise plan tailored to the user's fitness level and preferences. Furthermore, if the user prefers a particular exercise, the system can prioritize suggesting plans that include that exercise. This allows for the provision of more effective exercise plans by utilizing exercise history data.

[0076] The mental support robot system can also acquire user health data and use it to optimize exercise plans. The acquisition unit records health data such as the user's blood pressure, blood sugar levels, and body fat percentage, and provides it to the decision unit. For example, based on the user's health data, the decision unit can propose an exercise plan tailored to the user's health condition. Furthermore, if the user has specific health goals, the system can propose a plan that aligns with those goals. This allows for the provision of more effective exercise plans by utilizing health data.

[0077] The mental support robot system can also acquire user environmental data and use it to optimize exercise plans. The acquisition unit records temperature, humidity, noise level, and other information of the environment in which the user trains, and provides this data to the decision unit. For example, if the user trains in a hot and humid environment, the decision unit can suggest an exercise plan suitable for that environment. Similarly, if the user trains in a quiet environment, the decision unit can suggest relaxation exercises suitable for that environment. In this way, by utilizing environmental data, a more effective exercise plan can be provided.

[0078] The mental support robot system can also acquire user feedback data and use it to optimize exercise plans. The acquisition unit records user feedback on past exercise plans and provides it to the decision unit. For example, if a user expresses high satisfaction with a particular exercise plan, the decision unit can suggest that plan again. Conversely, if a user expresses dissatisfaction with a particular exercise plan, the decision unit can adjust the plan to avoid that plan. In this way, by utilizing feedback data, a more effective exercise plan can be provided.

[0079] The mental support robot system can also acquire the user's geographical location data and use it to optimize the exercise plan. The acquisition unit records the geographical location data of the place where the user is training and provides it to the decision unit. For example, if the user is training in a park, the decision unit can suggest an outdoor exercise plan suitable for that location. Similarly, if the user is training in a gym, it can suggest an indoor exercise plan suitable for that location. In this way, by utilizing geographical location data, a more effective exercise plan can be provided.

[0080] The following briefly describes the processing flow for example form 1.

[0081] Step 1: The acquisition unit acquires emotion estimation information, which is information used to estimate the user's emotions. Emotion estimation information includes, for example, facial expression data, voice data, and biosensor data. The acquisition unit acquires the user's facial expression data using a camera, the user's voice data using a microphone, and biosensor data such as the user's heart rate and skin electrical activity using biosensors. Step 2: The emotion estimation unit estimates the user's emotions based on the emotion estimation information acquired by the acquisition unit. The emotion estimation unit uses AI to analyze the emotion estimation information and estimates the user's emotions through facial recognition technology, voice analysis technology, and analysis of biosensor data. Step 3: The decision unit determines an exercise plan based on the emotions estimated by the emotion estimation unit. The decision unit uses AI to determine the exercise plan, suggesting recovery exercises or rest if the user is tired, and providing encouraging messages if motivation is low. Step 4: The delivery unit provides the exercise plan determined by the decision unit. The delivery unit uses AI to suggest recovery exercises and rest periods to the user and provides encouraging messages.

[0082] (Example of form 2) The mental support robot system according to an embodiment of the present invention is a system that estimates a user's emotions in a fitness center and provides exercise plans and encouraging messages to maintain motivation. This system estimates the user's emotions through an emotion engine and provides exercise plans and encouraging messages to maintain motivation. For example, if the user is feeling tired, it suggests appropriate rest times and recovery exercises to improve the effectiveness of the training. It also provides cooperative exercise plans according to the combination of emotions of the training pair or group. Furthermore, it creates a 3D avatar of the user and provides an experience of training while competing with oneself in virtual reality. First, emotion estimation information, which is information used to estimate the user's emotions, is acquired. For example, data such as the user's facial expressions, voice, and heart rate are collected. This information is input to the emotion estimation unit. Next, the emotion estimation unit estimates the user's emotions based on the acquired emotion estimation information. For example, it estimates whether the user is tired or if their motivation is low. This estimation result forms the basis for determining the exercise plan. Based on the emotions estimated by the emotion estimation unit, the exercise plan is determined. For example, if the user is tired, it suggests recovery exercises and rests. Also, if motivation is low, it provides encouraging messages. This exercise plan is provided to the user by the service provider. Furthermore, a 3D avatar of the user is created based on the determined exercise plan. The avatar creation unit generates the 3D avatar based on the user's physical data. This 3D avatar is used to support the user's training in a virtual reality environment. Additionally, an encouraging message is determined based on the emotions estimated by the emotion estimation unit. For example, if the user is tired, a message such as "Let's try a little harder!" is provided. This message is delivered to the user by the service provider. Furthermore, a cooperative exercise plan is determined based on the emotional combinations of the training pair or group.For example, if one partner is tired, the system suggests an exercise plan that allows the other partner to support them. In this way, the effectiveness of the training can be maximized. This allows the mental support robot system to provide optimal exercise plans and encouraging messages based on the user's emotions.

[0083] The mental support robot system according to the embodiment comprises an acquisition unit, an emotion estimation unit, a determination unit, and a provision unit. The acquisition unit acquires emotion estimation information, which is information used to estimate the user's emotions. Emotion estimation information includes, but is not limited to, facial expression data, voice data, and biosensor data. For example, the acquisition unit can acquire the user's facial expression data using a camera. The acquisition unit can also acquire the user's voice data using a microphone. Furthermore, the acquisition unit can acquire biosensor data such as the user's heart rate and skin electrical activity using a biosensor. For example, the acquisition unit captures the user's facial expressions in real time using a camera and saves them as facial expression data. It records the user's voice using a microphone and saves it as voice data. It measures the user's heart rate and skin electrical activity using a biosensor and saves it as biosensor data. The emotion estimation unit estimates the user's emotions based on the emotion estimation information acquired by the acquisition unit. The emotion estimation unit analyzes the emotion estimation information using, for example, AI and estimates the user's emotions. The emotion estimation unit analyzes the user's facial expression data using, for example, facial recognition technology to estimate the user's emotions. The emotion estimation unit can also analyze the user's voice data using voice analysis technology to estimate the user's emotions. Furthermore, the emotion estimation unit can analyze biosensor data to estimate the user's emotions. For example, the emotion estimation unit analyzes the user's facial expression data using facial recognition technology to estimate emotions such as whether the user is happy, sad, or angry. It analyzes the user's voice data using voice analysis technology to estimate emotions from the tone and speed of the user's voice. It analyzes biosensor data to estimate emotions from changes in the user's heart rate and skin electrical activity. The decision unit determines an exercise plan based on the emotions estimated by the emotion estimation unit. The decision unit determines the exercise plan using, for example, AI. For example, if the user is tired, the decision unit suggests recovery exercises or rest. The decision unit can also provide encouraging messages if the user's motivation is low.Furthermore, the decision unit can determine a cooperative exercise plan based on the emotional combination of the training pair or group. For example, if the user is tired, the decision unit suggests recovery exercises or rest. If motivation is low, it provides encouraging messages such as "Let's try a little harder!" The decision unit determines a cooperative exercise plan based on the emotional combination of the training pair or group. The provision unit provides the exercise plan determined by the decision unit. The provision unit provides the exercise plan, for example, using AI. The provision unit suggests recovery exercises or rest to the user. The provision unit can also provide encouraging messages. Furthermore, the provision unit can provide a cooperative exercise plan to the training pair or group. For example, the provision unit suggests recovery exercises or rest to the user. It provides encouraging messages. It provides a cooperative exercise plan to the training pair or group. As a result, the mental support robot system according to the embodiment can provide the optimal exercise plan and encouraging messages based on the user's emotions.

