system

Abstract

The system according to the embodiment aims to change the face and voice of the teacher according to the user's wishes. [Solution] A system according to an embodiment includes a reception unit, a change unit, and a provision unit. The reception unit receives a user's request. The change unit changes the teacher's face and voice based on the request received by the reception unit. The provision unit provides the face and voice changed by the change unit in real time.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not allow users to change the face or voice of their desired teacher during online learning, which can lead to a decrease in learning satisfaction.

[0005] The system according to the embodiment aims to change the face and voice of the teacher according to the user's wishes. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a change unit, and a provision unit. The reception unit receives a user's request. The change unit changes the teacher's face and voice based on the request received by the reception unit. The provision unit provides the face and voice changed by the change unit in real time. [Effects of the Invention]

[0007] The system according to the embodiment can change the teacher's face and voice according to the user's wishes. [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) An online learning system according to an embodiment of the present invention changes the teacher's face and voice based on the user's request and provides them in real time. When a user accesses an online learning service and requests a change to the teacher's face or voice, the AI ​​changes the teacher's face and voice based on the user's request. The AI ​​uses facial recognition and voice synthesis technologies to change the teacher's face and voice in real time. For example, the online learning system receives requests from users, such as "I want to change to a teacher with specific characteristics" or "I want to change to a teacher with a calm voice." This information is input into the AI. The AI ​​then analyzes the input information and changes the teacher's face and voice. The AI ​​uses facial recognition technology to change the teacher's face and voice, and voice synthesis technology to change the teacher's voice. For example, if a user requests a teacher with specific characteristics, the AI ​​changes the teacher's face and voice to match those characteristics. The changed face and voice are provided to the user in real time. For example, the teacher's face and voice are changed while the user is taking an online class. In this way, users can progress through their studies with a teacher's face and voice tailored to their preferences. This allows the online learning system to change the teacher's face and voice based on the user's preferences and provide them in real time. For example, even if a user does not like the face or voice of a particular teacher, they can study with a teacher's face and voice that suits their preferences. This is also expected to increase learning motivation and improve learning effectiveness. Furthermore, AI can learn the user's learning history and preferences and suggest more appropriate teacher faces and voices. For example, it can suggest appropriate teacher faces and voices for the next lesson based on the faces and voices of teachers that the user has preferred in the past. This allows users to study more comfortably.

[0029] An online learning system according to an embodiment includes a receiving unit, a modifying unit, and a providing unit. The receiving unit receives a user's request. The user's request may include, but is not limited to, facial features, a tone of voice, or a specific character. For example, when a user accesses an online learning service and requests to change the teacher's face or voice, the receiving unit inputs the user's specific request. For example, the user may input a request such as "I want to change to a teacher with specific features" or "I want to change to a teacher with a calm voice." The modifying unit uses AI to modify the teacher's face or voice based on the request received by the receiving unit. The modification may be performed by, but is not limited to, changing facial features, the pitch or speed of the voice, or other methods. For example, the modifying unit may use facial recognition technology to modify the teacher's face. The modifying unit may also use voice synthesis technology to modify the teacher's voice. For example, the AI ​​may use facial recognition technology to modify the teacher's face to have specific features and voice synthesis technology to modify the teacher's voice to a specific tone. The providing unit provides the face and voice modified by the modifying unit in real time. The real-time provision is performed based on, for example, criteria such as the allowable range of delay time and processing speed, but is not limited to these examples. For example, the provision unit changes the teacher's face and voice in real time while the user is taking an online class. In this way, the online learning system according to the embodiment can change the teacher's face and voice based on the user's wishes and provide them in real time.

[0030] The modification unit can modify the teacher's face using facial recognition technology. Facial recognition technology includes, but is not limited to, deep learning, feature point extraction, and the like. The modification unit can modify the teacher's face using deep learning, for example. For example, a deep learning model learns the teacher's facial features and modifies the face based on the user's wishes. The modification unit can also modify the teacher's face using feature point extraction technology. For example, feature point extraction technology extracts feature points of the teacher's face and modifies the face based on the user's wishes. The modification unit can also modify the teacher's face in real time using facial recognition technology. For example, facial recognition technology analyzes the teacher's face in real time and modifies the face based on the user's wishes. In this way, the teacher's face can be modified using facial recognition technology.

[0031] The change unit can change the teacher's voice using speech synthesis technology. Examples of speech synthesis technology include, but are not limited to, text-to-speech synthesis and waveform generation. The change unit can change the teacher's voice using, for example, text-to-speech synthesis technology. For example, text-to-speech synthesis technology converts text data into speech data and changes the voice based on the user's preference. The change unit can also change the teacher's voice using waveform generation technology. For example, waveform generation technology generates a speech waveform and changes the voice based on the user's preference. The change unit can also change the teacher's voice in real time using speech synthesis technology. For example, speech synthesis technology analyzes the teacher's voice in real time and changes the voice based on the user's preference. In this way, the teacher's voice can be changed using speech synthesis technology.

[0032] The providing unit can provide the user with a face or voice that has been modified in real time. Providing in real time includes, but is not limited to, criteria such as, for example, an acceptable delay time range and a processing speed. For example, the providing unit provides the teacher's face or voice in real time within an acceptable delay time range. For example, the providing unit modifies the teacher's face or voice in real time and provides it to the user. The providing unit can also provide the teacher's face or voice in real time, taking processing speed into consideration. For example, the providing unit modifies the teacher's face or voice at a high processing speed and provides it to the user. The providing unit can also build a system for providing the user with a face or voice that has been modified in real time. For example, the providing unit develops infrastructure for providing the user with a face or voice that has been modified in real time. As a result, the user can immediately confirm the changes by providing the face or voice that has been modified in real time.

[0033] The reception unit can accept specific user requests. Specific requests include, but are not limited to, facial features, tone of voice, and specific character traits. For example, when a user accesses an online learning service and requests to change the teacher's face or voice, the reception unit inputs the specific request. For example, the user may input a request such as "I want to change to a teacher with specific features" or "I want to change to a teacher with a calm voice." The reception unit can also provide an interface for accepting the user's specific requests. For example, the reception unit provides a form or options for the user to input their request. The reception unit can also build a system for accepting the user's specific requests. For example, the reception unit prepares a database or server for accepting the user's requests. By accepting the user's specific requests, more personalized services can be provided.

