System

Abstract

An object of the system according to the embodiment is to allow sports enthusiasts and athletes to perform effective information exchange and training in real time.SOLUTION: A system includes a conversion unit, an analysis unit, a provision unit, and a chat unit. The conversion unit converts the user's speech into text in real time. The analysis unit analyzes the text converted by the conversion unit. The providing unit provides advice based on the analysis result obtained by the analysis unit. The chat unit performs a video chat with a friend or a team member.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] Conventional technology has made it difficult for sports enthusiasts and athletes to exchange information and train effectively in real time.

[0005] The system according to the embodiment aims to enable sports enthusiasts and athletes to exchange information and train effectively in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversion unit, an analysis unit, a provision unit, and a chat unit. The conversion unit converts a user's speech into text in real time. The analysis unit analyzes the text converted by the conversion unit. The provision unit provides advice based on the analysis results obtained by the analysis unit. The chat unit performs video chats with friends or team members. [Effects of the Invention]

[0007] The system according to the embodiment allows sports enthusiasts and athletes to exchange information and train effectively in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A mobile app according to an embodiment of the present invention is a system that allows sports enthusiasts and athletes to exchange sports-related information and train through real-time video chats with friends and team members in various regions. This system utilizes a generative AI to convert the user's speech into text in real time and provide training and tactical advice as needed. For example, a user launches the app and starts a video chat with a friend or team member. The generative AI then converts the user's speech into text in real time. For example, if the user says, "What tactics should we use in the next game?", the text is displayed. The generative AI then analyzes the converted speech and provides appropriate training and tactical advice. For example, advice such as, "It would be a good idea to adopt a zone defense to strengthen your defense in the next game" is displayed. Furthermore, users can create training menus within the app and share them with friends and team members. The generative AI analyzes the user's training and suggests effective training methods. For example, advice such as, "Stretching before running can reduce the risk of injury" is provided. This allows the mobile app to enable sports enthusiasts and athletes to exchange information and train in real time with friends and team members from different regions, enabling them to practice effective training and tactics with the support of generative AI.This allows the mobile app to enable sports enthusiasts and athletes to exchange information and train in real time with friends and team members from different regions, enabling them to practice effective training and tactics with the support of generative AI.

[0029] A mobile app according to an embodiment includes a conversion unit, an analysis unit, a provision unit, and a chat unit. The conversion unit converts a user's speech into text in real time. For example, the conversion unit converts a user's speech into text in real time using a generation AI. The conversion unit can also convert the speech into text when the generation AI receives a prompt such as, "Please convert this speech into text." The conversion unit can also enable the generation AI to understand the context of the speech and perform appropriate text conversion. The analysis unit analyzes the text converted by the conversion unit. For example, the analysis unit can analyze the content of the text using a generation AI. The analysis unit can also analyze the content of the text when the generation AI receives a prompt such as, "Please analyze the content of this text." The analysis unit can also enable the generation AI to understand the context of the text and perform appropriate analysis. The provision unit provides advice based on the analysis results obtained by the analysis unit. For example, the provision unit can provide training or tactical advice based on the analysis results using the generation AI. The provision unit can also provide advice when the generation AI receives a prompt such as, "Please provide advice based on the analysis results." The providing unit also enables the generation AI to understand the analysis results and provide appropriate advice. The chat unit performs video chats with friends and team members. For example, the chat unit uses the generation AI to improve the quality of the video chat. The chat unit also enables the generation AI to receive a prompt such as "Please improve the quality of this video chat" and improve the quality of the video chat. The chat unit also enables the generation AI to understand the context of the video chat and make appropriate quality improvements. As a result, the mobile app according to the embodiment converts the user's speech into text in real time and provides advice based on the analysis results, thereby enabling effective information exchange and training related to sports.

[0030] The mobile app includes a menu creation unit that creates a training menu and shares it with friends and team members. The menu creation unit creates a training menu using a generation AI. The menu creation unit can also create a training menu when the generation AI receives a prompt such as "Please create this training menu." The menu creation unit can also have the generation AI analyze the contents of the training menu and suggest an appropriate menu. This allows for effective training by creating and sharing a training menu.

[0031] The mobile app includes a training analysis unit that analyzes training content and suggests effective training methods. The training analysis unit analyzes the training content and suggests effective training methods. For example, the training analysis unit analyzes the training content using a generation AI. The training analysis unit can also analyze the training content when the generation AI receives a prompt such as "Please analyze this training content." The training analysis unit can also allow the generation AI to understand the training content and suggest an appropriate training method. In this way, the effectiveness of training can be improved by analyzing the training content and suggesting an effective training method.

[0032] The conversion unit can convert the user's speech into text in real time. For example, the conversion unit can convert the user's speech into text using a generation AI. For example, the conversion unit can convert the speech content into text when the generation AI receives a prompt such as "Please convert this speech into text." The conversion unit can also enable the generation AI to understand the context of the speech and perform appropriate text conversion. This enables rapid information exchange by converting the user's speech into text in real time.

[0033] The analysis unit can analyze the content of the utterance converted into text. The analysis unit analyzes the content of the text, for example, using the generation AI. For example, the analysis unit can analyze the content of the text when the generation AI receives a prompt such as "Please analyze the content of this text." The analysis unit can also enable the generation AI to understand the context of the text and perform an appropriate analysis. This makes it possible to provide appropriate advice by analyzing the content of the utterance converted into text.

[0034] The providing unit can provide training or tactical advice based on the analysis results. For example, the providing unit can provide training or tactical advice based on the analysis results using a generation AI. For example, the providing unit can provide advice when the generation AI receives a prompt such as "Please provide advice based on this analysis result." The providing unit can also enable the generation AI to understand the analysis results and provide appropriate advice. This makes it possible to improve the user's performance by providing training or tactical advice based on the analysis results.

[0035] The conversion unit can analyze background sounds of the speech and remove noise to improve the accuracy of the text conversion. For example, the conversion unit uses a generation AI to analyze background sounds of the speech and remove noise to improve the accuracy of the text conversion. For example, the conversion unit can improve the accuracy of the text conversion of the speech by using a generation AI to detect ambient noise and perform noise filtering. The conversion unit can also remove noise in a specific frequency band and convert clear speech into text. The conversion unit can also use a generation AI to identify background music and environmental sounds and remove them to convert the speech into text. This improves the accuracy of the text conversion by removing background sounds.

[0036] The conversion unit can automatically adjust the speed of text conversion according to the user's speaking speed. The conversion unit automatically adjusts the speed of text conversion according to the user's speaking speed, for example, using a generation AI. For example, the conversion unit detects the user's speaking speed in real time and automatically adjusts the speed of text conversion. Furthermore, if the user speaks quickly, the conversion unit can also have the generation AI increase the processing speed of text conversion to maintain real-time performance. Furthermore, if the user speaks slowly, the conversion unit can also have the generation AI increase the processing time to improve the accuracy of text conversion. In this way, by adjusting the speed of text conversion according to the speaking speed, it is possible to improve accuracy while maintaining real-time performance.

[0037] The conversion unit can understand the context of the utterance and appropriately convert synonyms and similar words. The conversion unit, for example, uses a generation AI to understand the context of the utterance and appropriately convert synonyms and similar words. For example, the conversion unit uses a generation AI to analyze the context of the utterance and appropriately convert synonyms and similar words into text. The conversion unit can also use a generation AI to select appropriate synonyms based on the content of the utterance and reflect them in the text. The conversion unit can also use a generation AI to understand the context of the utterance and appropriately convert synonyms to generate natural-looking text. This makes it possible to generate natural-looking text by understanding the context and appropriately converting synonyms and similar words.

[0038] The conversion unit can automatically detect the language of speech and perform text conversion compatible with multiple languages. The conversion unit can automatically detect the language of speech using, for example, a generation AI and perform text conversion compatible with multiple languages. For example, the conversion unit can have the generation AI automatically detect the language of speech and perform text conversion in the appropriate language. The conversion unit can also have the generation AI simultaneously detect speech in multiple languages ​​and perform text conversion compatible with each language. The conversion unit can also have the generation AI automatically switch the language of speech and perform text conversion based on the user's language setting. In this way, automatic detection of the language of speech and support for multiple languages ​​facilitates communication between users of different languages.

[0039] The conversion unit can appropriately convert technical terms and slang based on the content of the utterance. The conversion unit, for example, uses a generation AI to appropriately convert technical terms and slang based on the content of the utterance. For example, the conversion unit uses a generation AI to analyze the content of the utterance and appropriately convert technical terms into text. The conversion unit can also use a generation AI to understand the context of the utterance and appropriately convert slang to generate natural-looking text. The conversion unit can also use a generation AI to appropriately convert technical terms and slang based on the content of the user's utterance. This makes it possible to generate more natural-looking text by appropriately converting technical terms and slang.