[0084] The acquisition unit acquires emotion estimation information, which is information used to estimate the user's emotions. Emotion estimation information includes, but is not limited to, facial expression data, voice data, and biosensor data. For example, the acquisition unit can acquire the user's facial expression data using a camera. Specifically, the camera can capture the user's face at high resolution and capture subtle changes in facial expression. This provides data for more accurately estimating the user's emotions. The acquisition unit can also acquire the user's voice data using a microphone. The microphone can record the tone, pitch, and speed of the user's voice with high precision and capture changes in emotion. Furthermore, the acquisition unit can acquire biosensor data such as the user's heart rate and skin electrical activity using biosensors. Biosensors are attached to the user's body and monitor heart rate and skin electrical activity in real time. This allows the user's stress level and relaxation state to be understood. For example, the acquisition unit can capture the user's facial expressions in real time using a camera and save them as facial expression data. It can also record the user's voice using a microphone and save it as voice data. The system uses biosensors to measure the user's heart rate and skin electrical activity, and stores this data as biosensor data. This allows the acquisition unit to collect emotion estimation information from various data sources, providing a foundation for comprehensively evaluating the user's emotions.

[0085] The emotion estimation unit estimates the user's emotions based on emotion estimation information acquired by the acquisition unit. The emotion estimation unit analyzes the emotion estimation information using AI, for example, to estimate the user's emotions. Specifically, the emotion estimation unit analyzes the user's facial expression data using facial expression recognition technology to estimate the user's emotions. Facial expression recognition technology utilizes a deep learning model to extract facial feature points and classify emotions. For example, it captures features such as smiles and frown lines to estimate whether the user is happy, sad, or angry. The emotion estimation unit can also analyze the user's voice data using voice analysis technology to estimate the user's emotions. Voice analysis technology analyzes the tone, pitch, speed, and volume of the voice to capture changes in emotion. For example, a higher voice tone may indicate joy or excitement, while a lower tone may indicate sadness or depression. Furthermore, the emotion estimation unit can also analyze biosensor data to estimate the user's emotions. Biosensor data analyzes changes in heart rate and skin electrical activity to assess the user's stress level and relaxation state. For example, an increase in heart rate and increased skin electrical activity suggests the user is likely to be stressed. Based on this, the emotion estimation unit integrates multiple data sources to estimate the user's emotions with high accuracy, enabling appropriate responses in the next steps.

[0086] The decision unit determines the exercise plan based on the emotions estimated by the emotion estimation unit. The decision unit uses AI, for example, to determine the exercise plan. Specifically, the decision unit considers the user's emotional state and generates the optimal exercise plan. For example, if the user is tired, it suggests recovery exercises or rest. Recovery exercises include light stretching, deep breathing, and relaxation exercises. The decision unit can also provide encouraging messages if motivation is low. For example, it displays positive messages such as "Let's try a little harder!" or "You can do it!" Furthermore, the decision unit can determine a cooperative exercise plan depending on the emotional combination of the training pair or group. For example, if some members of a group are tired, it suggests exercises that reduce the overall load, maintaining motivation by encouraging everyone to cooperate. In this way, the decision unit can provide flexible exercise plans that are tailored to the user's emotional state and support the user's mental health.

[0087] The service provider delivers the exercise plan determined by the decision-making unit. The service provider, for example, uses AI to deliver the exercise plan. Specifically, the service provider suggests recovery exercises and rest periods to the user. For example, if the user is tired, it displays the steps for recovery exercises on the screen and provides voice guidance on how to perform them. The service provider can also provide encouraging messages. For example, if the user is losing motivation, it displays messages such as "Let's try a little harder!" or "You can do it!" and provides voice encouragement. Furthermore, the service provider can provide collaborative exercise plans for training pairs or groups. For example, if some members of a group are tired, it suggests exercises that reduce the overall load, maintaining motivation by encouraging everyone to cooperate. The service provider can also collect user feedback and evaluate the effectiveness of the exercise plan. For example, after the user completes an exercise, they can input their impressions and changes in their physical condition, which the service provider can then incorporate into the next plan. This allows the service provider to provide the user with the optimal exercise plan and support the improvement of their mental health.

[0088] The decision unit includes an avatar creation unit that creates a 3D avatar of the user based on the exercise plan. The avatar creation unit creates the user's 3D avatar using, for example, modeling software. The avatar creation unit generates a 3D avatar based on the user's physical data. For example, the avatar creation unit receives physical data such as the user's height, weight, and body fat percentage as input and creates a 3D avatar based on it. The avatar creation unit can also display the 3D avatar in real time using rendering technology. For example, the avatar creation unit creates a 3D avatar based on the user's physical data and displays it in real time using rendering technology. This makes it possible to improve the user's training experience by creating a 3D avatar based on the exercise plan.

[0089] The avatar creation unit includes a support unit that assists user training using the created 3D avatar. The support unit provides, for example, real-time feedback. When the user is training, the support unit provides real-time feedback using the 3D avatar. For example, the support unit tracks the user's movements and reflects them in the 3D avatar to correct the user's form and posture in real time. The support unit can also provide guided exercises. For example, the support unit uses the 3D avatar to guide the user through the exercise procedures and movements. This allows for improved training effectiveness by supporting training using 3D avatars.

