system

Abstract

An object of a system according to an embodiment is to provide coaching from a professional athlete in VR and enhance the sports motivation of a user.SOLUTION: A system according to an embodiment includes a VR headset, a synthetic voice AI, and a conversational AI. The VR headset provides the user with a VR environment. The synthesized voice AI reproduces the voices of professional athletes. The conversational AI interacts with the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there were limited opportunities to receive coaching from professional athletes, and there was a lack of means to increase motivation for sports.

[0005] The system according to the embodiment aims to provide coaching from professional athletes in VR, thereby increasing users' motivation to play sports. [Means for solving the problem]

[0006] The system according to the embodiment includes a VR headset, a synthetic voice AI, and a conversation AI. The VR headset provides a VR environment to the user. The synthetic voice AI reproduces the voices of professional athletes. The conversation AI converses with the user. [Effects of the Invention]

[0007] The system according to the embodiment can provide coaching from professional athletes in VR, thereby increasing the user's motivation to play sports. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A coaching system according to an embodiment of the present invention is a system that allows users to receive coaching from professional athletes using VR technology. This system is realized by combining VR technology, synthetic voice AI, and conversation AI. As a result, the coaching system allows users to receive coaching from professional athletes, thereby increasing their motivation for sports.

[0029] A coaching system according to an embodiment includes a VR headset, a synthetic voice AI, and a conversational AI. The VR headset allows a user to enter a virtual space. For example, a user can wear the VR headset and enter a virtual space that recreates the environment in which a professional athlete actually trains. The synthetic voice AI realistically reproduces the voice of a professional athlete. For example, the synthetic voice AI can call out the name of a player selected by the user and provide specific instructions in their voice. The conversational AI interacts with the user in real time. For example, the conversational AI provides appropriate answers and advice to the user's questions. This allows the user to receive coaching from a professional athlete in the VR space.

[0030] VR headsets can analyze a user's movements in real time and provide feedback by comparing them with the movements of professional athletes. For example, a VR headset can capture the movements a user makes in a VR space in real time and compare that data with the movement data of a professional athlete. For example, a VR headset can analyze a soccer shooting motion and provide feedback on which parts of the motion differ from those of a professional athlete. This allows the user to receive feedback on their movements in real time.

[0031] VR headsets can record a user's movements as a 3D model and later play them back to allow for self-evaluation. VR headsets provide a function that records a user's movements as a 3D model and later plays them back to allow for self-evaluation. For example, a soccer dribbling motion can be recorded as a 3D model and later played back to allow for the user to check their movements. This allows the user to check their own movements later and perform self-evaluation.

[0032] VR headsets can store training data in the cloud and share it with other users to promote competition and cooperation. VR headsets provide the ability to store a user's training data in the cloud and share it with other users. For example, a user can store soccer training data in the cloud and compete against other users. This allows a user to share training data with other users, promoting competition and cooperation.

[0033] VR headsets can add different sports and training scenarios, allowing users to experience a variety of training. VR headsets can add different sports and training scenarios, allowing users to experience a variety of training. For example, they can provide training scenarios for soccer, basketball, tennis, etc. This allows users to experience a variety of training.

[0034] Synthetic speech AI can achieve more natural conversations by analyzing the user's pronunciation and intonation and adjusting the pronunciation accordingly. For example, when a user asks a question, the synthetic speech AI will respond with a natural intonation. This makes conversations with the user more natural.

[0035] Synthetic voice AI enables coaching in multiple languages, making it possible to cater to international users. Synthetic voice AI enables coaching in multiple languages, for example, providing coaching in English, French, Chinese, and other languages, making it possible to cater to international users.

[0036] Synthetic voice AI can address users not only by their name, but also by nicknames and pet names. Synthetic voice AI provides the ability to address users not only by their name, but also by nicknames and pet names. For example, instead of just calling them "Mr. / Ms. XX," it can address them as "Mr. / Ms. XX" or "Mr. / Ms. XX." This allows users to be addressed not only by their name, but also by their nicknames and pet names.

[0037] Conversational AI can provide individually customized advice based on the user's training history and performance data. For example, conversational AI can analyze the user's training history and performance data to provide individually customized advice. For example, it can suggest the next training content based on the user's past training data. This makes it possible to provide individually customized advice to the user.

[0038] Conversational AI adds a function that allows users to record their thoughts and feedback after training, allowing them to evaluate the effectiveness of their training. Conversational AI provides a function that allows users to record their thoughts and feedback after training. For example, users can record their thoughts on training by voice and check them later. This allows users to record their thoughts and feedback after training and evaluate the effectiveness of their training.