[0040] The conversion unit can store the speech audio data so that it can be reanalyzed later. The conversion unit can store the speech audio data using, for example, a generation AI so that it can be reanalyzed later. For example, the conversion unit can cause the generation AI to store the speech audio data so that it can be reanalyzed later. The conversion unit can also cause the generation AI to store the speech audio data in the cloud so that it can be reanalyzed as needed. The conversion unit can also cause the generation AI to store the speech audio data locally so that it can be reanalyzed later. By storing the speech audio data, it becomes possible to reanalyze it later, thereby improving the accuracy of the analysis.

[0041] The analysis unit can analyze the context of the text and accurately understand the intention of the utterance. The analysis unit, for example, uses a generation AI to analyze the context of the text and accurately understand the intention of the utterance. For example, the analysis unit allows the generation AI to analyze the context of the text and accurately understand the intention of the utterance. The analysis unit can also allow the generation AI to accurately analyze the intention by taking into account background information of the utterance. The analysis unit can also allow the generation AI to understand the context of the text and accurately analyze the intention of the utterance. In this way, by analyzing the context of the text, the intention of the utterance can be accurately understood and appropriate advice can be provided.

[0042] The analysis unit can extract keywords from the text and prioritize the analysis results based on their importance. The analysis unit, for example, uses a generation AI to extract keywords from the text and prioritize the analysis results based on their importance. For example, the analysis unit uses the generation AI to extract keywords from the text and prioritize the analysis results based on their importance. The analysis unit can also have the generation AI analyze the content of an utterance, extract important keywords, and prioritize them. The analysis unit can also have the generation AI analyze keywords from the text and prioritize the analysis results based on their importance. In this way, by extracting keywords and prioritizing the analysis results based on their importance, important information can be provided preferentially.

[0043] The analysis unit can analyze the grammatical structure of the text and make corrections to avoid misunderstandings. For example, the analysis unit uses a generation AI to analyze the grammatical structure of the text and make corrections to avoid misunderstandings. For example, the analysis unit allows the generation AI to analyze the grammatical structure of the text and make corrections to avoid misunderstandings. The analysis unit can also detect grammatical errors in the utterances of the generation AI and make appropriate corrections. The analysis unit can also allow the generation AI to understand the grammatical structure of the text and make corrections to avoid misunderstandings. In this way, by analyzing the grammatical structure and making corrections to avoid misunderstandings, more accurate analysis results can be provided.

[0044] The analysis unit can complement the analysis results by referencing related past data based on the content of the text. The analysis unit, for example, uses a generation AI to complement the analysis results by referencing related past data based on the content of the text. For example, the analysis unit has the generation AI analyze the content of the text and complement the analysis results by referencing related past data. The analysis unit can also complement the analysis results by referencing past data based on the content of the utterance by the generation AI. The analysis unit can also complement the analysis results by referencing related past data by the generation AI understanding the content of the text. In this way, by referencing past data, the analysis results can be complemented and more accurate information can be provided.

[0045] The analysis unit can apply different analysis algorithms depending on the text category. For example, the analysis unit uses a generation AI to apply different analysis algorithms depending on the text category. For example, the analysis unit has the generation AI analyze the text category and apply an appropriate analysis algorithm. The analysis unit can also apply different analysis algorithms to each category based on the content of the utterance by the generation AI. The analysis unit can also have the generation AI understand the text category and apply the optimal analysis algorithm. This makes it possible to provide more appropriate analysis results by applying an analysis algorithm depending on the category.

[0046] The analysis unit can adjust the level of detail of the analysis according to the length of the text. For example, the analysis unit uses a generation AI to adjust the level of detail of the analysis according to the length of the text. For example, the analysis unit has the generation AI analyze the length of the text and perform the analysis at an appropriate level of detail. The analysis unit can also perform the analysis at a level of detail according to the length of the text based on the content of the utterance made by the generation AI. The analysis unit can also have the generation AI understand the length of the text and perform the analysis at an optimal level of detail. In this way, by adjusting the level of detail of the analysis according to the length of the text, it is possible to provide appropriate analysis results.

[0047] The providing unit can adjust the display order based on the importance of the advice. The providing unit adjusts the display order based on the importance of the advice, for example, using a generation AI. For example, the providing unit causes the generation AI to analyze the importance of the advice and prioritize displaying important advice. The providing unit can also display advice with a higher importance level at the top based on the content of the utterance made by the generation AI. The providing unit can also cause the generation AI to understand the importance of the advice and set an optimal display order. In this way, by adjusting the display order based on the importance of the advice, important information can be prioritized.

[0048] The providing unit can customize the content of the advice based on the user's past behavioral history. The providing unit, for example, uses a generation AI to customize the content of the advice based on the user's past behavioral history. For example, the providing unit causes the generation AI to analyze the user's past behavioral history and provide customized advice. The providing unit can also cause the generation AI to provide advice that takes into account the user's past behavioral history based on the content of the utterance. The providing unit can also cause the generation AI to understand the user's behavioral history and customize optimal advice. In this way, more appropriate advice can be provided by customizing the advice based on the user's past behavioral history.

[0049] The providing unit can evaluate the effectiveness of the advice and reflect it in the next advice. The providing unit, for example, uses a generation AI to evaluate the effectiveness of the advice and reflect it in the next advice. For example, the providing unit has the generation AI evaluate the effectiveness of the advice and reflect it in the next advice. The providing unit can also have the generation AI analyze the user's feedback, evaluate the effectiveness of the advice, and reflect it in the next advice. The providing unit can also have the generation AI evaluate the effectiveness of the advice in real time and reflect it in the next advice. In this way, by evaluating the effectiveness of the advice and reflecting it in the next advice, more effective advice can be provided.

[0050] The providing unit can update the content of the advice in real time based on the user's current situation. The providing unit updates the content of the advice in real time based on the user's current situation, for example, using a generation AI. For example, the providing unit causes the generation AI to analyze the user's current situation and update the advice in real time. The providing unit can also provide advice that takes the current situation into consideration based on the content of the utterance made by the generation AI. The providing unit can also cause the generation AI to grasp the user's situation in real time and provide optimal advice. In this way, more appropriate advice can be provided by updating the advice in real time based on the current situation.

[0051] The providing unit can provide advice in multiple formats, such as text, audio, and video. The providing unit can use, for example, a generation AI to provide advice in multiple formats, such as text, audio, and video. For example, the providing unit can have the generation AI provide the advice in text format. The providing unit can also have the generation AI provide the advice in audio format. The providing unit can also have the generation AI provide the advice in video format. In this way, by providing advice in multiple formats, it becomes possible to provide information according to the user's preferences.

[0052] The providing unit can improve the content of the advice based on the user's feedback. The providing unit, for example, uses a generation AI to improve the content of the advice based on the user's feedback. For example, the providing unit causes the generation AI to analyze the user's feedback and improve the content of the advice. The providing unit can also provide advice that reflects the feedback based on the content of the utterance made by the generation AI. The providing unit can also cause the generation AI to reflect the user's feedback in real time and improve the content of the advice. In this way, more effective advice can be provided by improving the content of the advice based on the user's feedback.

[0053] The chat unit can automatically summarize the content of the chat and highlight important information. For example, the chat unit can use a generation AI to automatically summarize the content of the chat and highlight important information. For example, the chat unit can use a generation AI to automatically summarize the content of the chat and highlight important information. The chat unit can also use a generation AI to analyze the content of the utterance and summarize and display important information. The chat unit can also use a generation AI to understand the content of the chat and highlight important information. In this way, by summarizing the content of the chat and highlighting important information, efficient information comprehension is possible.

[0054] The chat unit can save the chat history so that it can be referenced later. The chat unit can, for example, use the generation AI to save the chat history so that it can be referenced later. For example, the chat unit can have the generation AI save the chat history so that it can be referenced later. The chat unit can also have the generation AI save the chat history in the cloud so that it can be referenced as needed. The chat unit can also have the generation AI save the chat history locally so that it can be referenced later. In this way, by saving the chat history, it becomes possible to reference it later, making it easier to reuse information.

[0055] The chat unit can adjust the priority of comments based on the frequency of comments made by chat participants. The chat unit adjusts the priority of comments based on the frequency of comments made by chat participants, for example, using a generation AI. For example, the chat unit adjusts the priority of comments by analyzing the frequency of comments made by chat participants with the generation AI. The chat unit can also prioritize displaying comments made by participants who speak frequently based on the content of their utterances with the generation AI. The chat unit can also set optimal priorities by using the generation AI to understand the frequency of comments made by chat participants. In this way, important comments can be prioritized by adjusting the priority of comments based on the frequency of comments.