[0090] The emotion estimation unit determines an encouraging message based on the estimated emotion, and the delivery unit delivers that message. For example, if the user is tired, the emotion estimation unit might determine an encouraging message such as, "Let's keep going!" The delivery unit then delivers the determined encouraging message to the user. For example, the delivery unit displays the message on the user's device. The delivery unit can also deliver the message via voice. For example, the delivery unit delivers the encouraging message audibly through a speaker. This allows the user to maintain motivation by providing encouraging messages based on their emotions.

[0091] The decision-making unit determines a cooperative exercise plan based on the emotional combination of the training pair or group. For example, if one partner is tired, the unit will determine an exercise plan that allows the other partner to provide support. The decision-making unit can also determine a cooperative exercise plan based on the emotional combination of the group. For example, the unit will determine an exercise plan that allows the most motivated member of the group to take on a leadership role. This maximizes the effectiveness of training by providing exercise plans tailored to the emotional needs of the pair or group.

[0092] The data acquisition unit can estimate the user's emotions and adjust the timing of acquiring emotion estimation information based on the estimated emotions. For example, if the user is stressed, the data acquisition unit increases the frequency of acquiring emotion estimation information to track emotional changes in real time. If the user is relaxed, the data acquisition unit can also decrease the frequency of acquiring emotion estimation information to acquire only the minimum necessary information. Furthermore, if the user is exercising, the data acquisition unit can acquire emotion estimation information in accordance with the exercise intervals to evaluate the effects of exercise. For example, if the user is stressed, the data acquisition unit frequently acquires heart rate and facial expression data to track emotional changes in real time. If the user is relaxed, it reduces the frequency of acquiring voice data and ambient sounds to acquire only the minimum necessary information. If the user is exercising, it acquires emotion estimation information in accordance with the exercise intervals to evaluate the effects of exercise. This allows for more accurate emotion estimation by adjusting the timing of acquiring emotion estimation information based on the user's emotions.

[0093] The data acquisition unit can analyze the user's past emotional data and select the optimal acquisition method. For example, the acquisition unit can identify time periods in the user's past when they experienced high stress and prioritize acquiring emotional estimation information during those time periods. The acquisition unit can also analyze environmental conditions in which the user was relaxed in the past and prioritize acquiring emotional estimation information under those conditions. Furthermore, the acquisition unit can analyze the emotional response to specific triggers (e.g., music, exercise) from the user's past emotional data and acquire emotional estimation information when those triggers occur. For example, the acquisition unit can identify time periods in the user's past when they experienced high stress and prioritize acquiring emotional estimation information during those time periods. It can analyze environmental conditions in which the user was relaxed in the past and prioritize acquiring emotional estimation information under those conditions. It can analyze the emotional response to specific triggers (e.g., music, exercise) from the user's past emotional data and acquire emotional estimation information when those triggers occur. In this way, by analyzing past emotional data, the optimal method for acquiring emotional estimation information can be selected.

[0094] The data acquisition unit can filter emotion estimation information based on the user's current activity status and environment. For example, if the user is exercising, the unit filters emotion estimation information based on the type and intensity of the exercise to obtain appropriate data. If the user is resting, the unit can also filter emotion estimation information considering ambient sounds and surrounding conditions. Furthermore, if the user is in a stressful environment, the unit can filter out stressors specific to that environment and acquire emotion estimation information. For example, if the user is exercising, the unit filters emotion estimation information based on the type and intensity of the exercise to obtain appropriate data. If the user is resting, it filters emotion estimation information considering ambient sounds and surrounding conditions. If the user is in a stressful environment, it filters out stressors specific to that environment and acquires emotion estimation information. This allows for the acquisition of appropriate data by filtering emotion estimation information based on the user's activity status and environment.

[0095] The data acquisition unit can estimate the user's emotions and determine the priority of emotion estimation information to acquire based on the estimated emotions. For example, if the user is stressed, the data acquisition unit will prioritize acquiring heart rate and facial expression data. If the user is relaxed, the data acquisition unit can also prioritize acquiring voice data and ambient sounds. Furthermore, if the user is exercising, the data acquisition unit can also prioritize acquiring exercise data and respiratory data. For example, if the user is stressed, the data acquisition unit will prioritize acquiring heart rate and facial expression data. If the user is relaxed, it will prioritize acquiring voice data and ambient sounds. If the user is exercising, it will prioritize acquiring exercise data and respiratory data. By determining the priority of emotion estimation information based on the user's emotions, important information can be acquired preferentially.

[0096] The data acquisition unit can prioritize acquiring highly relevant information when acquiring emotion estimation information, taking into account the user's geographical location. For example, if the user is in a park, the data acquisition unit will prioritize acquiring emotion estimation information related to the natural environment. If the user is in a gym, the data acquisition unit can also prioritize acquiring emotion estimation information related to exercise. Furthermore, if the user is at home, the data acquisition unit can also prioritize acquiring emotion estimation information related to a relaxing environment. In this way, by taking the user's geographical location into consideration, the data acquisition unit can prioritize acquiring highly relevant emotion estimation information.

[0097] The acquisition unit can analyze the user's social media activity and acquire relevant information when acquiring information for emotion estimation. For example, if a user posts on social media expressing stress, the acquisition unit can acquire the content of that post as emotion estimation information. The acquisition unit can also acquire the content of a user's social media post expressing relaxation as emotion estimation information. Furthermore, if a user posts on social media about exercise, the acquisition unit can also acquire the content of that post as emotion estimation information. For example, if a user posts on social media expressing stress, the acquisition unit can acquire the content of that post as emotion estimation information. If a user posts on social media expressing relaxation, the acquisition unit can acquire the content of that post as emotion estimation information. If a user posts on social media about exercise, the acquisition unit can acquire the content of that post as emotion estimation information. In this way, by analyzing social media activity, information related to the user's emotions can be acquired.

[0098] The emotion estimation unit can estimate the user's emotions and adjust the emotion estimation algorithm based on the estimated user emotions. For example, if the user is feeling stressed, the emotion estimation unit can adjust the emotion estimation algorithm to prioritize stress-related data. If the user is relaxed, the emotion estimation unit can also adjust the emotion estimation algorithm to prioritize relaxation-related data. Furthermore, if the user is exercising, the emotion estimation unit can adjust the emotion estimation algorithm to prioritize exercise-related data. For example, if the user is feeling stressed, the emotion estimation unit adjusts the emotion estimation algorithm to prioritize stress-related data. If the user is relaxed, the emotion estimation algorithm adjusts the emotion estimation algorithm to prioritize relaxation-related data. If the user is exercising, the emotion estimation algorithm adjusts the emotion estimation algorithm to prioritize exercise-related data. By adjusting the emotion estimation algorithm based on the user's emotions, the estimation accuracy is improved.