[0034] The change unit can learn the user's learning history and preferences and suggest a teacher's face and voice for the next lesson. The learning history includes, for example, past lesson content, test results, etc., but is not limited to these examples. The preferences include, for example, survey results, past selection history, etc., but are not limited to these examples. The change unit can, for example, analyze the user's learning history and suggest an appropriate teacher's face and voice for the next lesson. For example, the change unit can suggest an appropriate teacher's face and voice for the next lesson based on past lesson content and test results. The change unit can also learn the user's preferences and suggest a teacher's face and voice that matches the preferences for the next lesson. For example, the change unit can suggest an appropriate teacher's face and voice for the next lesson based on survey results and past selection history. The change unit can also build a system for learning the user's learning history and preferences. For example, the change unit can store the user's learning history and preferences in a database and make appropriate suggestions for the next lesson. By learning the user's learning history and preferences, more appropriate suggestions can be made for the next lesson.

[0035] The reception unit can analyze the user's past preference history and select a reception method. The past preference history includes, for example, past request content, history data, etc., but is not limited to these examples. The reception unit, for example, automatically displays the faces and voices of teachers that the user has frequently preferred in the past as candidates. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the faces and voices of teachers preferred for a specific time period based on the user's past preference history. For example, the reception unit suggests the faces and voices of teachers preferred for a specific time period based on the user's past preference history. The reception unit can also build a system for analyzing the user's past preference history. For example, the reception unit accumulates and analyzes the user's past preference history in a database. In this way, a more appropriate reception method can be provided by analyzing the past preference history.

[0036] The reception unit may perform filtering based on the user's current learning situation and areas of interest when receiving the request. Examples of the learning situation include, but are not limited to, the user's current learning progress and test results. Examples of the areas of interest include, but are not limited to, past selection history and survey results. The reception unit may, for example, preferentially suggest the faces and voices of teachers related to the subject the user is currently studying. For example, the reception unit may filter and display relevant teacher faces and voices based on the user's areas of interest. The reception unit may also suggest appropriate teacher faces and voices based on the user's learning progress. For example, the reception unit may suggest appropriate teacher faces and voices based on the user's learning progress. The reception unit may also build a system for filtering based on the user's current learning situation and areas of interest when receiving the request. For example, the reception unit may store the user's learning situation and areas of interest in a database and perform filtering. This enables more appropriate suggestions to be made by filtering based on the user's learning situation and areas of interest.

[0037] The reception unit can select a reception means according to the user's input method when receiving the request. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, when the user inputs the request by voice, the reception unit receives the request using voice recognition technology. For example, when the user inputs the request by text, the reception unit receives the request using text analysis technology. Furthermore, when the user inputs the request by image, the reception unit can also receive the request using image recognition technology. For example, the reception unit analyzes the user's request using image recognition technology and receives it. Furthermore, the reception unit can also build a system for selecting the optimal reception means according to the user's input method when receiving the request. For example, the reception unit stores the user's input method in a database and selects the optimal reception means. This improves user convenience by selecting the optimal reception means according to the input method.

[0038] When accepting requests, the reception unit can prioritize accepting highly relevant requests by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, when the user is in a specific area, the reception unit prioritizes suggesting the faces and voices of teachers related to that area. For example, when the user is traveling, the reception unit prioritizes suggesting the faces and voices of teachers related to the user's travel destination. Furthermore, when the user is at home, the reception unit can prioritize suggesting the faces and voices of teachers suitable for studying at home. For example, the reception unit prioritizes accepting highly relevant requests based on the user's geographical location information. Furthermore, the reception unit can also build a system for prioritizing accepting highly relevant requests by taking into account the user's geographical location information when accepting requests. For example, the reception unit stores the user's geographical location information in a database and prioritizes accepting highly relevant requests. This enables more relevant suggestions to be made by taking geographical location information into account.

[0039] The reception unit may analyze the user's social media activity when receiving a request and receive related requests. Social media activity includes, but is not limited to, for example, the content of posts and a history of likes. For example, the reception unit may prioritize suggestions based on the faces and voices of teachers the user follows on social media. For example, the reception unit may analyze the content of the user's social media posts and suggest the faces and voices of related teachers. The reception unit may also suggest the faces and voices of related teachers based on the activity of the user's friends on social media. For example, the reception unit may receive related requests based on the user's social media activity. The reception unit may also analyze the user's social media activity when receiving a request and build a system for receiving related requests. For example, the reception unit may accumulate and analyze the user's social media activity in a database. This allows for more relevant suggestions to be made by analyzing the social media activity.

[0040] The reception unit can customize the reception method by reflecting the user's past feedback at the time of desired reception. Past feedback includes, but is not limited to, survey results and comments. The reception unit, for example, suggests the optimal reception method based on feedback provided by the user in the past. For example, the reception unit may preferentially suggest the face and voice of a specific teacher based on the user's past feedback. The reception unit can also analyze the user's feedback and provide a customized reception method. For example, the reception unit customizes the reception method based on the user's feedback. The reception unit can also build a system for customizing the reception method by reflecting the user's past feedback at the time of desired reception. For example, the reception unit accumulates the user's past feedback in a database and analyzes it. In this way, a more appropriate reception method can be provided by reflecting the past feedback.

[0041] The modification unit may set the level of detail of the modification based on the user's preference when modifying the face or voice. The level of detail may include, but is not limited to, the scope of the modification and the depth of the modification. For example, if the user desires a detailed modification, the modification unit may modify even the facial details or the tone of the voice. For example, if the user desires a simple modification, the modification unit may modify only the facial contours or the basic tone of the voice. Furthermore, if the user desires a specific feature, the modification unit may modify the face or voice to match the feature. For example, the modification unit may set the level of detail of the modification based on the user's preference. Furthermore, the modification unit may construct a system for setting the level of detail of the modification based on the user's preference when modifying the face or voice. For example, the modification unit may store the user's preference in a database and set the level of detail of the modification. This allows for more personalized modification by adjusting the level of detail based on the user's preference.

[0042] The modification unit can improve the accuracy of the modification by referring to the user's past modification history when modifying the face or voice. The past modification history includes, but is not limited to, for example, past modification content and history data. The modification unit improves the accuracy of the modification, for example, based on facial and voice features selected by the user in the past. For example, the modification unit analyzes preference trends from the user's past modification history and improves the accuracy. The modification unit can also improve the accuracy of the modification based on feedback provided by the user in the past. For example, the modification unit improves the accuracy of the modification based on the user's feedback. The modification unit can also build a system for improving the accuracy of the modification by referring to the user's past modification history when modifying the face or voice. For example, the modification unit accumulates and analyzes the user's past modification history in a database. In this way, the accuracy of the modification is improved by referring to the past modification history.