[0056] The chat unit can automatically translate the chat content and support communication between users of different languages. The chat unit can, for example, use a generation AI to automatically translate the chat content and support communication between users of different languages. For example, the chat unit can automatically translate the chat content using a generation AI to support communication between users of different languages. The chat unit can also analyze the content of utterances using a generation AI, translate it into an appropriate language, and display it. The chat unit can also use a generation AI to understand the chat content and facilitate communication between users of different languages. In this way, automatic translation of the chat content facilitates communication between users of different languages.

[0057] The chat unit can analyze the content of the chat and automatically provide related information. For example, the chat unit can use a generation AI to analyze the content of the chat and automatically provide related information. For example, the chat unit can use a generation AI to analyze the content of the chat and automatically provide related information. The chat unit can also display related information based on the content of the utterances made by the generation AI. The chat unit can also use a generation AI to understand the content of the chat and provide related information. This makes it possible to provide information efficiently by analyzing the content of the chat and providing related information.

[0058] The chat unit can display profile information of chat participants to facilitate communication. The chat unit can, for example, use a generation AI to display profile information of chat participants to facilitate communication. For example, the chat unit can use a generation AI to display profile information of chat participants to facilitate communication. The chat unit can also display profile information of participants based on the content of utterances made by the generation AI. The chat unit can also use a generation AI to understand the profile information of chat participants and provide an optimal display method. In this way, displaying profile information of chat participants facilitates communication.

[0059] The menu creation unit can evaluate the effectiveness of the training menu and reflect it in the next menu. The menu creation unit, for example, uses a generation AI to evaluate the effectiveness of the training menu and reflect it in the next menu. For example, the menu creation unit uses a generation AI to evaluate the effectiveness of the training menu and reflect it in the next menu. The menu creation unit can also have the generation AI analyze user feedback, evaluate the effectiveness of the training menu, and reflect it in the next menu. The menu creation unit can also have the generation AI evaluate the effectiveness of the training menu in real time and reflect it in the next menu. In this way, more effective training is possible by evaluating the effectiveness of the training menu and reflecting it in the next menu.

[0060] The menu creation unit can improve the content of the training menu based on user feedback. The menu creation unit improves the content of the training menu based on user feedback, for example, using a generation AI. For example, the menu creation unit causes the generation AI to analyze the user's feedback and improve the content of the training menu. The menu creation unit can also provide a training menu that reflects the feedback based on the content of the utterances made by the generation AI. The menu creation unit can also cause the generation AI to reflect the user's feedback in real time and improve the content of the training menu. This enables more effective training by improving the content of the training menu based on user feedback.

[0061] The menu creation unit can customize the content of the training menu based on the user's current fitness level. The menu creation unit customizes the content of the training menu based on the user's current fitness level, for example, using a generation AI. For example, the menu creation unit uses the generation AI to analyze the user's current fitness level and provide a customized training menu. The menu creation unit can also provide a training menu that takes the current fitness level into consideration based on the content of the utterances made by the generation AI. The menu creation unit can also use the generation AI to grasp the user's fitness level in real time and provide an optimal training menu. This allows for more effective training by customizing the training menu based on the user's current fitness level.

[0062] The menu creation unit can share the contents of the training menu with other users and conduct joint training. The menu creation unit can, for example, use a generation AI to share the contents of the training menu with other users and conduct joint training. For example, the menu creation unit can have the generation AI share the training menu with other users and conduct joint training. The menu creation unit can also share the training menu based on the content of the utterance by the generation AI and conduct joint training. The menu creation unit can also have the generation AI share the training menu in real time and conduct joint training. This allows joint training to be conducted by sharing the training menu with other users.

[0063] The menu creation unit can adjust the content of the training menu based on the user's schedule. The menu creation unit adjusts the content of the training menu based on the user's schedule, for example, using a generation AI. For example, the menu creation unit uses the generation AI to analyze the user's schedule and provide an optimal training menu. The menu creation unit can also provide a training menu that takes the schedule into consideration based on the content of the utterances made by the generation AI. The menu creation unit can also use the generation AI to grasp the user's schedule in real time and provide an optimal training menu. This allows for more effective training by adjusting the training menu based on the user's schedule.

[0064] The menu creation unit can improve the content of the training menu based on user feedback. The menu creation unit improves the content of the training menu based on user feedback, for example, using a generation AI. For example, the menu creation unit causes the generation AI to analyze the user's feedback and improve the content of the training menu. The menu creation unit can also provide a training menu that reflects the feedback based on the content of the utterances made by the generation AI. The menu creation unit can also cause the generation AI to reflect the user's feedback in real time and improve the content of the training menu. This enables more effective training by improving the content of the training menu based on user feedback.

[0065] The training analysis unit can analyze the training data in real time and provide immediate feedback. The training analysis unit can, for example, use a generation AI to analyze the training data in real time and provide immediate feedback. For example, the training analysis unit can have the generation AI analyze the training data in real time and provide immediate feedback. The training analysis unit can also have the generation AI analyze the training data in real time based on the content of utterances and provide immediate feedback. The training analysis unit can also have the generation AI grasp the training data in real time and provide optimal feedback. In this way, by analyzing the training data in real time and providing immediate feedback, the effectiveness of training can be improved.

[0066] The training analysis unit can analyze trends in the training data and propose a long-term training plan. The training analysis unit can, for example, use a generation AI to analyze trends in the training data and propose a long-term training plan. For example, the training analysis unit can have the generation AI analyze trends in the training data and propose a long-term training plan. The training analysis unit can also have the generation AI analyze trends in the training data based on the content of utterances and propose a long-term training plan. The training analysis unit can also have the generation AI grasp trends in the training data in real time and propose an optimal long-term training plan. In this way, by analyzing trends in the training data and proposing a long-term training plan, the effectiveness of training can be improved.

[0067] The training analysis unit can collect training data from different devices and perform a comprehensive analysis. The training analysis unit can, for example, use a generation AI to collect training data from different devices and perform a comprehensive analysis. For example, the training analysis unit can have the generation AI collect training data from different devices and perform a comprehensive analysis. The training analysis unit can also analyze the training data collected from different devices based on the content of utterances made by the generation AI. The training analysis unit can also have the generation AI grasp the training data from different devices in real time and perform a comprehensive analysis. In this way, by collecting training data from different devices and performing a comprehensive analysis, more accurate analysis results can be provided.

[0068] The training analysis unit can compare the training data with other users to enhance a sense of competition. For example, the training analysis unit uses a generation AI to compare the training data with other users to enhance a sense of competition. For example, the training analysis unit can have the generation AI compare the training data with other users to enhance a sense of competition. The training analysis unit can also provide training data compared with other users based on the content of utterances made by the generation AI. The training analysis unit can also have the generation AI compare the training data in real time to enhance a sense of competition. In this way, by comparing the training data with other users, a sense of competition can be enhanced and motivation for training can be improved.

[0069] The training analysis unit can visualize the training data and provide it in a form that is easy for the user to understand. For example, the training analysis unit uses a generation AI to visualize the training data and provide it in a form that is easy for the user to understand. For example, the training analysis unit can use the generation AI to visualize the training data and provide it in a form that is easy for the user to understand. The training analysis unit can also provide visualized training data based on the content of the utterance made by the generation AI. The training analysis unit can also use the generation AI to visualize the training data in real time and provide it in a form that is easy for the user to understand. In this way, by visualizing the training data, information can be provided in a form that is easy for the user to understand.

[0070] The training analysis unit can collect training data from different devices and perform a comprehensive analysis. The training analysis unit can, for example, use a generation AI to collect training data from different devices and perform a comprehensive analysis. For example, the training analysis unit can have the generation AI collect training data from different devices and perform a comprehensive analysis. The training analysis unit can also analyze the training data collected from different devices based on the content of utterances made by the generation AI. The training analysis unit can also have the generation AI grasp the training data from different devices in real time and perform a comprehensive analysis. In this way, by collecting training data from different devices and performing a comprehensive analysis, more accurate analysis results can be provided.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] When analyzing the content of a user's speech, the analysis unit can refer to the user's past speech history. For example, more accurate analysis can be performed based on previous statements or questions made by the user. The analysis unit can also learn the user's speech patterns and perform analysis optimized for each individual user. Furthermore, the analysis unit can provide consistent analysis results by storing the user's speech history in the cloud and making it accessible from other devices. This makes it possible to provide more accurate analysis results by utilizing the user's past speech history.