[0099] The emotion estimation unit can improve its estimation accuracy by referring to the user's past emotion data during emotion estimation. For example, the emotion estimation unit can refer to the user's past emotion data and reflect it in the current emotion estimation. The emotion estimation unit can also extract specific patterns from the user's past emotion data to improve estimation accuracy. Furthermore, the emotion estimation unit can analyze the user's past emotion data and feed the results back into the emotion estimation algorithm. For example, the emotion estimation unit can refer to the user's past emotion data and reflect it in the current emotion estimation. It can extract specific patterns from the user's past emotion data to improve estimation accuracy. It can analyze the user's past emotion data and feed the results back into the emotion estimation algorithm. As a result, the accuracy of emotion estimation is improved by referring to past emotion data.

[0100] The emotion estimation unit can correct the estimation results based on the user's current activity status and environment during emotion estimation. For example, if the user is exercising, the emotion estimation unit corrects the emotion estimation results based on the type and intensity of the exercise. If the user is resting, the emotion estimation unit can also correct the emotion estimation results by considering ambient sounds and surrounding conditions. Furthermore, if the user is in a stressful environment, the emotion estimation unit can also correct the emotion estimation results by considering stress factors specific to that environment. For example, if the user is exercising, the emotion estimation unit corrects the emotion estimation results based on the type and intensity of the exercise. If the user is resting, it corrects the emotion estimation results by considering ambient sounds and surrounding conditions. If the user is in a stressful environment, it corrects the emotion estimation results by considering stress factors specific to that environment. By correcting the estimation results based on the user's activity status and environment, more accurate emotion estimation becomes possible.

[0101] The emotion estimation unit can estimate the user's emotions and adjust the display method of the emotion estimation results based on the estimated user emotions. For example, if the user is feeling stressed, the emotion estimation unit provides a simple and highly visible display method. If the user is relaxed, the emotion estimation unit can also provide a display method that includes detailed information. Furthermore, if the user is exercising, the emotion estimation unit can provide a display method that integrates exercise data and emotion data. For example, if the user is feeling stressed, the emotion estimation unit provides a simple and highly visible display method. If the user is relaxed, it provides a display method that includes detailed information. If the user is exercising, it provides a display method that integrates exercise data and emotion data. This allows for a highly visible display by adjusting the display method based on the user's emotions.

[0102] The emotion estimation unit can correct the estimation results by considering the user's geographical location information during emotion estimation. For example, if the user is in a park, the emotion estimation unit can correct the emotion estimation results related to the natural environment. If the user is in a gym, the emotion estimation unit can also correct the emotion estimation results related to exercise. Furthermore, if the user is at home, the emotion estimation unit can also correct the emotion estimation results related to a relaxing environment. For example, if the user is in a park, the emotion estimation unit can correct the emotion estimation results related to the natural environment. If the user is in a gym, it can correct the emotion estimation results related to exercise. If the user is at home, it can correct the emotion estimation results related to a relaxing environment. This makes it possible to estimate emotions more accurately by considering geographical location information.

[0103] The emotion estimation unit can analyze the user's social media activity and adjust the estimation results during emotion estimation. For example, if a user posts on social media expressing stress, the emotion estimation unit will reflect the content of that post in the emotion estimation result. The emotion estimation unit can also reflect the content of a user's posts expressing relaxation in the emotion estimation result. Furthermore, if a user posts on social media about exercise, the emotion estimation unit can also reflect the content of that post in the emotion estimation result. For example, if a user posts on social media expressing stress, the emotion estimation unit will reflect the content of that post in the emotion estimation result. If a user posts on social media expressing relaxation, the content of that post will be reflected in the emotion estimation result. If a user posts on social media about exercise, the content of that post will be reflected in the emotion estimation result. This allows for more accurate emotion estimation by analyzing social media activity.

[0104] The decision-making unit can estimate the user's emotions and adjust how it determines the exercise plan based on those emotions. For example, if the user is feeling stressed, the decision-making unit will prioritize exercise plans with a relaxing effect. If the user is relaxed, the decision-making unit may also prioritize challenging exercise plans. Furthermore, if the user is exercising, the decision-making unit can adjust the exercise plan based on the type and intensity of the exercise. For example, if the user is feeling stressed, the decision-making unit will prioritize exercise plans with a relaxing effect. If the user is relaxed, it will prioritize challenging exercise plans. If the user is exercising, it will adjust the exercise plan based on the type and intensity of the exercise. This allows the system to provide the optimal plan by adjusting how it determines the exercise plan based on the user's emotions.

[0105] The decision-making unit can select the optimal exercise plan by referring to the user's past training data. For example, the decision-making unit can refer to the user's past training data and select an exercise plan that matches the user's current physical condition and goals. The decision-making unit can also analyze the effects of specific exercises from the user's past training data and select the optimal plan. Furthermore, the decision-making unit can analyze the user's past training data and select an exercise plan that matches the user's training progress. For example, the decision-making unit can refer to the user's past training data and select an exercise plan that matches the user's current physical condition and goals. It can analyze the effects of specific exercises from the user's past training data and select the optimal plan. It can analyze the user's past training data and select an exercise plan that matches the user's training progress. In this way, by referring to past training data, the system can provide the user with the most suitable exercise plan.

[0106] The decision-making unit can customize the exercise plan based on the user's current physical condition and environment. For example, if the user is tired, the decision-making unit can customize a plan that includes recovery exercises and rest. If the user is healthy, the decision-making unit can also customize a challenging exercise plan. Furthermore, if the user is in a specific environment (e.g., outdoors, gym), the decision-making unit can also customize an exercise plan that is suitable for that environment. For example, if the user is tired, the decision-making unit can customize a plan that includes recovery exercises and rest. If the user is healthy, it can customize a challenging exercise plan. If the user is in a specific environment (e.g., outdoors, gym), it can customize an exercise plan that is suitable for that environment. By customizing the plan based on the user's physical condition and environment, more effective exercise becomes possible.

[0107] The decision-making unit can estimate the user's emotions and prioritize exercise plans based on those emotions. For example, if the user is feeling stressed, the decision-making unit will prioritize exercise plans with a relaxing effect. If the user is relaxed, the decision-making unit can also prioritize challenging exercise plans. Furthermore, if the user is exercising, the decision-making unit can prioritize exercise plans based on the type and intensity of the exercise. For example, if the user is feeling stressed, the decision-making unit will prioritize exercise plans with a relaxing effect. If the user is relaxed, it will prioritize challenging exercise plans. If the user is exercising, it will prioritize exercise plans based on the type and intensity of the exercise. This allows the system to provide exercise plans in the optimal order by prioritizing them based on the user's emotions.