[0039] Conversational AI can add a community function that allows users to share their training results and challenges with other users. Conversational AI, for example, provides a community function that allows users to share their training results and challenges with other users. For example, a user can post their training results and receive feedback from other users. This allows users to share their training results and challenges with other users.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The coaching system can provide a customized training plan based on the user's training data. For example, it can analyze the user's past training data and suggest the next training content. This allows the user to receive a training plan that is optimal for them. The training plan can also be dynamically adjusted according to the user's goals and progress. Furthermore, the training plan may also include rest days and recovery sessions that take into account the user's physical condition and fatigue level.

[0042] The coaching system can store a user's training data in the cloud and share it with other users to promote competition and cooperation. For example, a user can store their training data in the cloud and compete with other users. This allows users to share their training data with other users, promoting competition and cooperation. Furthermore, by sharing data on the cloud, users can learn from the training methods and results of other users. Furthermore, sharing training data contributes to the formation of a community among users.

[0043] The coaching system can provide personalized advice based on the user's training history and performance data. For example, it can analyze the user's past training data and suggest the next training content. This allows the system to provide personalized advice to the user. The advice can also be dynamically adjusted based on the user's goals and progress. Furthermore, the advice may also include rest days and recovery sessions that take into account the user's physical condition and fatigue level.

[0044] The coaching system can add a community function that allows users to share their training results and challenges with other users. For example, a user can post their training results and receive feedback from other users. This allows users to share their training results and challenges with other users. The community function also promotes interaction between users and contributes to improving motivation. Furthermore, by sharing information within the community, users can gain new training methods and ideas.

[0045] The coaching system can add a function that allows users to record their thoughts and feedback after training, and evaluate the effectiveness of the training. For example, users can record their thoughts and feedback after training and check it later. This allows users to record their thoughts and feedback after training and evaluate the effectiveness of the training. The feedback can also be used to adjust the next training plan. Furthermore, user feedback can also be used to improve the system as a whole.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: A VR headset allows users to enter a virtual space. For example, a user can put on a VR headset and enter a virtual space that replicates the environment in which a professional athlete actually trains. Step 2: The synthetic voice AI realistically reproduces the voices of professional athletes. For example, the synthetic voice AI can call out the names of athletes selected by the user and give specific instructions. Step 3: The conversational AI interacts with the user in real time. For example, the conversational AI provides appropriate answers and advice to the user's questions. This allows the user to receive coaching from a professional athlete in the VR space.

[0048] (Example 2) A coaching system according to an embodiment of the present invention is a system that allows users to receive coaching from professional athletes using VR technology. This system is realized by combining VR technology, synthetic voice AI, and conversation AI. As a result, the coaching system allows users to receive coaching from professional athletes, thereby increasing their motivation for sports.

[0049] A coaching system according to an embodiment includes a VR headset, a synthetic voice AI, and a conversational AI. The VR headset allows a user to enter a virtual space. For example, a user can wear the VR headset and enter a virtual space that recreates the environment in which a professional athlete actually trains. The synthetic voice AI realistically reproduces the voice of a professional athlete. For example, the synthetic voice AI can call out the name of a player selected by the user and provide specific instructions in their voice. The conversational AI interacts with the user in real time. For example, the conversational AI provides appropriate answers and advice to the user's questions. This allows the user to receive coaching from a professional athlete in the VR space.

[0050] VR headsets can analyze a user's movements in real time and provide feedback by comparing them with the movements of professional athletes. For example, a VR headset can capture the movements a user makes in a VR space in real time and compare that data with the movement data of a professional athlete. For example, a VR headset can analyze a soccer shooting motion and provide feedback on which parts of the motion differ from those of a professional athlete. This allows the user to receive feedback on their movements in real time.

[0051] VR headsets can record a user's movements as a 3D model and later play them back to allow for self-evaluation. VR headsets provide a function that records a user's movements as a 3D model and later plays them back to allow for self-evaluation. For example, a soccer dribbling motion can be recorded as a 3D model and later played back to allow for the user to check their movements. This allows the user to check their own movements later and perform self-evaluation.

[0052] The VR headset uses an emotion estimation function to estimate the user's emotions from their facial expressions and tone of voice, and can display encouraging messages if their motivation is declining. For example, the VR headset provides a function to estimate emotions by analyzing the user's facial expressions and tone of voice. For example, if the user is tired, an encouraging message can be displayed. This allows the user's motivation to be maintained by displaying an encouraging message if their motivation is declining.

[0053] VR headsets can store training data in the cloud and share it with other users to promote competition and cooperation. VR headsets provide the ability to store a user's training data in the cloud and share it with other users. For example, a user can store soccer training data in the cloud and compete against other users. This allows a user to share training data with other users, promoting competition and cooperation.