[0073] The menu creation unit can monitor the user's fitness level in real time and dynamically adjust the training menu. For example, it can monitor the user's heart rate and exercise intensity to provide an appropriate training menu. It can also analyze the user's fatigue level and reduce the training menu as necessary. Furthermore, if the user's fitness level improves, the generation AI can automatically update the training menu and provide a more challenging menu. This allows for effective training by dynamically adjusting the training menu according to the user's fitness level.

[0074] The chat module can analyze the content of a user's speech and automatically provide related topics and information. For example, if a user asks a question about a specific sport, the generation AI can provide the latest news and articles related to that sport. If a user talks about training methods, the generation AI can provide relevant training videos and guides. Furthermore, if a user talks about a specific player, the generation AI can provide that player's statistical data and performance analysis. This allows for a richer chat experience by providing relevant information based on the user's speech.

[0075] The conversion unit analyzes the content of the user's utterance and understands the intention of the utterance, allowing for more natural text conversion. For example, if the user speaks in the form of a question, the generation AI can understand that intention and convert it into text as an appropriate question. Also, if the user speaks in the form of an imperative, the generation AI can understand that intention and convert it into text as an appropriate imperative. Furthermore, if the user speaks with emotion, the generation AI can convert the text to reflect that emotion. This understanding of the intention of the utterance enables more natural and accurate text conversion.

[0076] When analyzing the content of a user's speech, the analysis unit can simultaneously analyze text in different languages. For example, if a user speaks in multiple languages, the generation AI can analyze each language and provide appropriate advice. The analysis unit can also automatically translate the content of the speech based on the user's language settings and provide analysis results. Furthermore, the analysis unit can integrate text in different languages ​​and provide comprehensive analysis results. This makes it possible to provide services that cater to global users by analyzing content of speech in different languages.

[0077] The menu creation unit can analyze the user's training history and propose an optimal training menu based on past training data. For example, it can analyze the effects of the user's past training and propose an effective training menu. The generation AI can also customize the training menu based on the user's training history and provide a menu optimized for each individual user. Furthermore, by storing the user's training history in the cloud and making it accessible from other devices, a consistent training menu can be provided. This makes it possible to provide a more effective training menu by utilizing the user's training history.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The conversion unit converts the user's speech into text in real time. For example, the conversion unit uses a generation AI to convert the user's speech into text in real time. The conversion unit can also convert the speech into text when the generation AI receives a prompt such as "Please convert this speech into text." Furthermore, the conversion unit can enable the generation AI to understand the context of the speech and perform appropriate text conversion. Step 2: The analysis unit analyzes the text converted by the conversion unit. For example, the analysis unit uses the generation AI to analyze the content of the text. The analysis unit can also analyze the content of the text when the generation AI receives a prompt such as "Please analyze the content of this text." Furthermore, the analysis unit can enable the generation AI to understand the context of the text and perform appropriate analysis. Step 3: The provision unit provides advice based on the analysis results obtained by the analysis unit. For example, the provision unit uses the generation AI to provide training or tactical advice based on the analysis results. The provision unit can also provide advice when the generation AI receives a prompt such as "Please provide advice based on this analysis result." Furthermore, the provision unit can enable the generation AI to understand the analysis results and provide appropriate advice. Step 4: The chat section has a video chat with friends or team members. For example, the chat section uses the generation AI to improve the quality of the video chat. The chat section can also improve the quality of the video chat by receiving a prompt such as "Please improve the quality of this video chat." Furthermore, the chat section can use the generation AI to understand the context of the video chat and make appropriate quality improvements.

[0080] (Example 2) A mobile app according to an embodiment of the present invention is a system that allows sports enthusiasts and athletes to exchange sports-related information and train through real-time video chats with friends and team members in various regions. This system utilizes a generative AI to convert the user's speech into text in real time and provide training and tactical advice as needed. For example, a user launches the app and starts a video chat with a friend or team member. The generative AI then converts the user's speech into text in real time. For example, if the user says, "What tactics should we use in the next game?", the text is displayed. The generative AI then analyzes the converted speech and provides appropriate training and tactical advice. For example, advice such as, "It would be a good idea to adopt a zone defense to strengthen your defense in the next game" is displayed. Furthermore, users can create training menus within the app and share them with friends and team members. The generative AI analyzes the user's training and suggests effective training methods. For example, advice such as, "Stretching before running can reduce the risk of injury" is provided. This allows the mobile app to enable sports enthusiasts and athletes to exchange information and train in real time with friends and team members from different regions, enabling them to practice effective training and tactics with the support of generative AI.This allows the mobile app to enable sports enthusiasts and athletes to exchange information and train in real time with friends and team members from different regions, enabling them to practice effective training and tactics with the support of generative AI.

[0081] A mobile app according to an embodiment includes a conversion unit, an analysis unit, a provision unit, and a chat unit. The conversion unit converts a user's speech into text in real time. For example, the conversion unit converts a user's speech into text in real time using a generation AI. The conversion unit can also convert the speech into text when the generation AI receives a prompt such as, "Please convert this speech into text." The conversion unit can also enable the generation AI to understand the context of the speech and perform appropriate text conversion. The analysis unit analyzes the text converted by the conversion unit. For example, the analysis unit can analyze the content of the text using a generation AI. The analysis unit can also analyze the content of the text when the generation AI receives a prompt such as, "Please analyze the content of this text." The analysis unit can also enable the generation AI to understand the context of the text and perform appropriate analysis. The provision unit provides advice based on the analysis results obtained by the analysis unit. For example, the provision unit can provide training or tactical advice based on the analysis results using the generation AI. The provision unit can also provide advice when the generation AI receives a prompt such as, "Please provide advice based on the analysis results." The providing unit also enables the generation AI to understand the analysis results and provide appropriate advice. The chat unit performs video chats with friends and team members. For example, the chat unit uses the generation AI to improve the quality of the video chat. The chat unit also enables the generation AI to receive a prompt such as "Please improve the quality of this video chat" and improve the quality of the video chat. The chat unit also enables the generation AI to understand the context of the video chat and make appropriate quality improvements. As a result, the mobile app according to the embodiment converts the user's speech into text in real time and provides advice based on the analysis results, thereby enabling effective information exchange and training related to sports.

[0082] The mobile app includes a menu creation unit that creates a training menu and shares it with friends and team members. The menu creation unit creates a training menu using a generation AI. The menu creation unit can also create a training menu when the generation AI receives a prompt such as "Please create this training menu." The menu creation unit can also have the generation AI analyze the contents of the training menu and suggest an appropriate menu. This allows for effective training by creating and sharing a training menu.

[0083] The mobile app includes a training analysis unit that analyzes training content and suggests effective training methods. The training analysis unit analyzes the training content and suggests effective training methods. For example, the training analysis unit analyzes the training content using a generation AI. The training analysis unit can also analyze the training content when the generation AI receives a prompt such as "Please analyze this training content." The training analysis unit can also allow the generation AI to understand the training content and suggest an appropriate training method. In this way, the effectiveness of training can be improved by analyzing the training content and suggesting an effective training method.

[0084] The conversion unit can convert the user's speech into text in real time. For example, the conversion unit can convert the user's speech into text using a generation AI. For example, the conversion unit can convert the speech content into text when the generation AI receives a prompt such as "Please convert this speech into text." The conversion unit can also enable the generation AI to understand the context of the speech and perform appropriate text conversion. This enables rapid information exchange by converting the user's speech into text in real time.

[0085] The analysis unit can analyze the content of the utterance converted into text. The analysis unit analyzes the content of the text, for example, using the generation AI. For example, the analysis unit can analyze the content of the text when the generation AI receives a prompt such as "Please analyze the content of this text." The analysis unit can also enable the generation AI to understand the context of the text and perform an appropriate analysis. This makes it possible to provide appropriate advice by analyzing the content of the utterance converted into text.

[0086] The providing unit can provide training or tactical advice based on the analysis results. For example, the providing unit can provide training or tactical advice based on the analysis results using a generation AI. For example, the providing unit can provide advice when the generation AI receives a prompt such as "Please provide advice based on this analysis result." The providing unit can also enable the generation AI to understand the analysis results and provide appropriate advice. This makes it possible to improve the user's performance by providing training or tactical advice based on the analysis results.