[0108] The decision-making unit can select the optimal exercise plan by considering the user's geographical location. For example, if the user is in a park, the decision-making unit will select an exercise plan that utilizes the natural environment. If the user is in a gym, the decision-making unit can also select an exercise plan that utilizes the gym's equipment. Furthermore, if the user is at home, the decision-making unit can also select an exercise plan that can be done at home. In this way, by considering geographical location information, the system can provide the user with the most suitable exercise plan.

[0109] The decision-making unit can analyze the user's social media activity and customize the exercise plan when determining the plan. For example, the decision-making unit can customize the exercise plan based on the fitness goals the user has shared on social media. The decision-making unit can also customize the plan by referencing the training methods of fitness influencers the user follows on social media. Furthermore, the decision-making unit can also customize the exercise plan based on the fitness challenges the user is participating in on social media. For example, the decision-making unit can customize the exercise plan based on the fitness goals the user has shared on social media. It can customize the plan by referencing the training methods of fitness influencers the user follows on social media. It can customize the exercise plan based on the fitness challenges the user is participating in on social media. This allows the system to provide the user with the most suitable exercise plan by analyzing their social media activity.

[0110] The service provider can estimate the user's emotions and adjust how the exercise plan is delivered based on those emotions. For example, if the user is feeling stressed, the service provider can provide an exercise plan accompanied by relaxing music. If the user is relaxed, the service provider can also provide an exercise plan with detailed explanations. Furthermore, if the user is exercising, the service provider can adjust the exercise plan to match the intervals of the exercise. In this way, by adjusting the delivery method based on the user's emotions, the service provider can provide the optimal exercise plan.

[0111] The service provider can select the optimal delivery method when providing exercise plans by referring to the user's past feedback. For example, the service provider can prioritize the delivery method of exercise plans that the user has preferred in the past. The service provider can also select a specific delivery method (e.g., audio guide, video guide) based on the user's past feedback. Furthermore, the service provider can analyze the user's past feedback and customize the delivery method. For example, the service provider can prioritize the delivery method of exercise plans that the user has preferred in the past. They can select a specific delivery method (e.g., audio guide, video guide) based on the user's past feedback. They can analyze the user's past feedback and customize the delivery method. This allows the service provider to select the most suitable delivery method for the user by referring to past feedback.

[0112] The service provider can customize the content of the exercise plan based on the user's current physical condition and environment. For example, if the user is tired, the service provider can provide a plan that includes recovery exercises and rest. If the user is in good health, the service provider can also provide a challenging exercise plan. Furthermore, if the user is in a specific environment (e.g., outdoors, gym), the service provider can provide an exercise plan suited to that environment. For example, if the user is tired, the service provider can provide a plan that includes recovery exercises and rest. If the user is in good health, the service provider can provide a challenging exercise plan. If the user is in a specific environment (e.g., outdoors, gym), the service provider can provide an exercise plan suited to that environment. This allows the service provider to provide the optimal exercise plan by customizing the content based on the user's physical condition and environment.

[0113] The service provider can estimate the user's emotions and determine the order in which exercise plans are presented based on those emotions. For example, if the user is feeling stressed, the service provider can first provide a relaxing exercise plan. If the user is relaxed, the service provider can also first provide a challenging exercise plan. Furthermore, if the user is exercising, the service provider can determine the order of exercise plans to match the exercise intervals. For example, if the user is feeling stressed, the service provider can first provide a relaxing exercise plan. If the user is relaxed, it can first provide a challenging exercise plan. If the user is exercising, the service provider can determine the order of exercise plans to match the exercise intervals. This allows the service provider to deliver exercise plans in the optimal order by determining the order based on the user's emotions.

[0114] The service provider can select the optimal delivery method when providing exercise plans, taking into account the user's geographical location. For example, if the user is in a park, the service provider can provide an exercise plan that utilizes the natural environment. If the user is in a gym, the service provider can also provide an exercise plan that utilizes the gym's equipment. Furthermore, if the user is at home, the service provider can provide an exercise plan that can be done at home. In this way, by considering geographical location information, the service provider can provide the user with the most suitable exercise plan.

[0115] The service provider can analyze users' social media activity and customize the content of their exercise plans. For example, they can customize exercise plans based on fitness goals shared by users on social media. They can also customize plans by referencing training methods of fitness influencers that users follow on social media. Furthermore, they can customize exercise plans based on fitness challenges that users are participating in on social media. For example, they can customize exercise plans based on fitness goals shared by users on social media. They can customize plans by referencing training methods of fitness influencers that users follow on social media. They can customize exercise plans based on fitness challenges that users are participating in on social media. This allows them to provide users with the most suitable exercise plans by analyzing their social media activity.

[0116] The avatar creation unit can estimate the user's emotions and adjust the 3D avatar creation method based on those emotions. For example, if the user is feeling stressed, the avatar creation unit will create a relaxing avatar. If the user is relaxed, the avatar creation unit can also create a challenging avatar. Furthermore, if the user is exercising, the avatar creation unit can customize the avatar based on the type and intensity of the exercise. For example, if the user is feeling stressed, the avatar creation unit will create a relaxing avatar. If the user is relaxed, it will create a challenging avatar. If the user is exercising, it will customize the avatar based on the type and intensity of the exercise. This allows the system to generate the optimal avatar by adjusting the 3D avatar creation method based on the user's emotions.

[0117] The avatar creation unit can generate an optimal avatar by referencing the user's past physical data when creating a 3D avatar. For example, the avatar creation unit can refer to the user's past physical data to generate an avatar that matches their current physical condition and goals. The avatar creation unit can also generate an avatar that reflects the effects of a specific exercise based on the user's past physical data. Furthermore, the avatar creation unit can analyze the user's past physical data and generate an avatar that matches their training progress. For example, the avatar creation unit can refer to the user's past physical data to generate an avatar that matches their current physical condition and goals. It can generate an avatar that reflects the effects of a specific exercise based on the user's past physical data. It can analyze the user's past physical data and generate an avatar that matches their training progress. In this way, by referencing past physical data, it is possible to generate an optimal 3D avatar for the user.