[0043] When changing the face or voice, the change unit can learn the user's learning history and preferences and suggest an appropriate face or voice for the next lesson. The learning history includes, for example, but is not limited to, past lesson content and test results. The preferences include, for example, but are not limited to, survey results and past selection history. The change unit, for example, analyzes the user's learning history and suggests an appropriate face or voice for the next lesson. For example, the change unit suggests an appropriate face or voice for the next lesson based on past lesson content and test results. The change unit can also learn the user's preferences and suggest a face or voice that matches the preferences for the next lesson. For example, the change unit suggests an appropriate face or voice for the next lesson based on survey results and past selection history. The change unit can also build a system for learning the user's learning history and preferences. For example, the change unit stores the user's learning history and preferences in a database and makes appropriate suggestions for the next lesson. By learning the user's learning history and preferences, more appropriate suggestions can be made for the next lesson.

[0044] The change unit can make changes to the face and voice to make them more relevant by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, if the user is in a specific area, the change unit changes the face and voice to ones related to that area. For example, if the user is traveling, the change unit changes the face and voice to ones related to the travel destination. Furthermore, if the user is at home, the change unit can also change the face and voice to ones suitable for studying at home. For example, the change unit changes the face and voice to ones more relevant based on the user's geographical location information. Furthermore, the change unit can also build a system for making changes to the face and voice to make them more relevant by taking into account the user's geographical location information. For example, the change unit stores the user's geographical location information in a database and changes the face and voice to ones more relevant. This enables more relevant changes by taking the geographical location information into account.

[0045] The change unit can analyze the user's social media activity and make related changes when changing the face or voice. Social media activity includes, but is not limited to, for example, the content of posts and the history of likes. For example, the change unit changes the face or voice to that of a teacher the user follows on social media. For example, the change unit analyzes the content of the user's social media posts and changes the face or voice to a related face or voice. The change unit can also change the face or voice to a related face or voice based on the activity of the user's friends on social media. For example, the change unit changes the face or voice to a related face or voice based on the user's social media activity. The change unit can also build a system for analyzing the user's social media activity and making related changes when changing the face or voice. For example, the change unit accumulates and analyzes the user's social media activity in a database. This makes it possible to make more relevant changes by analyzing the social media activity.

[0046] The modification unit can customize the modification method by reflecting the user's past feedback when modifying the face or voice. Past feedback includes, but is not limited to, survey results, comments, and the like. The modification unit, for example, proposes an optimal modification method based on feedback provided by the user in the past. For example, the modification unit prioritizes modifying a specific face or voice based on the user's past feedback. The modification unit can also analyze the user's feedback and customize and provide the modification method. For example, the modification unit customizes the modification method based on the user's feedback. The modification unit can also build a system for customizing the modification method by reflecting the user's past feedback when modifying the face or voice. For example, the modification unit accumulates the user's past feedback in a database and analyzes it. In this way, a more appropriate modification method can be provided by reflecting the past feedback.

[0047] The providing unit can select a display method by referring to the user's past display history when providing the face or voice. The past display history includes, but is not limited to, past display content and history data, for example. The providing unit, for example, proposes an optimal display method based on display methods that the user has previously preferred. For example, the providing unit preferentially proposes a specific display method from the user's past display history. The providing unit can also analyze the user's past display history and propose the most efficient display method. For example, the providing unit proposes the most efficient display method based on the user's past display history. The providing unit can also build a system for selecting a display method by referring to the user's past display history when providing the face or voice. For example, the providing unit accumulates and analyzes the user's past display history in a database. This makes it possible to provide a more appropriate display method by referring to the past display history.

[0048] The providing unit can customize the display content according to the user's current learning situation when providing the face and voice. Learning situations include, but are not limited to, current learning progress, test results, and the like. For example, the providing unit prioritizes providing display content related to the subject the user is currently studying. For example, the providing unit provides appropriate display content according to the user's learning progress. The providing unit can also customize and provide related display content based on the user's area of ​​interest. For example, the providing unit can customize and provide related display content based on the user's area of ​​interest. The providing unit can also build a system for customizing the display content according to the user's current learning situation when providing the face and voice. For example, the providing unit accumulates the user's learning situation in a database and customizes the display content. This allows more appropriate information to be provided by customizing the display content according to the learning situation.

[0049] The providing unit can improve the display method by reflecting user feedback when providing a face or voice. Examples of feedback include, but are not limited to, survey results and comments. The providing unit, for example, suggests an optimal display method based on feedback previously provided by the user. For example, the providing unit preferentially suggests a specific display method based on the user's past feedback. The providing unit can also analyze the user's feedback and customize and provide the display method. For example, the providing unit customizes the display method based on the user's feedback. The providing unit can also build a system for improving the display method by reflecting user feedback when providing a face or voice. For example, the providing unit accumulates and analyzes user feedback in a database. In this way, a more appropriate display method can be provided by reflecting the feedback.

[0050] The providing unit can select a display method by taking into consideration the user's geographical location information when providing the face and voice. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, when the user is in a specific area, the providing unit can provide a display method related to that area. For example, when the user is traveling, the providing unit can provide a display method related to the travel destination. Furthermore, when the user is at home, the providing unit can also provide a display method suitable for studying at home. For example, the providing unit can select a highly relevant display method based on the user's geographical location information. Furthermore, the providing unit can also build a system for selecting an optimal display method by taking into consideration the user's geographical location information when providing the face and voice. For example, the providing unit can accumulate the user's geographical location information in a database and select a highly relevant display method. This allows a more appropriate display method to be provided by taking the geographical location information into consideration.

[0051] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the face and voice. Device information includes, but is not limited to, the device type and OS version. For example, if the user is using a smartphone, the providing unit provides a display method tailored to the screen size. For example, if the user is using a tablet, the providing unit provides a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. For example, the providing unit selects the optimal display method based on the user's device information. Furthermore, the providing unit can also build a system for selecting the optimal display method by taking into consideration the user's device information when providing the face and voice. For example, the providing unit accumulates the user's device information in a database and selects the optimal display method. This allows a more appropriate display method to be provided by taking the device information into consideration.

[0052] The providing unit can make the display content multilingual in accordance with the user's language setting when providing the face or voice. Examples of language settings include, but are not limited to, the user's language selection and the device's language setting. The providing unit automatically sets the display content based on the user's device's language setting, for example. For example, the providing unit provides a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the providing unit can also provide the display content in that language. For example, the providing unit makes the display content multilingual based on the user's language setting. Furthermore, the providing unit can also build a system for making the display content multilingual in accordance with the user's language setting when providing the face or voice. For example, the providing unit stores the user's language setting in a database and makes the display content multilingual. This allows for multilingual support according to the language setting, thereby accommodating a larger number of users.