[0087] The conversion unit can estimate the user's emotions and adjust the accuracy of the speech-to-text conversion based on the estimated user's emotions. The conversion unit, for example, uses a generation AI to estimate the user's emotions and adjust the accuracy of the speech-to-text conversion based on the estimated user's emotions. For example, if the user is nervous, the conversion unit causes the generation AI to increase the accuracy of the speech-to-text conversion and reduce erroneous conversions. Furthermore, if the user is relaxed, the conversion unit can also cause the generation AI to emphasize the natural flow of speech and adjust the speed of text conversion. Furthermore, if the user is excited, the conversion unit can cause the generation AI to accurately reflect the emphasized parts of the speech in the text. This allows for more accurate text conversion by adjusting the text conversion accuracy according to the user's emotions.

[0088] The conversion unit can analyze background sounds of the speech and remove noise to improve the accuracy of the text conversion. For example, the conversion unit uses a generation AI to analyze background sounds of the speech and remove noise to improve the accuracy of the text conversion. For example, the conversion unit can improve the accuracy of the text conversion of the speech by using a generation AI to detect ambient noise and perform noise filtering. The conversion unit can also remove noise in a specific frequency band and convert clear speech into text. The conversion unit can also use a generation AI to identify background music and environmental sounds and remove them to convert the speech into text. This improves the accuracy of the text conversion by removing background sounds.

[0089] The conversion unit can automatically adjust the speed of text conversion according to the user's speaking speed. The conversion unit automatically adjusts the speed of text conversion according to the user's speaking speed, for example, using a generation AI. For example, the conversion unit detects the user's speaking speed in real time and automatically adjusts the speed of text conversion. Furthermore, if the user speaks quickly, the conversion unit can also have the generation AI increase the processing speed of text conversion to maintain real-time performance. Furthermore, if the user speaks slowly, the conversion unit can also have the generation AI increase the processing time to improve the accuracy of text conversion. In this way, by adjusting the speed of text conversion according to the speaking speed, it is possible to improve accuracy while maintaining real-time performance.

[0090] The conversion unit can understand the context of the utterance and appropriately convert synonyms and similar words. The conversion unit, for example, uses a generation AI to understand the context of the utterance and appropriately convert synonyms and similar words. For example, the conversion unit uses a generation AI to analyze the context of the utterance and appropriately convert synonyms and similar words into text. The conversion unit can also use a generation AI to select appropriate synonyms based on the content of the utterance and reflect them in the text. The conversion unit can also use a generation AI to understand the context of the utterance and appropriately convert synonyms to generate natural-looking text. This makes it possible to generate natural-looking text by understanding the context and appropriately converting synonyms and similar words.

[0091] The conversion unit can estimate the user's emotions and adjust the expression method for text conversion based on the estimated user emotions. The conversion unit, for example, uses a generation AI to estimate the user's emotions and adjust the expression method for text conversion based on the estimated user emotions. For example, if the user is nervous, the conversion unit causes the generation AI to select a simple and clear expression method. Also, if the user is relaxed, the conversion unit can cause the generation AI to select a natural and fluent expression method. Also, if the user is excited, the conversion unit can cause the generation AI to select an emphasized expression method. This makes it possible to adjust the expression method for text conversion according to the user's emotions, thereby enabling more appropriate expressions.

[0092] The conversion unit can automatically detect the language of speech and perform text conversion compatible with multiple languages. The conversion unit can automatically detect the language of speech using, for example, a generation AI and perform text conversion compatible with multiple languages. For example, the conversion unit can have the generation AI automatically detect the language of speech and perform text conversion in the appropriate language. The conversion unit can also have the generation AI simultaneously detect speech in multiple languages ​​and perform text conversion compatible with each language. The conversion unit can also have the generation AI automatically switch the language of speech and perform text conversion based on the user's language setting. In this way, automatic detection of the language of speech and support for multiple languages ​​facilitates communication between users of different languages.

[0093] The conversion unit can appropriately convert technical terms and slang based on the content of the utterance. The conversion unit, for example, uses a generation AI to appropriately convert technical terms and slang based on the content of the utterance. For example, the conversion unit uses a generation AI to analyze the content of the utterance and appropriately convert technical terms into text. The conversion unit can also use a generation AI to understand the context of the utterance and appropriately convert slang to generate natural-looking text. The conversion unit can also use a generation AI to appropriately convert technical terms and slang based on the content of the user's utterance. This makes it possible to generate more natural-looking text by appropriately converting technical terms and slang.

[0094] The conversion unit can store the speech audio data so that it can be reanalyzed later. The conversion unit can store the speech audio data using, for example, a generation AI so that it can be reanalyzed later. For example, the conversion unit can cause the generation AI to store the speech audio data so that it can be reanalyzed later. The conversion unit can also cause the generation AI to store the speech audio data in the cloud so that it can be reanalyzed as needed. The conversion unit can also cause the generation AI to store the speech audio data locally so that it can be reanalyzed later. By storing the speech audio data, it becomes possible to reanalyze it later, thereby improving the accuracy of the analysis.

[0095] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis results based on the estimated user emotions. The analysis unit can, for example, use a generation AI to estimate the user's emotions and adjust the accuracy of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can have the generation AI increase the accuracy of the analysis results to avoid misunderstandings. Furthermore, if the user is relaxed, the analysis unit can have the generation AI provide natural analysis results. Furthermore, if the user is excited, the analysis unit can have the generation AI provide emphasized analysis results. In this way, by adjusting the accuracy of the analysis results according to the user's emotions, more accurate analysis results can be provided.

[0096] The analysis unit can analyze the context of the text and accurately understand the intention of the utterance. The analysis unit, for example, uses a generation AI to analyze the context of the text and accurately understand the intention of the utterance. For example, the analysis unit allows the generation AI to analyze the context of the text and accurately understand the intention of the utterance. The analysis unit can also allow the generation AI to accurately analyze the intention by taking into account background information of the utterance. The analysis unit can also allow the generation AI to understand the context of the text and accurately analyze the intention of the utterance. In this way, by analyzing the context of the text, the intention of the utterance can be accurately understood and appropriate advice can be provided.

[0097] The analysis unit can extract keywords from the text and prioritize the analysis results based on their importance. The analysis unit, for example, uses a generation AI to extract keywords from the text and prioritize the analysis results based on their importance. For example, the analysis unit uses the generation AI to extract keywords from the text and prioritize the analysis results based on their importance. The analysis unit can also have the generation AI analyze the content of an utterance, extract important keywords, and prioritize them. The analysis unit can also have the generation AI analyze keywords from the text and prioritize the analysis results based on their importance. In this way, by extracting keywords and prioritizing the analysis results based on their importance, important information can be provided preferentially.

[0098] The analysis unit can analyze the grammatical structure of the text and make corrections to avoid misunderstandings. For example, the analysis unit uses a generation AI to analyze the grammatical structure of the text and make corrections to avoid misunderstandings. For example, the analysis unit allows the generation AI to analyze the grammatical structure of the text and make corrections to avoid misunderstandings. The analysis unit can also detect grammatical errors in the utterances of the generation AI and make appropriate corrections. The analysis unit can also allow the generation AI to understand the grammatical structure of the text and make corrections to avoid misunderstandings. In this way, by analyzing the grammatical structure and making corrections to avoid misunderstandings, more accurate analysis results can be provided.

[0099] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can, for example, use a generation AI to estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the generation AI can provide a simple, highly visible display method. Also, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Also, if the user is excited, the generation AI can provide an emphasized display method. This allows for a more appropriate display by adjusting the display method of the analysis results according to the user's emotions.

[0100] The analysis unit can complement the analysis results by referencing related past data based on the content of the text. The analysis unit, for example, uses a generation AI to complement the analysis results by referencing related past data based on the content of the text. For example, the analysis unit has the generation AI analyze the content of the text and complement the analysis results by referencing related past data. The analysis unit can also complement the analysis results by referencing past data based on the content of the utterance by the generation AI. The analysis unit can also complement the analysis results by referencing related past data by the generation AI understanding the content of the text. In this way, by referencing past data, the analysis results can be complemented and more accurate information can be provided.

[0101] The analysis unit can apply different analysis algorithms depending on the text category. For example, the analysis unit uses a generation AI to apply different analysis algorithms depending on the text category. For example, the analysis unit has the generation AI analyze the text category and apply an appropriate analysis algorithm. The analysis unit can also apply different analysis algorithms to each category based on the content of the utterance by the generation AI. The analysis unit can also have the generation AI understand the text category and apply the optimal analysis algorithm. This makes it possible to provide more appropriate analysis results by applying an analysis algorithm depending on the category.