[0118] The avatar creation function can customize 3D avatars based on the user's current physical condition and environment. For example, if the user is tired, the avatar creation function will generate an avatar that reflects recovery exercises or rest. If the user is healthy, the avatar creation function can also generate a challenging avatar. Furthermore, if the user is in a specific environment (e.g., outdoors, gym), the avatar creation function can generate an avatar suitable for that environment. For example, if the user is tired, the avatar creation function will generate an avatar that reflects recovery exercises or rest. If the user is healthy, it will generate a challenging avatar. If the user is in a specific environment (e.g., outdoors, gym), it will generate an avatar suitable for that environment. This allows for the creation of an optimal 3D avatar by customizing it based on the user's physical condition and environment.

[0119] The avatar creation unit can estimate the user's emotions and adjust the display method of the 3D avatar based on the estimated emotions. For example, if the user is feeling stressed, the avatar creation unit can provide a display method that has a relaxing effect. If the user is relaxed, the avatar creation unit can also provide a challenging display method. Furthermore, if the user is exercising, the avatar creation unit can adjust the display method based on the type and intensity of the exercise. For example, if the user is feeling stressed, the avatar creation unit can provide a display method that has a relaxing effect. If the user is relaxed, it can provide a challenging display method. If the user is exercising, it can adjust the display method based on the type and intensity of the exercise. In this way, by adjusting the display method based on the user's emotions, the optimal 3D avatar can be provided.

[0120] The avatar creation unit can generate an optimal avatar by considering the user's geographical location information when creating a 3D avatar. For example, if the user is in a park, the avatar creation unit will generate an avatar that reflects the natural environment. If the user is in a gym, the avatar creation unit can also generate an avatar that reflects the gym's equipment. Furthermore, if the user is at home, the avatar creation unit can generate an avatar that reflects the user's home environment. For example, if the user is in a park, the avatar creation unit will generate an avatar that reflects the natural environment. If the user is in a gym, the avatar will generate an avatar that reflects the gym's equipment. If the user is at home, the avatar will generate an avatar that reflects the user's home environment. In this way, by considering geographical location information, it is possible to generate a 3D avatar that is optimal for the user.

[0121] The avatar creation function can analyze a user's social media activity and customize the avatar during 3D avatar creation. For example, the avatar creation function can customize the avatar based on the fitness goals the user has shared on social media. It can also customize the avatar by referencing the training methods of fitness influencers the user follows on social media. Furthermore, the avatar creation function can customize the avatar based on the fitness challenges the user is participating in on social media. For example, the avatar creation function can customize the avatar based on the fitness goals the user has shared on social media. It can customize the avatar by referencing the training methods of fitness influencers the user follows on social media. It can customize the avatar based on the fitness challenges the user is participating in on social media. This allows for the generation of the most suitable 3D avatar for each user by analyzing their social media activity.

[0122] The support unit can estimate the user's emotions and adjust the training support method based on those emotions. For example, if the user is feeling stressed, the support unit can provide a training support method with a relaxing effect. If the user is relaxed, the support unit can also provide a challenging training support method. Furthermore, if the user is exercising, the support unit can adjust the training support method based on the type and intensity of the exercise. For example, if the user is feeling stressed, the support unit can provide a training support method with a relaxing effect. If the user is relaxed, it can provide a challenging training support method. If the user is exercising, the support unit can adjust the training support method based on the type and intensity of the exercise. This allows the system to provide optimal support by adjusting the training support method based on the user's emotions.

[0123] The support department can select the optimal support method by referring to the user's past training data during training support. For example, the support department can refer to the user's past training data and select a training support method that suits the user's current physical condition and goals. The support department can also analyze the effects of specific exercises from the user's past training data and select the optimal support method. Furthermore, the support department can analyze the user's past training data and select a support method that matches the user's training progress. For example, the support department can refer to the user's past training data and select a training support method that suits the user's current physical condition and goals. It can analyze the effects of specific exercises from the user's past training data and select the optimal support method. It can analyze the user's past training data and select a support method that matches the user's training progress. In this way, the support department can select the optimal support method for the user by referring to past training data.

[0124] The support team can customize training support based on the user's current physical condition and environment. For example, if the user is tired, the support team can provide support that includes recovery exercises and rest. If the user is healthy, the support team can also provide challenging support. Furthermore, if the user is in a specific environment (e.g., outdoors, gym), the support team can provide support that is appropriate for that environment. For example, if the user is tired, the support team can provide support that includes recovery exercises and rest. If the user is healthy, the support team can provide challenging support. If the user is in a specific environment (e.g., outdoors, gym), the support team can provide support that is appropriate for that environment. This allows for optimal training support by customizing support based on the user's physical condition and environment.

[0125] The support unit can estimate the user's emotions and determine the priority of training support based on those emotions. For example, if the user is feeling stressed, the support unit will prioritize providing training support with a relaxing effect. If the user is relaxed, the support unit may also prioritize providing challenging training support. Furthermore, if the user is exercising, the support unit can also determine the priority of training support based on the type and intensity of the exercise. For example, if the user is feeling stressed, the support unit will prioritize providing training support with a relaxing effect. If the user is relaxed, it will prioritize providing challenging training support. If the user is exercising, the support unit will determine the priority of training support based on the type and intensity of the exercise. This allows the support to be provided in the optimal order by determining the priority of training support based on the user's emotions.

[0126] The support unit can select the optimal support method during training by considering the user's geographical location. For example, if the user is in a park, the support unit can provide training support methods that utilize the natural environment. If the user is in a gym, the support unit can also provide training support methods that utilize the gym's equipment. Furthermore, if the user is at home, the support unit can provide training support methods that can be done at home. For example, if the user is in a park, the support unit can provide training support methods that utilize the natural environment. If the user is in a gym, the support unit can provide training support methods that utilize the gym's equipment. If the user is at home, the support unit can provide training support methods that can be done at home. In this way, by considering geographical location information, the support unit can provide the user with the most suitable training support method.

[0127] The support team can analyze users' social media activity during training support to customize the support provided. For example, the support team can customize training support based on fitness goals shared by users on social media. The support team can also customize support by referencing the training methods of fitness influencers that users follow on social media. Furthermore, the support team can customize training support based on fitness challenges that users participate in on social media. For example, the support team can customize training support based on fitness goals shared by users on social media. The support team can customize support by referencing the training methods of fitness influencers that users follow on social media. The support team can customize training support based on fitness challenges that users participate in on social media. This allows the support team to provide users with the most suitable training support by analyzing their social media activity.