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

[0054] The change unit can gradually change the teacher's face and voice based on the user's learning progress. For example, when a user takes a lesson for the first time, a friendly face and voice are provided, and as the user's learning progresses, the face and voice are changed to a more professional face and voice. Also, when a user is working on a specific task, a face and voice appropriate for that task can be provided. Furthermore, a system can be constructed that automatically adjusts the changes in the face and voice according to the user's learning progress. This can improve the learning effect by providing the optimal face and voice according to the user's learning progress.

[0055] The providing unit can suggest changes to the teacher's face or voice based on the user's learning history. For example, it can suggest an appropriate face or voice for the next lesson based on the user's past preferred teacher faces and voices. It can also suggest an appropriate face or voice for a related lesson based on the user's preferred faces and voices for a specific subject or topic. Furthermore, it can build a system that analyzes the user's learning history and suggests the most suitable face or voice. This enables more personalized suggestions based on the user's learning history.

[0056] The providing unit can change the teacher's face and voice based on the user's learning environment. For example, if the user is learning in a quiet environment, a calm voice can be provided, and if the user is learning in a noisy environment, a clear voice can be provided. Also, if the user is learning at night, a calm face and voice can be provided, and if the user is learning during the day, an energetic face and voice can be provided. Furthermore, a system can be constructed that monitors the user's learning environment in real time and provides an appropriate face and voice. This can improve learning effectiveness by providing the optimal face and voice according to the user's learning environment.

[0057] The change unit can change the teacher's face and voice based on the user's learning style. For example, a visually appealing face can be provided for a visual learner, and a clear voice can be provided for an auditory learner. If a user prefers interactive learning, an expressive face and voice can be provided, and if a user prefers static learning, a calm face and voice can be provided. Furthermore, a system can be constructed that analyzes a user's learning style and provides the most appropriate face and voice. This can improve learning effectiveness by providing the most appropriate face and voice according to the user's learning style.

[0058] The change unit can change the teacher's face and voice based on the user's learning goals. For example, if the user is studying for a specific exam, the change unit can provide a face and voice appropriate for that exam. Also, if the user is trying to master a specific skill, the change unit can provide a face and voice appropriate for that skill. Furthermore, it is possible to build a system that analyzes the user's learning goals and provides the most appropriate face and voice. This can improve learning effectiveness by providing the most appropriate face and voice according to the user's learning goals.

[0059] The modification unit can customize the changes to the teacher's face and voice based on the user's learning history. For example, it can provide an appropriate face and voice for the next lesson based on the facial and voice characteristics that the user has previously preferred. It can also provide an appropriate face and voice for related lessons based on the face and voice that the user has preferred for a specific subject or topic. Furthermore, it can also build a system that analyzes the user's learning history and provides the most appropriate face and voice. This makes it possible to modify the face and voice more individually based on the user's learning history.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit receives the user's wishes. The user's wishes may include, for example, facial features, tone of voice, or a specific character. When a user accesses the online learning service and wishes to change the teacher's face or voice, the user inputs a specific wish. For example, the user may input a wish such as "I want to change to a teacher with specific features" or "I want to change to a teacher with a calm voice." Step 2: The modification unit uses AI to modify the teacher's face and voice based on the requests received by the reception unit. Modifications can be made, for example, by changing facial features or the pitch or speed of the voice. The modification unit can modify the teacher's face using facial recognition technology and the teacher's voice using voice synthesis technology. For example, the AI ​​can modify the teacher's face to have specific features using facial recognition technology and the teacher's voice to have a specific tone using voice synthesis technology. Step 3: The providing unit provides the face and voice changed by the changing unit in real time. The real-time provision is performed based on criteria such as the acceptable range of delay time and processing speed. The providing unit changes the teacher's face and voice in real time while the user is taking the online class.

[0062] (Example 2) An online learning system according to an embodiment of the present invention changes the teacher's face and voice based on the user's request and provides them in real time. When a user accesses an online learning service and requests a change to the teacher's face or voice, the AI ​​changes the teacher's face and voice based on the user's request. The AI ​​uses facial recognition and voice synthesis technologies to change the teacher's face and voice in real time. For example, the online learning system receives requests from users, such as "I want to change to a teacher with specific characteristics" or "I want to change to a teacher with a calm voice." This information is input into the AI. The AI ​​then analyzes the input information and changes the teacher's face and voice. The AI ​​uses facial recognition technology to change the teacher's face and voice, and voice synthesis technology to change the teacher's voice. For example, if a user requests a teacher with specific characteristics, the AI ​​changes the teacher's face and voice to match those characteristics. The changed face and voice are provided to the user in real time. For example, the teacher's face and voice are changed while the user is taking an online class. In this way, users can progress through their studies with a teacher's face and voice tailored to their preferences. This allows the online learning system to change the teacher's face and voice based on the user's preferences and provide them in real time. For example, even if a user does not like the face or voice of a particular teacher, they can study with a teacher's face and voice that suits their preferences. This is also expected to increase learning motivation and improve learning effectiveness. Furthermore, AI can learn the user's learning history and preferences and suggest more appropriate teacher faces and voices. For example, it can suggest appropriate teacher faces and voices for the next lesson based on the faces and voices of teachers that the user has preferred in the past. This allows users to study more comfortably.

[0063] An online learning system according to an embodiment includes a receiving unit, a modifying unit, and a providing unit. The receiving unit receives a user's request. The user's request may include, but is not limited to, facial features, a tone of voice, or a specific character. For example, when a user accesses an online learning service and requests to change the teacher's face or voice, the receiving unit inputs the user's specific request. For example, the user may input a request such as "I want to change to a teacher with specific features" or "I want to change to a teacher with a calm voice." The modifying unit uses AI to modify the teacher's face or voice based on the request received by the receiving unit. The modification may be performed by, but is not limited to, changing facial features, the pitch or speed of the voice, or other methods. For example, the modifying unit may use facial recognition technology to modify the teacher's face. The modifying unit may also use voice synthesis technology to modify the teacher's voice. For example, the AI ​​may use facial recognition technology to modify the teacher's face to have specific features and voice synthesis technology to modify the teacher's voice to a specific tone. The providing unit provides the face and voice modified by the modifying unit in real time. The real-time provision is performed based on, for example, criteria such as the allowable range of delay time and processing speed, but is not limited to these examples. For example, the provision unit changes the teacher's face and voice in real time while the user is taking an online class. In this way, the online learning system according to the embodiment can change the teacher's face and voice based on the user's wishes and provide them in real time.