[0102] The analysis unit can adjust the level of detail of the analysis according to the length of the text. For example, the analysis unit uses a generation AI to adjust the level of detail of the analysis according to the length of the text. For example, the analysis unit has the generation AI analyze the length of the text and perform the analysis at an appropriate level of detail. The analysis unit can also perform the analysis at a level of detail according to the length of the text based on the content of the utterance made by the generation AI. The analysis unit can also have the generation AI understand the length of the text and perform the analysis at an optimal level of detail. In this way, by adjusting the level of detail of the analysis according to the length of the text, it is possible to provide appropriate analysis results.

[0103] The providing unit can estimate the user's emotions and adjust the way in which advice is expressed based on the estimated user's emotions. The providing unit can, for example, use a generation AI to estimate the user's emotions and adjust the way in which advice is expressed based on the estimated user's emotions. For example, if the user is nervous, the providing unit can cause the generation AI to provide simple and clear advice. Also, if the user is relaxed, the providing unit can cause the generation AI to provide detailed advice. Also, if the user is excited, the providing unit can cause the generation AI to provide emphasized advice. In this way, more appropriate advice can be provided by adjusting the way in which advice is expressed according to the user's emotions.

[0104] The providing unit can adjust the display order based on the importance of the advice. The providing unit adjusts the display order based on the importance of the advice, for example, using a generation AI. For example, the providing unit causes the generation AI to analyze the importance of the advice and prioritize displaying important advice. The providing unit can also display advice with a higher importance level at the top based on the content of the utterance made by the generation AI. The providing unit can also cause the generation AI to understand the importance of the advice and set an optimal display order. In this way, by adjusting the display order based on the importance of the advice, important information can be prioritized.

[0105] The providing unit can customize the content of the advice based on the user's past behavioral history. The providing unit, for example, uses a generation AI to customize the content of the advice based on the user's past behavioral history. For example, the providing unit causes the generation AI to analyze the user's past behavioral history and provide customized advice. The providing unit can also cause the generation AI to provide advice that takes into account the user's past behavioral history based on the content of the utterance. The providing unit can also cause the generation AI to understand the user's behavioral history and customize optimal advice. In this way, more appropriate advice can be provided by customizing the advice based on the user's past behavioral history.

[0106] The providing unit can evaluate the effectiveness of the advice and reflect it in the next advice. The providing unit, for example, uses a generation AI to evaluate the effectiveness of the advice and reflect it in the next advice. For example, the providing unit has the generation AI evaluate the effectiveness of the advice and reflect it in the next advice. The providing unit can also have the generation AI analyze the user's feedback, evaluate the effectiveness of the advice, and reflect it in the next advice. The providing unit can also have the generation AI evaluate the effectiveness of the advice in real time and reflect it in the next advice. In this way, by evaluating the effectiveness of the advice and reflecting it in the next advice, more effective advice can be provided.

[0107] The providing unit can estimate the user's emotions and adjust the level of detail of the advice based on the estimated user's emotions. The providing unit can, for example, use a generation AI to estimate the user's emotions and adjust the level of detail of the advice based on the estimated user's emotions. For example, if the user is nervous, the providing unit can cause the generation AI to provide simple advice that focuses on the main points. Furthermore, if the user is relaxed, the providing unit can cause the generation AI to provide detailed advice. Furthermore, if the user is excited, the providing unit can cause the generation AI to provide emphasized advice. In this way, more appropriate advice can be provided by adjusting the level of detail of the advice according to the user's emotions.

[0108] The providing unit can update the content of the advice in real time based on the user's current situation. The providing unit updates the content of the advice in real time based on the user's current situation, for example, using a generation AI. For example, the providing unit causes the generation AI to analyze the user's current situation and update the advice in real time. The providing unit can also provide advice that takes the current situation into consideration based on the content of the utterance made by the generation AI. The providing unit can also cause the generation AI to grasp the user's situation in real time and provide optimal advice. In this way, more appropriate advice can be provided by updating the advice in real time based on the current situation.

[0109] The providing unit can provide advice in multiple formats, such as text, audio, and video. The providing unit can use, for example, a generation AI to provide advice in multiple formats, such as text, audio, and video. For example, the providing unit can have the generation AI provide the advice in text format. The providing unit can also have the generation AI provide the advice in audio format. The providing unit can also have the generation AI provide the advice in video format. In this way, by providing advice in multiple formats, it becomes possible to provide information according to the user's preferences.

[0110] The providing unit can improve the content of the advice based on the user's feedback. The providing unit, for example, uses a generation AI to improve the content of the advice based on the user's feedback. For example, the providing unit causes the generation AI to analyze the user's feedback and improve the content of the advice. The providing unit can also provide advice that reflects the feedback based on the content of the utterance made by the generation AI. The providing unit can also cause the generation AI to reflect the user's feedback in real time and improve the content of the advice. In this way, more effective advice can be provided by improving the content of the advice based on the user's feedback.

[0111] The chat unit can estimate the user's emotions and adjust the display method of the chat based on the estimated user's emotions. The chat unit can, for example, use a generation AI to estimate the user's emotions and adjust the display method of the chat based on the estimated user's emotions. For example, if the user is nervous, the generation AI can provide a display method that is simple and highly visible. Also, if the user is relaxed, the chat unit can provide a display method that includes detailed information. Also, if the user is excited, the generation AI can provide an emphasized display method. This allows for a more appropriate display by adjusting the display method of the chat according to the user's emotions.

[0112] The chat unit can automatically summarize the content of the chat and highlight important information. For example, the chat unit can use a generation AI to automatically summarize the content of the chat and highlight important information. For example, the chat unit can use a generation AI to automatically summarize the content of the chat and highlight important information. The chat unit can also use a generation AI to analyze the content of the utterance and summarize and display important information. The chat unit can also use a generation AI to understand the content of the chat and highlight important information. In this way, by summarizing the content of the chat and highlighting important information, efficient information comprehension is possible.

[0113] The chat unit can save the chat history so that it can be referenced later. The chat unit can, for example, use the generation AI to save the chat history so that it can be referenced later. For example, the chat unit can have the generation AI save the chat history so that it can be referenced later. The chat unit can also have the generation AI save the chat history in the cloud so that it can be referenced as needed. The chat unit can also have the generation AI save the chat history locally so that it can be referenced later. In this way, by saving the chat history, it becomes possible to reference it later, making it easier to reuse information.

[0114] The chat unit can adjust the priority of comments based on the frequency of comments made by chat participants. The chat unit adjusts the priority of comments based on the frequency of comments made by chat participants, for example, using a generation AI. For example, the chat unit adjusts the priority of comments by analyzing the frequency of comments made by chat participants with the generation AI. The chat unit can also prioritize displaying comments made by participants who speak frequently based on the content of their utterances with the generation AI. The chat unit can also set optimal priorities by using the generation AI to understand the frequency of comments made by chat participants. In this way, important comments can be prioritized by adjusting the priority of comments based on the frequency of comments.

[0115] The chat unit can estimate the user's emotions and adjust the chat notification method based on the estimated user's emotions. The chat unit can, for example, use a generation AI to estimate the user's emotions and adjust the chat notification method based on the estimated user's emotions. For example, if the user is nervous, the generation AI can provide a simple, highly visible notification method. Also, if the user is relaxed, the chat unit can provide a notification method that includes detailed information. Also, if the user is excited, the generation AI can provide an emphasized notification method. This allows for more appropriate notifications by adjusting the notification method according to the user's emotions.

[0116] The chat unit can automatically translate the chat content and support communication between users of different languages. The chat unit can, for example, use a generation AI to automatically translate the chat content and support communication between users of different languages. For example, the chat unit can automatically translate the chat content using a generation AI to support communication between users of different languages. The chat unit can also analyze the content of utterances using a generation AI, translate it into an appropriate language, and display it. The chat unit can also use a generation AI to understand the chat content and facilitate communication between users of different languages. In this way, automatic translation of the chat content facilitates communication between users of different languages.

[0117] The chat unit can analyze the content of the chat and automatically provide related information. For example, the chat unit can use a generation AI to analyze the content of the chat and automatically provide related information. For example, the chat unit can use a generation AI to analyze the content of the chat and automatically provide related information. The chat unit can also display related information based on the content of the utterances made by the generation AI. The chat unit can also use a generation AI to understand the content of the chat and provide related information. This makes it possible to provide information efficiently by analyzing the content of the chat and providing related information.

[0118] The chat unit can display profile information of chat participants to facilitate communication. The chat unit can, for example, use a generation AI to display profile information of chat participants to facilitate communication. For example, the chat unit can use a generation AI to display profile information of chat participants to facilitate communication. The chat unit can also display profile information of participants based on the content of utterances made by the generation AI. The chat unit can also use a generation AI to understand the profile information of chat participants and provide an optimal display method. In this way, displaying profile information of chat participants facilitates communication.