[0128] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0129] The mental support robot system can also acquire user meal data and use it for emotion estimation. The acquisition unit records the content, calories, and nutrients of the meals consumed by the user and provides this data to the emotion estimation unit. For example, if the user has consumed a high-calorie meal, the emotion estimation unit can use that data to estimate the user's energy level and satisfaction level. Also, if the user has consumed a balanced meal, the emotion estimation unit can use that data to estimate the user's health condition and stress level. In this way, utilizing meal data enables more accurate emotion estimation.

[0130] The mental support robot system can also acquire user sleep data and utilize it for emotion estimation. The acquisition unit records the user's sleep duration and quality and provides this data to the emotion estimation unit. For example, if the user is not getting enough sleep, the emotion estimation unit can use this data to estimate the user's fatigue level and stress level. Conversely, if the user is getting good quality sleep, the emotion estimation unit can use this data to estimate the user's energy level and concentration level. This allows for more accurate emotion estimation by utilizing sleep data.

[0131] The mental support robot system can also monitor the user's stress level in real time and utilize this data for emotion estimation. The acquisition unit measures the user's heart rate variability and skin electrical activity in real time and provides this data to the emotion estimation unit. For example, if the user is stressed, the emotion estimation unit can use this data to estimate the user's stress level and suggest appropriate relaxation exercises. If the user is relaxed, the emotion estimation unit can use this data to estimate the user's level of relaxation and provide advice to maintain that relaxation. This allows for more accurate emotion estimation by utilizing real-time stress monitoring.

[0132] The mental support robot system can also acquire user social activity data and utilize it for emotion estimation. The acquisition unit records the frequency and content of the user's social activities and provides this data to the emotion estimation unit. For example, if the user frequently engages in social activities, the emotion estimation unit can use this data to estimate the user's happiness level and stress level. Conversely, if the user does not engage in social activities very often, the emotion estimation unit can use this data to estimate the user's feelings of loneliness and stress level. This allows for more accurate emotion estimation by utilizing social activity data.

[0133] The mental support robot system can also acquire data on the user's hobbies and interests and use it for emotion estimation. The acquisition unit records what hobbies and interests the user has and provides this data to the emotion estimation unit. For example, if the user is engrossed in a hobby, the emotion estimation unit can use that data to estimate the user's happiness level and stress level. Also, if the user is not spending time on a hobby, the emotion estimation unit can use that data to estimate the user's stress level and motivation. In this way, by utilizing data on hobbies and interests, more accurate emotion estimation becomes possible.

[0134] The mental support robot system can also acquire the user's exercise history data and use it to optimize exercise plans. The acquisition unit records the type, frequency, and intensity of exercises the user has performed in the past and provides this data to the decision unit. For example, based on the user's past exercise data, the decision unit can propose an exercise plan tailored to the user's fitness level and preferences. Furthermore, if the user prefers a particular exercise, the system can prioritize suggesting plans that include that exercise. This allows for the provision of more effective exercise plans by utilizing exercise history data.

[0135] The mental support robot system can also acquire user health data and use it to optimize exercise plans. The acquisition unit records health data such as the user's blood pressure, blood sugar levels, and body fat percentage, and provides it to the decision unit. For example, based on the user's health data, the decision unit can propose an exercise plan tailored to the user's health condition. Furthermore, if the user has specific health goals, the system can propose a plan that aligns with those goals. This allows for the provision of more effective exercise plans by utilizing health data.

[0136] The mental support robot system can also acquire user environmental data and use it to optimize exercise plans. The acquisition unit records temperature, humidity, noise level, and other information of the environment in which the user trains, and provides this data to the decision unit. For example, if the user trains in a hot and humid environment, the decision unit can suggest an exercise plan suitable for that environment. Similarly, if the user trains in a quiet environment, the decision unit can suggest relaxation exercises suitable for that environment. In this way, by utilizing environmental data, a more effective exercise plan can be provided.

[0137] The mental support robot system can also acquire user feedback data and use it to optimize exercise plans. The acquisition unit records user feedback on past exercise plans and provides it to the decision unit. For example, if a user expresses high satisfaction with a particular exercise plan, the decision unit can suggest that plan again. Conversely, if a user expresses dissatisfaction with a particular exercise plan, the decision unit can adjust the plan to avoid that plan. In this way, by utilizing feedback data, a more effective exercise plan can be provided.

[0138] The mental support robot system can also acquire the user's geographical location data and use it to optimize the exercise plan. The acquisition unit records the geographical location data of the place where the user is training and provides it to the decision unit. For example, if the user is training in a park, the decision unit can suggest an outdoor exercise plan suitable for that location. Similarly, if the user is training in a gym, it can suggest an indoor exercise plan suitable for that location. In this way, by utilizing geographical location data, a more effective exercise plan can be provided.

[0139] The following briefly describes the processing flow for example form 2.

[0140] Step 1: The acquisition unit acquires emotion estimation information, which is information used to estimate the user's emotions. Emotion estimation information includes, for example, facial expression data, voice data, and biosensor data. The acquisition unit acquires the user's facial expression data using a camera, the user's voice data using a microphone, and biosensor data such as the user's heart rate and skin electrical activity using biosensors. Step 2: The emotion estimation unit estimates the user's emotions based on the emotion estimation information acquired by the acquisition unit. The emotion estimation unit uses AI to analyze the emotion estimation information and estimates the user's emotions through facial recognition technology, voice analysis technology, and analysis of biosensor data. Step 3: The decision unit determines an exercise plan based on the emotions estimated by the emotion estimation unit. The decision unit uses AI to determine the exercise plan, suggesting recovery exercises or rest if the user is tired, and providing encouraging messages if motivation is low. Step 4: The delivery unit provides the exercise plan determined by the decision unit. The delivery unit uses AI to suggest recovery exercises and rest periods to the user and provides encouraging messages.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] For example, the acquisition unit can acquire user facial expression data and voice data using the camera 42 and microphone 38B of the smart device 14. The emotion estimation unit is implemented by the identification processing unit 290 of the data processing device 12 and estimates the user's emotion by analyzing the emotion estimation information obtained from the acquisition unit. The decision unit is implemented by the identification processing unit 290 of the data processing device 12 and determines an exercise plan based on the estimation results of the emotion estimation unit. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the user with the determined exercise plan and encouraging messages. The correspondence between each unit and the device and control unit is not limited to the example described above and can be changed in various ways.

[0145] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0146] 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.

[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 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.

[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 (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).