[0064] The modification unit can modify the teacher's face using facial recognition technology. Facial recognition technology includes, but is not limited to, deep learning, feature point extraction, and the like. The modification unit can modify the teacher's face using deep learning, for example. For example, a deep learning model learns the teacher's facial features and modifies the face based on the user's wishes. The modification unit can also modify the teacher's face using feature point extraction technology. For example, feature point extraction technology extracts feature points of the teacher's face and modifies the face based on the user's wishes. The modification unit can also modify the teacher's face in real time using facial recognition technology. For example, facial recognition technology analyzes the teacher's face in real time and modifies the face based on the user's wishes. In this way, the teacher's face can be modified using facial recognition technology.

[0065] The change unit can change the teacher's voice using speech synthesis technology. Examples of speech synthesis technology include, but are not limited to, text-to-speech synthesis and waveform generation. The change unit can change the teacher's voice using, for example, text-to-speech synthesis technology. For example, text-to-speech synthesis technology converts text data into speech data and changes the voice based on the user's preference. The change unit can also change the teacher's voice using waveform generation technology. For example, waveform generation technology generates a speech waveform and changes the voice based on the user's preference. The change unit can also change the teacher's voice in real time using speech synthesis technology. For example, speech synthesis technology analyzes the teacher's voice in real time and changes the voice based on the user's preference. In this way, the teacher's voice can be changed using speech synthesis technology.

[0066] The providing unit can provide the user with a face or voice that has been modified in real time. Providing in real time includes, but is not limited to, criteria such as, for example, an acceptable delay time range and a processing speed. For example, the providing unit provides the teacher's face or voice in real time within an acceptable delay time range. For example, the providing unit modifies the teacher's face or voice in real time and provides it to the user. The providing unit can also provide the teacher's face or voice in real time, taking processing speed into consideration. For example, the providing unit modifies the teacher's face or voice at a high processing speed and provides it to the user. The providing unit can also build a system for providing the user with a face or voice that has been modified in real time. For example, the providing unit develops infrastructure for providing the user with a face or voice that has been modified in real time. As a result, the user can immediately confirm the changes by providing the face or voice that has been modified in real time.

[0067] The reception unit can accept specific user requests. Specific requests include, but are not limited to, facial features, tone of voice, and specific character traits. For example, when a user accesses an online learning service and requests to change the teacher's face or voice, the reception unit inputs the specific request. For example, the user may input a request such as "I want to change to a teacher with specific features" or "I want to change to a teacher with a calm voice." The reception unit can also provide an interface for accepting the user's specific requests. For example, the reception unit provides a form or options for the user to input their request. The reception unit can also build a system for accepting the user's specific requests. For example, the reception unit prepares a database or server for accepting the user's requests. By accepting the user's specific requests, more personalized services can be provided.

[0068] The change unit can learn the user's learning history and preferences and suggest a teacher's face and voice for the next lesson. The learning history includes, for example, past lesson content, test results, etc., but is not limited to these examples. The preferences include, for example, survey results, past selection history, etc., but are not limited to these examples. The change unit can, for example, analyze the user's learning history and suggest an appropriate teacher's face and voice for the next lesson. For example, the change unit can suggest an appropriate teacher's face and voice for the next lesson based on past lesson content and test results. The change unit can also learn the user's preferences and suggest a teacher's face and voice that matches the preferences for the next lesson. For example, the change unit can suggest an appropriate teacher's face and voice for the next lesson based on survey results and past selection history. The change unit can also build a system for learning the user's learning history and preferences. For example, the change unit can store the user's learning history and preferences in a database and make appropriate suggestions for the next lesson. By learning the user's learning history and preferences, more appropriate suggestions can be made for the next lesson.

[0069] The reception unit can estimate the user's emotions and adjust the desired reception method based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit provides a simple interface and minimizes input steps. For example, if the user is relaxed, the reception unit provides detailed input options and suggests a customizable input method. The reception unit can also prioritize voice input when the user is in a hurry, allowing the user to quickly input their preferences. For example, if the user is in a hurry, the reception unit uses voice recognition technology to receive the user's preferences. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the reception method to be adjusted according to the user's emotions, providing a more comfortable user experience.

[0070] The reception unit can analyze the user's past preference history and select a reception method. The past preference history includes, for example, past request content, history data, etc., but is not limited to these examples. The reception unit, for example, automatically displays the faces and voices of teachers that the user has frequently preferred in the past as candidates. For example, the reception unit preferentially suggests input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest the faces and voices of teachers preferred for a specific time period based on the user's past preference history. For example, the reception unit suggests the faces and voices of teachers preferred for a specific time period based on the user's past preference history. The reception unit can also build a system for analyzing the user's past preference history. For example, the reception unit accumulates and analyzes the user's past preference history in a database. In this way, a more appropriate reception method can be provided by analyzing the past preference history.

[0071] The reception unit may perform filtering based on the user's current learning situation and areas of interest when receiving the request. Examples of the learning situation include, but are not limited to, the user's current learning progress and test results. Examples of the areas of interest include, but are not limited to, past selection history and survey results. The reception unit may, for example, preferentially suggest the faces and voices of teachers related to the subject the user is currently studying. For example, the reception unit may filter and display relevant teacher faces and voices based on the user's areas of interest. The reception unit may also suggest appropriate teacher faces and voices based on the user's learning progress. For example, the reception unit may suggest appropriate teacher faces and voices based on the user's learning progress. The reception unit may also build a system for filtering based on the user's current learning situation and areas of interest when receiving the request. For example, the reception unit may store the user's learning situation and areas of interest in a database and perform filtering. This enables more appropriate suggestions to be made by filtering based on the user's learning situation and areas of interest.

[0072] The reception unit can select a reception means according to the user's input method when receiving the request. Examples of input methods include, but are not limited to, voice input, text input, and image input. For example, when the user inputs the request by voice, the reception unit receives the request using voice recognition technology. For example, when the user inputs the request by text, the reception unit receives the request using text analysis technology. Furthermore, when the user inputs the request by image, the reception unit can also receive the request using image recognition technology. For example, the reception unit analyzes the user's request using image recognition technology and receives it. Furthermore, the reception unit can also build a system for selecting the optimal reception means according to the user's input method when receiving the request. For example, the reception unit stores the user's input method in a database and selects the optimal reception means. This improves user convenience by selecting the optimal reception means according to the input method.

[0073] The reception unit can estimate the user's emotions and determine the priority of the requests to be received based on the estimated user's emotions. For example, if the user is nervous, the reception unit can prioritize suggesting teachers with relaxing faces and voices. For example, if the user is having fun, the reception unit can prioritize suggesting teachers with a cheerful face and voice. Furthermore, if the user is tired, the reception unit can prioritize suggesting teachers with a calm face and voice. For example, the reception unit can estimate the user's emotions and determine the priority based on the estimated emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. This enables more appropriate suggestions to be made by determining the priority based on the user's emotions.