[0119] The menu creation unit can estimate the user's emotions and adjust the content of the training menu based on the estimated user's emotions. The menu creation unit can, for example, use a generation AI to estimate the user's emotions and adjust the content of the training menu based on the estimated user's emotions. For example, if the user is nervous, the menu creation unit can have the generation AI provide a simple training menu that focuses on the main points. Also, if the user is relaxed, the menu creation unit can have the generation AI provide a detailed training menu. Also, if the user is excited, the menu creation unit can have the generation AI provide an emphasized training menu. This allows for more effective training by adjusting the content of the training menu according to the user's emotions.

[0120] The menu creation unit can evaluate the effectiveness of the training menu and reflect it in the next menu. The menu creation unit, for example, uses a generation AI to evaluate the effectiveness of the training menu and reflect it in the next menu. For example, the menu creation unit uses a generation AI to evaluate the effectiveness of the training menu and reflect it in the next menu. The menu creation unit can also have the generation AI analyze user feedback, evaluate the effectiveness of the training menu, and reflect it in the next menu. The menu creation unit can also have the generation AI evaluate the effectiveness of the training menu in real time and reflect it in the next menu. In this way, more effective training is possible by evaluating the effectiveness of the training menu and reflecting it in the next menu.

[0121] The menu creation unit can improve the content of the training menu based on user feedback. The menu creation unit improves the content of the training menu based on user feedback, for example, using a generation AI. For example, the menu creation unit causes the generation AI to analyze the user's feedback and improve the content of the training menu. The menu creation unit can also provide a training menu that reflects the feedback based on the content of the utterances made by the generation AI. The menu creation unit can also cause the generation AI to reflect the user's feedback in real time and improve the content of the training menu. This enables more effective training by improving the content of the training menu based on user feedback.

[0122] The menu creation unit can customize the content of the training menu based on the user's current fitness level. The menu creation unit customizes the content of the training menu based on the user's current fitness level, for example, using a generation AI. For example, the menu creation unit uses the generation AI to analyze the user's current fitness level and provide a customized training menu. The menu creation unit can also provide a training menu that takes the current fitness level into consideration based on the content of the utterances made by the generation AI. The menu creation unit can also use the generation AI to grasp the user's fitness level in real time and provide an optimal training menu. This allows for more effective training by customizing the training menu based on the user's current fitness level.

[0123] The menu creation unit can estimate the user's emotions and adjust the display method of the training menu based on the estimated user's emotions. The menu creation unit, for example, uses a generation AI to estimate the user's emotions and adjust the display method of the training menu based on the estimated user's emotions. For example, if the user is nervous, the menu creation unit can cause the generation AI to provide a simple, highly visible display method. Furthermore, if the user is relaxed, the menu creation unit can also cause the generation AI to provide a display method that includes detailed information. Furthermore, if the user is excited, the menu creation unit can also cause the generation AI to provide an emphasized display method. This allows for a more appropriate display by adjusting the display method of the training menu according to the user's emotions.

[0124] The menu creation unit can share the contents of the training menu with other users and conduct joint training. The menu creation unit can, for example, use a generation AI to share the contents of the training menu with other users and conduct joint training. For example, the menu creation unit can have the generation AI share the training menu with other users and conduct joint training. The menu creation unit can also share the training menu based on the content of the utterance by the generation AI and conduct joint training. The menu creation unit can also have the generation AI share the training menu in real time and conduct joint training. This allows joint training to be conducted by sharing the training menu with other users.

[0125] The menu creation unit can adjust the content of the training menu based on the user's schedule. The menu creation unit adjusts the content of the training menu based on the user's schedule, for example, using a generation AI. For example, the menu creation unit uses the generation AI to analyze the user's schedule and provide an optimal training menu. The menu creation unit can also provide a training menu that takes the schedule into consideration based on the content of the utterances made by the generation AI. The menu creation unit can also use the generation AI to grasp the user's schedule in real time and provide an optimal training menu. This allows for more effective training by adjusting the training menu based on the user's schedule.

[0126] The menu creation unit can improve the content of the training menu based on user feedback. The menu creation unit improves the content of the training menu based on user feedback, for example, using a generation AI. For example, the menu creation unit causes the generation AI to analyze the user's feedback and improve the content of the training menu. The menu creation unit can also provide a training menu that reflects the feedback based on the content of the utterances made by the generation AI. The menu creation unit can also cause the generation AI to reflect the user's feedback in real time and improve the content of the training menu. This enables more effective training by improving the content of the training menu based on user feedback.

[0127] The training analysis unit can estimate the user's emotions and adjust the accuracy of the training analysis based on the estimated user's emotions. The training analysis unit can, for example, use a generation AI to estimate the user's emotions and adjust the accuracy of the training analysis based on the estimated user's emotions. For example, if the user is nervous, the training analysis unit can cause the generation AI to increase the accuracy of the training analysis and avoid misunderstandings. Furthermore, if the user is relaxed, the training analysis unit can cause the generation AI to provide a natural analysis result. Furthermore, if the user is excited, the generation AI can provide an emphasized analysis result. In this way, by adjusting the accuracy of the training analysis according to the user's emotions, more accurate analysis results can be provided.

[0128] The training analysis unit can analyze the training data in real time and provide immediate feedback. The training analysis unit can, for example, use a generation AI to analyze the training data in real time and provide immediate feedback. For example, the training analysis unit can have the generation AI analyze the training data in real time and provide immediate feedback. The training analysis unit can also have the generation AI analyze the training data in real time based on the content of utterances and provide immediate feedback. The training analysis unit can also have the generation AI grasp the training data in real time and provide optimal feedback. In this way, by analyzing the training data in real time and providing immediate feedback, the effectiveness of training can be improved.

[0129] The training analysis unit can analyze trends in the training data and propose a long-term training plan. The training analysis unit can, for example, use a generation AI to analyze trends in the training data and propose a long-term training plan. For example, the training analysis unit can have the generation AI analyze trends in the training data and propose a long-term training plan. The training analysis unit can also have the generation AI analyze trends in the training data based on the content of utterances and propose a long-term training plan. The training analysis unit can also have the generation AI grasp trends in the training data in real time and propose an optimal long-term training plan. In this way, by analyzing trends in the training data and proposing a long-term training plan, the effectiveness of training can be improved.

[0130] The training analysis unit can collect training data from different devices and perform a comprehensive analysis. The training analysis unit can, for example, use a generation AI to collect training data from different devices and perform a comprehensive analysis. For example, the training analysis unit can have the generation AI collect training data from different devices and perform a comprehensive analysis. The training analysis unit can also analyze the training data collected from different devices based on the content of utterances made by the generation AI. The training analysis unit can also have the generation AI grasp the training data from different devices in real time and perform a comprehensive analysis. In this way, by collecting training data from different devices and performing a comprehensive analysis, more accurate analysis results can be provided.

[0131] The training analysis unit can estimate the user's emotions and adjust the display method of the training analysis based on the estimated user's emotions. The training analysis unit can, for example, use a generation AI to estimate the user's emotions and adjust the display method of the training analysis based on the estimated user's emotions. For example, if the user is nervous, the generation AI can provide a simple, highly visible display method. Also, if the user is relaxed, the training analysis unit can provide a display method that includes detailed information. Also, if the user is excited, the generation AI can provide an emphasized display method. This allows for a more appropriate display by adjusting the display method of the training analysis according to the user's emotions.

[0132] The training analysis unit can compare the training data with other users to enhance a sense of competition. For example, the training analysis unit uses a generation AI to compare the training data with other users to enhance a sense of competition. For example, the training analysis unit can have the generation AI compare the training data with other users to enhance a sense of competition. The training analysis unit can also provide training data compared with other users based on the content of utterances made by the generation AI. The training analysis unit can also have the generation AI compare the training data in real time to enhance a sense of competition. In this way, by comparing the training data with other users, a sense of competition can be enhanced and motivation for training can be improved.

[0133] The training analysis unit can visualize the training data and provide it in a form that is easy for the user to understand. For example, the training analysis unit uses a generation AI to visualize the training data and provide it in a form that is easy for the user to understand. For example, the training analysis unit can use the generation AI to visualize the training data and provide it in a form that is easy for the user to understand. The training analysis unit can also provide visualized training data based on the content of the utterance made by the generation AI. The training analysis unit can also use the generation AI to visualize the training data in real time and provide it in a form that is easy for the user to understand. In this way, by visualizing the training data, information can be provided in a form that is easy for the user to understand.