[0054] VR headsets can add different sports and training scenarios, allowing users to experience a variety of training. VR headsets can add different sports and training scenarios, allowing users to experience a variety of training. For example, they can provide training scenarios for soccer, basketball, tennis, etc. This allows users to experience a variety of training.

[0055] The VR headset can use the emotion estimation function to analyze how the user feels about a specific training session and suggest an optimal training plan. For example, the VR headset can use the emotion estimation function to analyze how the user feels about a specific training session. For example, if the user feels positive about soccer training, the VR headset can suggest a soccer training plan. This allows the VR headset to suggest an optimal training plan based on the user's emotions.

[0056] Synthetic speech AI can achieve more natural conversations by analyzing the user's pronunciation and intonation and adjusting the pronunciation accordingly. For example, when a user asks a question, the synthetic speech AI will respond with a natural intonation. This makes conversations with the user more natural.

[0057] The synthetic voice AI uses its emotion estimation function to generate synthetic voice with a tone and tempo that corresponds to the user's emotions, enabling it to provide more emotional coaching. For example, the synthetic voice AI uses its emotion estimation function to generate synthetic voice with a tone and tempo that corresponds to the user's emotions. For example, if the user is feeling down, coaching can be provided in an encouraging tone. This allows coaching to be provided in a tone and tempo that corresponds to the user's emotions.

[0058] Synthetic voice AI enables coaching in multiple languages, making it possible to cater to international users. Synthetic voice AI enables coaching in multiple languages, for example, providing coaching in English, French, Chinese, and other languages, making it possible to cater to international users.

[0059] Synthetic voice AI can address users not only by their name, but also by nicknames and pet names. Synthetic voice AI provides the ability to address users not only by their name, but also by nicknames and pet names. For example, instead of just calling them "Mr. / Ms. XX," it can address them as "Mr. / Ms. XX" or "Mr. / Ms. XX." This allows users to be addressed not only by their name, but also by their nicknames and pet names.

[0060] The synthetic voice AI can use its emotion estimation function to analyze how a user feels about a specific instruction and adjust the content and method of the instruction. For example, the synthetic voice AI can use its emotion estimation function to analyze how a user feels about a specific instruction. For example, if the user feels anxious about the instruction, it can adjust the content of the instruction. This makes it possible to adjust the content and method of the instruction according to the user's emotions.

[0061] Conversational AI can provide individually customized advice based on the user's training history and performance data. For example, conversational AI can analyze the user's training history and performance data to provide individually customized advice. For example, it can suggest the next training content based on the user's past training data. This makes it possible to provide individually customized advice to the user.

[0062] Conversational AI can use emotion estimation to analyze the emotions expressed in response to user questions and feedback, and provide appropriate answers based on those emotions. For example, if the user is feeling anxious, the conversational AI can provide a reassuring answer. This allows the AI ​​to provide appropriate answers based on the user's emotions.

[0063] Conversational AI adds a function that allows users to record their thoughts and feedback after training, allowing them to evaluate the effectiveness of their training. Conversational AI provides a function that allows users to record their thoughts and feedback after training. For example, users can record their thoughts on training by voice and check them later. This allows users to record their thoughts and feedback after training and evaluate the effectiveness of their training.

[0064] Conversational AI can add a community function that allows users to share their training results and challenges with other users. Conversational AI, for example, provides a community function that allows users to share their training results and challenges with other users. For example, a user can post their training results and receive feedback from other users. This allows users to share their training results and challenges with other users.

[0065] The conversational AI can use its emotion estimation function to analyze the stress and fatigue a user feels during training and suggest appropriate rest and recovery. For example, the conversational AI can use its emotion estimation function to analyze the stress and fatigue a user feels during training and suggest appropriate rest and recovery. For example, if the user is tired, it will suggest taking a break. This makes it possible to suggest appropriate rest and recovery according to the user's stress and fatigue.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The coaching system can provide a customized training plan based on the user's training data. For example, it can analyze the user's past training data and suggest the next training content. This allows the user to receive a training plan that is optimal for them. The training plan can also be dynamically adjusted according to the user's goals and progress. Furthermore, the training plan may also include rest days and recovery sessions that take into account the user's physical condition and fatigue level.

[0068] The coaching system can store a user's training data in the cloud and share it with other users to promote competition and cooperation. For example, a user can store their training data in the cloud and compete with other users. This allows users to share their training data with other users, promoting competition and cooperation. Furthermore, by sharing data on the cloud, users can learn from the training methods and results of other users. Furthermore, sharing training data contributes to the formation of a community among users.

[0069] The coaching system can provide personalized advice based on the user's training history and performance data. For example, it can analyze the user's past training data and suggest the next training content. This allows the system to provide personalized advice to the user. The advice can also be dynamically adjusted based on the user's goals and progress. Furthermore, the advice may also include rest days and recovery sessions that take into account the user's physical condition and fatigue level.