[0074] When accepting requests, the reception unit can prioritize accepting highly relevant requests by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, when the user is in a specific area, the reception unit prioritizes suggesting the faces and voices of teachers related to that area. For example, when the user is traveling, the reception unit prioritizes suggesting the faces and voices of teachers related to the user's travel destination. Furthermore, when the user is at home, the reception unit can prioritize suggesting the faces and voices of teachers suitable for studying at home. For example, the reception unit prioritizes accepting highly relevant requests based on the user's geographical location information. Furthermore, the reception unit can also build a system for prioritizing accepting highly relevant requests by taking into account the user's geographical location information when accepting requests. For example, the reception unit stores the user's geographical location information in a database and prioritizes accepting highly relevant requests. This enables more relevant suggestions to be made by taking geographical location information into account.

[0075] The reception unit may analyze the user's social media activity when receiving a request and receive related requests. Social media activity includes, but is not limited to, for example, the content of posts and a history of likes. For example, the reception unit may prioritize suggestions based on the faces and voices of teachers the user follows on social media. For example, the reception unit may analyze the content of the user's social media posts and suggest the faces and voices of related teachers. The reception unit may also suggest the faces and voices of related teachers based on the activity of the user's friends on social media. For example, the reception unit may receive related requests based on the user's social media activity. The reception unit may also analyze the user's social media activity when receiving a request and build a system for receiving related requests. For example, the reception unit may accumulate and analyze the user's social media activity in a database. This allows for more relevant suggestions to be made by analyzing the social media activity.

[0076] The reception unit can customize the reception method by reflecting the user's past feedback at the time of desired reception. Past feedback includes, but is not limited to, survey results and comments. The reception unit, for example, suggests the optimal reception method based on feedback provided by the user in the past. For example, the reception unit may preferentially suggest the face and voice of a specific teacher based on the user's past feedback. The reception unit can also analyze the user's feedback and provide a customized reception method. For example, the reception unit customizes the reception method based on the user's feedback. The reception unit can also build a system for customizing the reception method by reflecting the user's past feedback at the time of desired reception. For example, the reception unit accumulates the user's past feedback in a database and analyzes it. In this way, a more appropriate reception method can be provided by reflecting the past feedback.

[0077] The modification unit can estimate the user's emotions and adjust the method of modifying the face and voice based on the estimated user emotions. For example, if the user is relaxed, the modification unit modifies the face and voice to a calm one. For example, if the user is excited, the modification unit modifies the face and voice to an energetic one. Furthermore, if the user is tired, the modification unit can also modify the face and voice to a calm one. For example, the modification unit can estimate the user's emotions and adjust the method of modifying the face and voice based on the estimated emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This allows for more appropriate modification by adjusting the modification method based on the user's emotions.

[0078] The modification unit may set the level of detail of the modification based on the user's preference when modifying the face or voice. The level of detail may include, but is not limited to, the scope of the modification and the depth of the modification. For example, if the user desires a detailed modification, the modification unit may modify even the facial details or the tone of the voice. For example, if the user desires a simple modification, the modification unit may modify only the facial contours or the basic tone of the voice. Furthermore, if the user desires a specific feature, the modification unit may modify the face or voice to match the feature. For example, the modification unit may set the level of detail of the modification based on the user's preference. Furthermore, the modification unit may construct a system for setting the level of detail of the modification based on the user's preference when modifying the face or voice. For example, the modification unit may store the user's preference in a database and set the level of detail of the modification. This allows for more personalized modification by adjusting the level of detail based on the user's preference.

[0079] The modification unit can improve the accuracy of the modification by referring to the user's past modification history when modifying the face or voice. The past modification history includes, but is not limited to, for example, past modification content and history data. The modification unit improves the accuracy of the modification, for example, based on facial and voice features selected by the user in the past. For example, the modification unit analyzes preference trends from the user's past modification history and improves the accuracy. The modification unit can also improve the accuracy of the modification based on feedback provided by the user in the past. For example, the modification unit improves the accuracy of the modification based on the user's feedback. The modification unit can also build a system for improving the accuracy of the modification by referring to the user's past modification history when modifying the face or voice. For example, the modification unit accumulates and analyzes the user's past modification history in a database. In this way, the accuracy of the modification is improved by referring to the past modification history.

[0080] When changing the face or voice, the change unit can learn the user's learning history and preferences and suggest an appropriate face or voice for the next lesson. The learning history includes, for example, but is not limited to, past lesson content and test results. The preferences include, for example, but are not limited to, survey results and past selection history. The change unit, for example, analyzes the user's learning history and suggests an appropriate face or voice for the next lesson. For example, the change unit suggests an appropriate face or voice for the next lesson based on past lesson content and test results. The change unit can also learn the user's preferences and suggest a face or voice that matches the preferences for the next lesson. For example, the change unit suggests an appropriate face or voice for the next lesson based on survey results and past selection history. The change unit can also build a system for learning the user's learning history and preferences. For example, the change unit stores the user's learning history and preferences in a database and makes appropriate suggestions for the next lesson. By learning the user's learning history and preferences, more appropriate suggestions can be made for the next lesson.

[0081] The modification unit can estimate the user's emotions and determine the priority of the faces and voices to be modified based on the estimated user emotions. For example, if the user is nervous, the modification unit prioritizes modifying the faces and voices to make them more relaxed. For example, if the user is having fun, the modification unit prioritizes modifying the faces and voices to make them more cheerful. Furthermore, if the user is tired, the modification unit can prioritize modifying the faces and voices to make them more calm. For example, the modification unit estimates the user's emotions and determines the priority based on the estimated emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. This enables more appropriate modification by determining the priority based on the user's emotions.

[0082] The change unit can make changes to the face and voice to make them more relevant by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, if the user is in a specific area, the change unit changes the face and voice to ones related to that area. For example, if the user is traveling, the change unit changes the face and voice to ones related to the travel destination. Furthermore, if the user is at home, the change unit can also change the face and voice to ones suitable for studying at home. For example, the change unit changes the face and voice to ones more relevant based on the user's geographical location information. Furthermore, the change unit can also build a system for making changes to the face and voice to make them more relevant by taking into account the user's geographical location information. For example, the change unit stores the user's geographical location information in a database and changes the face and voice to ones more relevant. This enables more relevant changes by taking the geographical location information into account.