[0134] The training analysis unit can collect training data from different devices and perform a comprehensive analysis. The training analysis unit can, for example, use a generation AI to collect training data from different devices and perform a comprehensive analysis. For example, the training analysis unit can have the generation AI collect training data from different devices and perform a comprehensive analysis. The training analysis unit can also analyze the training data collected from different devices based on the content of utterances made by the generation AI. The training analysis unit can also have the generation AI grasp the training data from different devices in real time and perform a comprehensive analysis. In this way, by collecting training data from different devices and performing a comprehensive analysis, more accurate analysis results can be provided. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned conversion unit, analysis unit, provision unit, chat unit, menu creation unit, and training analysis unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the conversion unit is realized by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the chat unit is realized by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the menu creation unit is realized by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the training analysis unit is realized by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned conversion unit, analysis unit, provision unit, chat unit, menu creation unit, and training analysis unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the conversion unit is realized by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the chat unit is realized by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the menu creation unit is realized by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the training analysis unit is realized by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned conversion unit, analysis unit, provision unit, chat unit, menu creation unit, and training analysis unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the conversion unit is realized by the processor 46 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the processor 46 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the chat unit is realized by the processor 46 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the menu creation unit is realized by the processor 46 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the training analysis unit is realized by the processor 46 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned conversion unit, analysis unit, provision unit, chat unit, menu creation unit, and training analysis unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the conversion unit is realized by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the chat unit is realized by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the menu creation unit is realized by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the training analysis unit is realized by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0135] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0136] When analyzing the content of a user's speech, the analysis unit can refer to the user's past speech history. For example, more accurate analysis can be performed based on previous statements or questions made by the user. The analysis unit can also learn the user's speech patterns and perform analysis optimized for each individual user. Furthermore, the analysis unit can provide consistent analysis results by storing the user's speech history in the cloud and making it accessible from other devices. This makes it possible to provide more accurate analysis results by utilizing the user's past speech history.

[0137] The providing unit can estimate the user's emotions and adjust the timing of advice based on the estimated user emotions. For example, if the user is nervous, the generation AI can delay the timing of providing advice and wait for the user to relax. Also, if the user is relaxed, the generation AI can immediately provide advice, thereby maintaining the user's concentration. Furthermore, if the user is excited, the generation AI can adjust the timing of providing advice to calm the user's excitement. In this way, more effective advice can be provided by adjusting the timing of advice according to the user's emotions.

[0138] The menu creation unit can monitor the user's fitness level in real time and dynamically adjust the training menu. For example, it can monitor the user's heart rate and exercise intensity to provide an appropriate training menu. It can also analyze the user's fatigue level and reduce the training menu as necessary. Furthermore, if the user's fitness level improves, the generation AI can automatically update the training menu and provide a more challenging menu. This allows for effective training by dynamically adjusting the training menu according to the user's fitness level.

[0139] The training analysis unit can estimate the user's emotions and customize training feedback based on the estimated user emotions. For example, if the user is nervous, the generation AI can provide positive feedback to increase the user's motivation. If the user is relaxed, the generation AI can provide detailed feedback to deepen the user's understanding. Furthermore, if the user is excited, the generation AI can provide calm feedback to maintain the user's concentration. This allows for more effective training by customizing feedback according to the user's emotions.

[0140] The chat module can analyze the content of a user's speech and automatically provide related topics and information. For example, if a user asks a question about a specific sport, the generation AI can provide the latest news and articles related to that sport. If a user talks about training methods, the generation AI can provide relevant training videos and guides. Furthermore, if a user talks about a specific player, the generation AI can provide that player's statistical data and performance analysis. This allows for a richer chat experience by providing relevant information based on the user's speech.

[0141] The providing unit can estimate the user's emotions and adjust the format of the advice based on the estimated user's emotions. For example, if the user is nervous, the generation AI can provide simple, visual advice that is easy for the user to understand. Alternatively, if the user is relaxed, the generation AI can provide detailed text-format advice that allows the user to understand in depth. Furthermore, if the user is excited, the generation AI can provide audio or video-format advice, thereby maintaining the user's excitement and providing effective advice. In this way, by adjusting the format of advice according to the user's emotions, more effective advice can be provided.

[0142] The conversion unit analyzes the content of the user's utterance and understands the intention of the utterance, allowing for more natural text conversion. For example, if the user speaks in the form of a question, the generation AI can understand that intention and convert it into text as an appropriate question. Also, if the user speaks in the form of an imperative, the generation AI can understand that intention and convert it into text as an appropriate imperative. Furthermore, if the user speaks with emotion, the generation AI can convert the text to reflect that emotion. This understanding of the intention of the utterance enables more natural and accurate text conversion.

[0143] When analyzing the content of a user's speech, the analysis unit can simultaneously analyze text in different languages. For example, if a user speaks in multiple languages, the generation AI can analyze each language and provide appropriate advice. The analysis unit can also automatically translate the content of the speech based on the user's language settings and provide analysis results. Furthermore, the analysis unit can integrate text in different languages ​​and provide comprehensive analysis results. This makes it possible to provide services that cater to global users by analyzing content of speech in different languages.

[0144] The providing unit can estimate the user's emotions and personalize the content of advice based on the estimated user's emotions. For example, if the user is nervous, the generating AI can provide advice to relax. Also, if the user is relaxed, the generating AI can provide more challenging advice. Furthermore, if the user is excited, the generating AI can provide advice that takes advantage of that excitement. In this way, by personalizing the content of advice according to the user's emotions, more effective advice can be provided.

[0145] The menu creation unit can analyze the user's training history and propose an optimal training menu based on past training data. For example, it can analyze the effects of the user's past training and propose an effective training menu. The generation AI can also customize the training menu based on the user's training history and provide a menu optimized for each individual user. Furthermore, by storing the user's training history in the cloud and making it accessible from other devices, a consistent training menu can be provided. This makes it possible to provide a more effective training menu by utilizing the user's training history.

[0146] The processing flow of the second embodiment will be briefly explained below.

[0147] Step 1: The conversion unit converts the user's speech into text in real time. For example, the conversion unit uses a generation AI to convert the user's speech into text in real time. The conversion unit can also convert the speech into text when the generation AI receives a prompt such as "Please convert this speech into text." Furthermore, the conversion unit can enable the generation AI to understand the context of the speech and perform appropriate text conversion. Step 2: The analysis unit analyzes the text converted by the conversion unit. For example, the analysis unit uses the generation AI to analyze the content of the text. The analysis unit can also analyze the content of the text when the generation AI receives a prompt such as "Please analyze the content of this text." Furthermore, the analysis unit can enable the generation AI to understand the context of the text and perform appropriate analysis. Step 3: The provision unit provides advice based on the analysis results obtained by the analysis unit. For example, the provision unit uses the generation AI to provide training or tactical advice based on the analysis results. The provision unit can also provide advice when the generation AI receives a prompt such as "Please provide advice based on this analysis result." Furthermore, the provision unit can enable the generation AI to understand the analysis results and provide appropriate advice. Step 4: The chat section has a video chat with friends or team members. For example, the chat section uses the generation AI to improve the quality of the video chat. The chat section can also improve the quality of the video chat by receiving a prompt such as "Please improve the quality of this video chat." Furthermore, the chat section can use the generation AI to understand the context of the video chat and make appropriate quality improvements.

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0168] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0169] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0178] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0184] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0195] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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).

[0205] 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.

[0206] 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."

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0218] 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.

[0219] [Explanation of symbols]

[0220] 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. a conversion unit that converts a user's speech into text in real time; an analysis unit that analyzes the text converted by the conversion unit; a providing unit that provides advice based on the analysis result obtained by the analyzing unit; A chat section for video chatting with friends or team members. A system characterized by:

2. It has a menu creation section where you can create training menus and share them with friends and team members.

2. The system of claim 1.

3. Equipped with a training analysis department that analyzes training content and proposes effective training methods 2. The system of claim 1.

4. The conversion unit Convert user speech into text in real time 2. The system of claim 1.

5. The analysis unit Analyzing the converted speech 2. The system of claim 1.

6. The providing unit Providing training and tactical advice based on the analysis results 2. The system of claim 1.

7. The conversion unit Estimate the user's emotions and adjust the accuracy of the speech-to-text conversion based on the estimated user emotions.

2. The system of claim 1.

8. The conversion unit Analyze background sounds and remove noise to improve text conversion accuracy 2. The system of claim 1.

9. The conversion unit Automatically adjusts the speed of text conversion according to the user's speaking rate 2. The system of claim 1.

Images