[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] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] For example, the acquisition unit can acquire the user's facial expression data and voice data using the camera 42 and microphone 238 of the smart glasses 214. The emotion estimation unit is implemented by the identification processing unit 290 of the data processing device 12 and estimates the user's emotion by analyzing the emotion estimation information obtained from the acquisition unit. The decision unit is implemented by the identification processing unit 290 of the data processing device 12 and determines an exercise plan based on the estimation results of the emotion estimation unit. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the user with the determined exercise plan and encouraging messages. The correspondence between each unit and the device and control unit is not limited to the example described above and can be changed in various ways.

[0161] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] For example, the acquisition unit can acquire user facial expression data and voice data using the camera 42 and microphone 238 of the headset terminal 314. The emotion estimation unit is implemented by the identification processing unit 290 of the data processing device 12 and estimates the user's emotion by analyzing the emotion estimation information obtained from the acquisition unit. The decision unit is implemented by the identification processing unit 290 of the data processing device 12 and determines an exercise plan based on the estimation results of the emotion estimation unit. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the user with the determined exercise plan and encouraging messages. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0177] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] For example, the acquisition unit can acquire user facial expression data and voice data using the camera 42 and microphone 238 of the robot 414. The emotion estimation unit is implemented by the identification processing unit 290 of the data processing device 12 and estimates the user's emotions by analyzing the emotion estimation information obtained from the acquisition unit. The decision unit is implemented by the identification processing unit 290 of the data processing device 12 and determines an exercise plan based on the estimation results of the emotion estimation unit. The provision unit is implemented by the control unit 46A of the robot 414 and provides the user with the determined exercise plan and encouraging messages. The correspondence between each unit and the device and control unit is not limited to the example described above and can be changed in various ways.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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."

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] (Note 1) An acquisition unit that acquires emotion estimation information, which is information used to estimate the user's emotions, An emotion estimation unit estimates the user's emotions based on the emotion estimation information acquired by the acquisition unit, A decision unit that determines an exercise plan based on the emotions estimated by the emotion estimation unit, The system includes a providing unit that provides an exercise plan determined by the aforementioned determination unit. A system characterized by the following features. (Note 2) The system includes an avatar creation unit that creates a 3D avatar of the user based on the exercise plan determined by the aforementioned determination unit. The system described in Appendix 1, characterized by the features described herein. (Note 3) The system includes a support unit that assists in the training of the user using the 3D avatar created by the avatar creation unit. The system described in Appendix 2, characterized by the features described herein. (Note 4) Based on the emotion estimated by the emotion estimation unit, the provision unit determines an encouraging message, and the provision unit provides the encouraging message determined by the determination unit. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned determination unit, Determine a cooperative exercise plan based on the emotional combination of the pair or group performing the training. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of acquiring emotion estimation information based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, Analyze the user's past emotional data and select the appropriate acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, When acquiring information for sentiment estimation, filtering is performed based on the user's current activity status and environment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, It estimates the user's emotions and determines the priority of emotion estimation information to acquire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When acquiring information for sentiment estimation, the system prioritizes acquiring 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 acquisition unit is, When acquiring information for sentiment estimation, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The emotion estimation unit, The system estimates the user's emotions and adjusts the emotion estimation algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The emotion estimation unit, When estimating emotions, we improve estimation accuracy by referencing the user's past emotional data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The emotion estimation unit, During emotion estimation, the estimation results are corrected based on the user's current activity and environment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The emotion estimation unit, It estimates the user's emotions and adjusts how the emotion estimation results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The emotion estimation unit, When estimating emotions, the estimation results are corrected based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The emotion estimation unit, During sentiment estimation, the system analyzes the user's social media activity to correct the estimation results. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned determination unit, It estimates the user's emotions and adjusts how the exercise plan is determined based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned determination unit, When determining an exercise plan, the system selects an appropriate plan by referring to the user's past training data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned determination unit, When determining an exercise plan, the plan is customized based on the user's current physical condition and environment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned determination unit, The system estimates the user's emotions and prioritizes exercise plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned determination unit, When determining an exercise plan, the system selects an appropriate plan based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned determination unit, When determining an exercise plan, analyze the user's social media activity to customize the plan. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, The system estimates the user's emotions and adjusts how the exercise plan is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing exercise plans, we select the optimal delivery method by referring to the user's past feedback. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing an exercise plan, the content is customized based on the user's current physical condition and environment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, The system estimates the user's emotions and determines the order in which exercise plans are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing an exercise plan, the appropriate delivery method is selected based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing exercise plans, we analyze users' social media activity to customize the content. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned avatar creation unit is: It estimates the user's emotions and adjusts the 3D avatar creation method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned avatar creation unit is: When creating a 3D avatar, the system generates the optimal avatar by referencing the user's past physical data. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned avatar creation unit is: When creating a 3D avatar, the avatar is customized based on the user's current physical condition and environment. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned avatar creation unit is: It estimates the user's emotions and adjusts how the 3D avatar is displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned avatar creation unit is: When creating a 3D avatar, an appropriate avatar is generated based on the user's geographical location information. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned avatar creation unit is: When creating a 3D avatar, the system analyzes the user's social media activity to customize the avatar. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned support unit, It estimates the user's emotions and adjusts the training support method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned support unit, During training support, the system selects the optimal support method by referring to the user's past training data. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned support unit, During training support, the support content is customized based on the user's current physical condition and environment. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned support unit, It estimates the user's emotions and determines the priority of training support based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned support unit, During training support, the appropriate support method is selected based on the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned support unit, During training support, analyze the user's social media activity to customize the support content. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0213] 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. An acquisition unit that acquires emotion estimation information, which is information used to estimate the user's emotions, An emotion estimation unit estimates the user's emotions based on the emotion estimation information acquired by the acquisition unit, A decision unit that determines an exercise plan based on the emotions estimated by the emotion estimation unit, The system includes a providing unit that provides an exercise plan determined by the aforementioned determination unit. A system characterized by the following features.

2. The system includes an avatar creation unit that creates a 3D avatar of the user based on the exercise plan determined by the aforementioned determination unit. The system according to feature 1.

3. The system includes a support unit that assists in the training of the user using the 3D avatar created by the avatar creation unit. The system according to feature 2.

4. Based on the emotion estimated by the emotion estimation unit, the provision unit determines an encouraging message, and the provision unit provides the encouraging message determined by the determination unit. The system according to feature 1.

5. The aforementioned determination unit, Determine a cooperative exercise plan based on the emotional combination of the pair or group performing the training. The system according to feature 1.

6. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of acquiring emotion estimation information based on the estimated user's emotions. The system according to feature 1.

7. The acquisition unit is, The user's past emotional data is analyzed, and an appropriate acquisition method is selected. The system according to feature 1.

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