[0083] The change unit can analyze the user's social media activity and make related changes when changing the face or voice. Social media activity includes, but is not limited to, for example, the content of posts and the history of likes. For example, the change unit changes the face or voice to that of a teacher the user follows on social media. For example, the change unit analyzes the content of the user's social media posts and changes the face or voice to a related face or voice. The change unit can also change the face or voice to a related face or voice based on the activity of the user's friends on social media. For example, the change unit changes the face or voice to a related face or voice based on the user's social media activity. The change unit can also build a system for analyzing the user's social media activity and making related changes when changing the face or voice. For example, the change unit accumulates and analyzes the user's social media activity in a database. This makes it possible to make more relevant changes by analyzing the social media activity.

[0084] The modification unit can customize the modification method by reflecting the user's past feedback when modifying the face or voice. Past feedback includes, but is not limited to, survey results, comments, and the like. The modification unit, for example, proposes an optimal modification method based on feedback provided by the user in the past. For example, the modification unit prioritizes modifying a specific face or voice based on the user's past feedback. The modification unit can also analyze the user's feedback and customize and provide the modification method. For example, the modification unit customizes the modification method based on the user's feedback. The modification unit can also build a system for customizing the modification method by reflecting the user's past feedback when modifying the face or voice. For example, the modification unit accumulates the user's past feedback in a database and analyzes it. In this way, a more appropriate modification method can be provided by reflecting the past feedback.

[0085] The providing unit can estimate the user's emotions and adjust the display method of the face and voice to be provided based on the estimated user's emotions. For example, when the user is nervous, the providing unit provides a simple, highly visible display method. For example, when the user is relaxed, the providing unit provides a display method including detailed information. Furthermore, when the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. For example, when the user is in a hurry, the providing unit provides a display method that focuses on the main points. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. This makes it possible to provide a more comfortable user experience by adjusting the display method based on the user's emotions.

[0086] The providing unit can select a display method by referring to the user's past display history when providing the face or voice. The past display history includes, but is not limited to, past display content and history data, for example. The providing unit, for example, proposes an optimal display method based on display methods that the user has previously preferred. For example, the providing unit preferentially proposes a specific display method from the user's past display history. The providing unit can also analyze the user's past display history and propose the most efficient display method. For example, the providing unit proposes the most efficient display method based on the user's past display history. The providing unit can also build a system for selecting a display method by referring to the user's past display history when providing the face or voice. For example, the providing unit accumulates and analyzes the user's past display history in a database. This makes it possible to provide a more appropriate display method by referring to the past display history.

[0087] The providing unit can customize the display content according to the user's current learning situation when providing the face and voice. Learning situations include, but are not limited to, current learning progress, test results, and the like. For example, the providing unit prioritizes providing display content related to the subject the user is currently studying. For example, the providing unit provides appropriate display content according to the user's learning progress. The providing unit can also customize and provide related display content based on the user's area of ​​interest. For example, the providing unit can customize and provide related display content based on the user's area of ​​interest. The providing unit can also build a system for customizing the display content according to the user's current learning situation when providing the face and voice. For example, the providing unit accumulates the user's learning situation in a database and customizes the display content. This allows more appropriate information to be provided by customizing the display content according to the learning situation.

[0088] The providing unit can improve the display method by reflecting user feedback when providing a face or voice. Examples of feedback include, but are not limited to, survey results and comments. The providing unit, for example, suggests an optimal display method based on feedback previously provided by the user. For example, the providing unit preferentially suggests a specific display method based on the user's past feedback. The providing unit can also analyze the user's feedback and customize and provide the display method. For example, the providing unit customizes the display method based on the user's feedback. The providing unit can also build a system for improving the display method by reflecting user feedback when providing a face or voice. For example, the providing unit accumulates and analyzes user feedback in a database. In this way, a more appropriate display method can be provided by reflecting the feedback.

[0089] The providing unit can estimate the user's emotions and determine the priority of faces and voices to be provided based on the estimated user emotions. For example, if the user is nervous, the providing unit can prioritize providing relaxing faces and voices. For example, if the user is having fun, the providing unit can prioritize providing happy faces and voices. Furthermore, if the user is tired, the providing unit can prioritize providing calm faces and voices. For example, the providing unit can estimate the user's emotions and determine the priority based on the estimated emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. This enables more appropriate suggestions to be made by determining the priority based on the user's emotions.

[0090] The providing unit can select a display method by taking into consideration the user's geographical location information when providing the face and voice. Examples of geographical location information include, but are not limited to, GPS data and IP addresses. For example, when the user is in a specific area, the providing unit can provide a display method related to that area. For example, when the user is traveling, the providing unit can provide a display method related to the travel destination. Furthermore, when the user is at home, the providing unit can also provide a display method suitable for studying at home. For example, the providing unit can select a highly relevant display method based on the user's geographical location information. Furthermore, the providing unit can also build a system for selecting an optimal display method by taking into consideration the user's geographical location information when providing the face and voice. For example, the providing unit can accumulate the user's geographical location information in a database and select a highly relevant display method. This allows a more appropriate display method to be provided by taking the geographical location information into consideration.

[0091] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the face and voice. Device information includes, but is not limited to, the device type and OS version. For example, if the user is using a smartphone, the providing unit provides a display method tailored to the screen size. For example, if the user is using a tablet, the providing unit provides a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a simple and highly visible display method. For example, the providing unit selects the optimal display method based on the user's device information. Furthermore, the providing unit can also build a system for selecting the optimal display method by taking into consideration the user's device information when providing the face and voice. For example, the providing unit accumulates the user's device information in a database and selects the optimal display method. This allows a more appropriate display method to be provided by taking the device information into consideration.

[0092] The providing unit can make the display content multilingual in accordance with the user's language setting when providing the face or voice. Examples of language settings include, but are not limited to, the user's language selection and the device's language setting. The providing unit automatically sets the display content based on the user's device's language setting, for example. For example, the providing unit provides a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the providing unit can also provide the display content in that language. For example, the providing unit makes the display content multilingual based on the user's language setting. Furthermore, the providing unit can also build a system for making the display content multilingual in accordance with the user's language setting when providing the face or voice. For example, the providing unit stores the user's language setting in a database and makes the display content multilingual. This allows for multilingual support according to the language setting, thereby accommodating a larger number of users. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, change unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives a user's request. The change unit is realized by the specific processing unit 290 of the data processing device 12 and changes the teacher's face and voice using AI. The provision unit is realized by the output device 40 of the smart device 14 and provides the changed face and voice in real time. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, change unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a user's request. The change unit is realized by the specific processing unit 290 of the data processing device 12 and changes the teacher's face and voice using AI. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the changed face and voice in real time. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, change unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives the user's request. The change unit is realized by the specific processing unit 290 of the data processing device 12 and changes the teacher's face and voice using AI. The provision unit is realized by the display 343 of the headset type terminal 314 and provides the changed face and voice in real time. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, change unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives the user's request. The change unit is realized by the specific processing unit 290 of the data processing device 12 and changes the teacher's face and voice using AI. The provision unit is realized by the speaker 240 of the robot 414 and provides the changed face and voice in real time.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The change unit can gradually change the teacher's face and voice based on the user's learning progress. For example, when a user takes a lesson for the first time, a friendly face and voice are provided, and as the user's learning progresses, the face and voice are changed to a more professional face and voice. Also, when a user is working on a specific task, a face and voice appropriate for that task can be provided. Furthermore, a system can be constructed that automatically adjusts the changes in the face and voice according to the user's learning progress. This can improve the learning effect by providing the optimal face and voice according to the user's learning progress.