[0070] The coaching system can add a community function that allows users to share their training results and challenges with other users. For example, a user can post their training results and receive feedback from other users. This allows users to share their training results and challenges with other users. The community function also promotes interaction between users and contributes to improving motivation. Furthermore, by sharing information within the community, users can gain new training methods and ideas.

[0071] The coaching system can add a function that allows users to record their thoughts and feedback after training, and evaluate the effectiveness of the training. For example, users can record their thoughts and feedback after training and check it later. This allows users to record their thoughts and feedback after training and evaluate the effectiveness of the training. The feedback can also be used to adjust the next training plan. Furthermore, user feedback can also be used to improve the system as a whole.

[0072] The coaching system can estimate the user's emotions and adjust the training content based on the estimated emotions. For example, if the user is tired, it can suggest a lighter training session. This allows the system to provide training content that is appropriate for the user's emotions. The emotion estimation function can also display encouraging messages if the user's motivation is low. Furthermore, the emotion estimation function may suggest recovery sessions to reduce the user's stress and fatigue.

[0073] The coaching system can estimate the user's emotions and suggest a training plan based on the estimated emotions. For example, if the user has positive emotions about a specific training, the system will suggest a plan centered around that training. This allows the system to provide an optimal training plan based on the user's emotions. The emotion estimation function can also adjust the training content if the user has negative emotions. Furthermore, the emotion estimation function may display encouraging messages to maintain the user's motivation.

[0074] The coaching system can estimate the user's emotions and adjust the tone and tempo of the synthetic voice based on the estimated emotions. For example, if the user is feeling down, the coaching system can provide coaching in an encouraging tone. This allows coaching to be provided in a tone and tempo that matches the user's emotions. The emotion estimation function can also provide instructions in a calm tone if the user is excited. Furthermore, the emotion estimation function can adjust the content of the synthetic voice based on the user's emotions.

[0075] The coaching system can estimate the user's emotions and adjust the conversational AI's responses based on the estimated emotions. For example, if the user is feeling anxious, the system can respond in a reassuring manner. This allows the system to provide appropriate responses according to the user's emotions. The emotion estimation function can also respond in a calm tone if the user is excited. Furthermore, the emotion estimation function can adjust the conversational AI's advice according to the user's emotions.

[0076] The coaching system can estimate the user's emotions and suggest rest and recovery times during training based on the estimated emotions. For example, if the user is tired, a break is suggested. This allows appropriate rest and recovery to be provided according to the user's emotions. The emotion estimation function can also suggest relaxation exercises if the user is feeling stressed. Furthermore, the emotion estimation function may adjust the training content according to the user's emotions.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: A VR headset allows users to enter a virtual space. For example, a user can put on a VR headset and enter a virtual space that replicates the environment in which a professional athlete actually trains. Step 2: The synthetic voice AI realistically reproduces the voices of professional athletes. For example, the synthetic voice AI can call out the names of athletes selected by the user and give specific instructions. Step 3: The conversational AI interacts with the user in real time. For example, the conversational AI provides appropriate answers and advice to the user's questions. This allows the user to receive coaching from a professional athlete in the VR space.

[0079] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0080] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0081] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0084] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0085] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0086] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0087] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0088] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0089] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0090] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0091] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0092] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0093] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 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.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0120] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0129] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0130] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0131] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0132] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0133] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0134] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0135] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0136] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0137] 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.

[0138] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0139] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0140] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0141] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0142] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0143] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0144] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0145] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0146] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The VR headset; A synthetic voice AI that reproduces the voices of professional athletes, A conversational AI that interacts with the user. A system characterized by:

2. The VR headset is Analyzing the user's movements in real time and comparing them with the movements of the professional athlete to provide feedback 2. The system of claim 1.

3. The VR headset is Store your training data in the cloud and share it with other users to promote competition and collaboration 2. The system of claim 1.

4. The conversational AI is Providing personalized advice based on the user's training history and performance data 2. The system of claim 1.

5. The VR headset is The user's emotions are estimated from the facial expressions and tone of voice of the user, and an encouraging message is displayed if the user's motivation is low.

2. The system of claim 1.

6. The synthetic voice AI is: The synthesized voice is generated with a tone and tempo corresponding to the user's emotions, thereby providing more emotional coaching.

2. The system of claim 1.

7. The synthetic voice AI is: Analyzing how the user feels about a specific instruction and adjusting the content and method of the instruction 2. The system of claim 1.

8. The conversational AI is Analyze the stress and fatigue the user feels during training and suggest appropriate rest and recovery.

2. The system of claim 1.

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