[0095] The providing unit can suggest changes to the teacher's face or voice based on the user's learning history. For example, it can suggest an appropriate face or voice for the next lesson based on the user's past preferred teacher faces and voices. It can also suggest an appropriate face or voice for a related lesson based on the user's preferred faces and voices for a specific subject or topic. Furthermore, it can build a system that analyzes the user's learning history and suggests the most suitable face or voice. This enables more personalized suggestions based on the user's learning history.

[0096] The change unit can estimate the user's emotions and change the teacher's face and voice based on the estimated emotions. For example, if the user is feeling stressed, the change unit can change the teacher's face and voice to one that is relaxing. Also, if the user is concentrating, the change unit can change the teacher's face and voice to one that helps the user maintain concentration. Furthermore, it is possible to build a system that monitors the user's emotions in real time and changes the teacher's face and voice at the appropriate time. This can improve learning effectiveness by providing the most appropriate face and voice according to the user's emotions.

[0097] The providing unit can change the teacher's face and voice based on the user's learning environment. For example, if the user is learning in a quiet environment, a calm voice can be provided, and if the user is learning in a noisy environment, a clear voice can be provided. Also, if the user is learning at night, a calm face and voice can be provided, and if the user is learning during the day, an energetic face and voice can be provided. Furthermore, a system can be constructed that monitors the user's learning environment in real time and provides an appropriate face and voice. This can improve learning effectiveness by providing the optimal face and voice according to the user's learning environment.

[0098] The change unit can change the teacher's face and voice based on the user's learning style. For example, a visually appealing face can be provided for a visual learner, and a clear voice can be provided for an auditory learner. If a user prefers interactive learning, an expressive face and voice can be provided, and if a user prefers static learning, a calm face and voice can be provided. Furthermore, a system can be constructed that analyzes a user's learning style and provides the most appropriate face and voice. This can improve learning effectiveness by providing the most appropriate face and voice according to the user's learning style.

[0099] The providing unit can estimate the user's emotions and adjust the display method of the teacher's face and voice based on the estimated emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. Alternatively, if the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a system can be constructed that provides a display method that focuses on the main points. In this way, a more comfortable user experience can be provided by adjusting the display method based on the user's emotions.

[0100] The change unit can change the teacher's face and voice based on the user's learning goals. For example, if the user is studying for a specific exam, the change unit can provide a face and voice appropriate for that exam. Also, if the user is trying to master a specific skill, the change unit can provide a face and voice appropriate for that skill. Furthermore, it is possible to build a system that analyzes the user's learning goals and provides the most appropriate face and voice. This can improve learning effectiveness by providing the most appropriate face and voice according to the user's learning goals.

[0101] The providing unit can estimate the user's emotions and adjust the timing of providing the teacher's face and voice based on the estimated emotions. For example, if the user is concentrating, the unit can refrain from changing the face and voice to maintain concentration. Also, if the user is tired, the unit can provide a refreshing face and voice. Furthermore, it is possible to build a system that monitors the user's emotions in real time and changes the face and voice at the appropriate timing. This can improve learning effectiveness by adjusting the optimal timing of providing the face and voice based on the user's emotions.

[0102] The modification unit can customize the changes to the teacher's face and voice based on the user's learning history. For example, it can provide an appropriate face and voice for the next lesson based on the facial and voice characteristics that the user has previously preferred. It can also provide an appropriate face and voice for related lessons based on the face and voice that the user has preferred for a specific subject or topic. Furthermore, it can also build a system that analyzes the user's learning history and provides the most appropriate face and voice. This makes it possible to modify the face and voice more individually based on the user's learning history.

[0103] The providing unit can estimate the user's emotions and customize the way in which the teacher's face and voice are presented based on the estimated emotions. For example, if the user is relaxed, the system can provide a face and voice containing detailed information. Alternatively, if the user is nervous, the system can provide a simple, highly visible face and voice. Furthermore, it is possible to build a system that monitors the user's emotions in real time and selects an appropriate presentation method. This allows the system to customize the optimal presentation method according to the user's emotions, providing a more comfortable user experience.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The reception unit receives the user's wishes. The user's wishes may include, for example, facial features, tone of voice, or a specific character. When a user accesses the online learning service and wishes to change the teacher's face or voice, the user inputs a specific wish. For example, the user may input a wish such as "I want to change to a teacher with specific features" or "I want to change to a teacher with a calm voice." Step 2: The modification unit uses AI to modify the teacher's face and voice based on the requests received by the reception unit. Modifications can be made, for example, by changing facial features or the pitch or speed of the voice. The modification unit can modify the teacher's face using facial recognition technology and the teacher's voice using voice synthesis technology. For example, the AI ​​can modify the teacher's face to have specific features using facial recognition technology and the teacher's voice to have a specific tone using voice synthesis technology. Step 3: The providing unit provides the face and voice changed by the changing unit in real time. The real-time provision is performed based on criteria such as the acceptable range of delay time and processing speed. The providing unit changes the teacher's face and voice in real time while the user is taking the online class.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 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 reception unit that receives a user's request; a change unit that changes the face and voice of the teacher based on the request received by the reception unit; a providing unit that provides the face and voice changed by the unit in real time. A system characterized by:

2. The change unit Change the teacher's face using facial recognition technology 2. The system of claim 1.

3. The change unit Change the teacher's voice using speech synthesis technology 2. The system of claim 1.

4. The providing unit Providing users with real-time changes to their face and voice 2. The system of claim 1.

5. The reception unit Accepting specific user requests 2. The system of claim 1.

6. The change unit Learns the user's learning history and preferences and suggests the teacher's face and voice for the next lesson 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and change the desired reception method based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past request history and select the reception method 2. The system of claim 1.

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