System

Abstract

An object of the system according to the embodiment is to provide an experience in which a user can interact with a past famous person or a historical person in real time.SOLUTION: A system according to an embodiment includes a deployment platform, a dialog generator, and a person reproducer. The deployment platform provides an interface according to the user's device. The dialogue generation unit generates dialogue content on the basis of an input of a user. The person reproducing unit reproduces the target person.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to provide users with the experience of interacting with famous people or historical figures of the past in real time.

[0005] The system according to the embodiment aims to provide an experience in which a user can have a real-time conversation with famous people and historical figures from the past. [Means for solving the problem]

[0006] The system according to the embodiment includes a deployment platform, a dialogue generation unit, and a character reproduction unit. The deployment platform provides an interface according to a user's device. The dialogue generation unit generates dialogue content based on user input. The character reproduction unit reproduces a target character. [Effects of the Invention]

[0007] The system according to the embodiment can provide users with an experience of interacting with famous people and historical figures of the past in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The dialogue application according to an embodiment of the present invention is a system that provides a user with the experience of having real-time dialogue with famous people and historical figures from the past, thereby enabling the user to deepen their knowledge of the thoughts, statements, and historical background of famous people and historical figures from the past.

[0029] A dialogue application according to an embodiment includes a deployment platform, a dialogue generation unit, and a character reproduction unit. The deployment platform provides an interface tailored to the user's device. For example, it provides a simple interface for one-handed operation for smartphones, and a multifunctional interface that takes advantage of the user's large screen for tablets. The deployment platform also provides seamless data synchronization between devices, allowing users to access the same dialogue history from any device. For example, it synchronizes the user's dialogue history and settings information in real time using cloud storage. The dialogue generation unit generates dialogue content based on user input. For example, the generation AI receives prompts including the name of the person the user wants to interact with and questions, and generates dialogue content based on the prompts. The generation AI also generates dialogue content based on the user's emotions, providing a more personalized experience. For example, if the user is excited, it provides an interesting anecdote. The character reproduction unit reproduces the target character. For example, the generation AI learns data such as the target character's statements, actions, and historical background, and then conducts a dialogue in real time based on that data. The character reproduction unit also reproduces not only the target character's statements and actions, but also their facial expressions and tone of voice, providing a more realistic dialogue experience. For example, Napoleon's words can be reproduced in his tone of voice and displayed along with his facial expressions. This allows the dialogue application according to the embodiment to provide users with an experience of interacting with famous people and historical figures in real time. For example, by interacting with historical figures, users can gain a deeper understanding of their thoughts and historical background. Furthermore, by interacting with scientists and artists, users can learn about their achievements and influences.

[0030] The deployment platform provides a user interface optimized for each device, enabling operability that takes advantage of the characteristics of each device. For example, the deployment platform provides a simple interface that is easy to operate with one hand for smartphones, and a multifunctional interface that takes advantage of the large screen for tablets. For example, the design for smartphones is centered around swipe operations, while the design for tablets is centered around drag-and-drop operations. This makes it possible to provide operability that is optimized for each device.

[0031] The deployment platform realizes seamless data synchronization between devices, allowing users to access the same interaction history from any device. The deployment platform uses, for example, cloud storage to synchronize the user's interaction history and setting information in real time. For example, it allows a user to continue an interaction started on a smartphone on a PC. This makes it possible to realize seamless data synchronization between devices.

[0032] The deployment platform is also compatible with wearable devices, enabling use on a wider variety of devices. For example, the deployment platform provides simple interaction and notification functions for smartwatches, allowing users to easily start interactions. For example, questions can be asked using voice input and simple answers displayed. This allows support for wearable devices, enabling use on a wider variety of devices.

[0033] The deployment platform provides an offline mode, allowing it to be used even in environments with unstable internet connections. For example, the deployment platform locally stores the interaction history in offline mode and automatically synchronizes it when the internet connection is restored. For example, the interaction history can be continued while using the platform on an airplane and synchronized after landing. This allows it to be used even in environments with unstable internet connections.

[0034] The dialogue generation unit can automatically present related historical materials and literature based on the dialogue content, thereby deepening the user's understanding. For example, if the user shows interest in a particular statement or event during the dialogue, the dialogue generation unit automatically presents related historical materials and literature. For example, if the user shows interest in a statement made by Napoleon, the dialogue generation unit displays materials on wars and policies related to that statement. This allows the automatic presentation of related historical materials and literature based on the dialogue content, thereby deepening the user's understanding.

[0035] The dialogue generation unit can provide detailed information about a topic in which the user is interested during the dialogue in real time. For example, if the user shows interest in a particular topic during the dialogue, the dialogue generation unit provides detailed information about the topic in real time. For example, if the user says, "I want to know more about this person's life," detailed information about the person's life is displayed. This makes it possible to provide detailed information about a topic in which the user is interested during the dialogue in real time.

[0036] The dialogue generation unit supports voice input and voice output of dialogue, making it possible to provide an interface that is easy to use for people with visual or hearing impairments. The dialogue generation unit, for example, supports voice input and voice output of dialogue, allowing people with visual impairments to engage in dialogue by voice. For example, it uses voice recognition technology to convert a user's question into text, and AI responds by voice. This makes it possible to provide an interface that is easy to use for people with visual or hearing impairments.

[0037] The dialogue generation unit automatically translates the dialogue content, allowing users who speak different languages ​​to converse with the same person. The dialogue generation unit, for example, automatically translates the dialogue content in real time, allowing users who speak different languages ​​to converse with the same person. For example, a question asked in Japanese can be translated into English, and an answer can be obtained in English. This allows users who speak different languages ​​to converse with the same person.

[0038] The character reproduction unit reproduces not only the target person's words and actions, but also their facial expressions and tone of voice, providing a more realistic dialogue experience. For example, the character reproduction unit combines speech synthesis technology and facial recognition technology to reproduce the target person's words and actions, as well as their facial expressions and tone of voice. For example, Napoleon's words are reproduced in his tone of voice and his facial expressions are also displayed. This allows the target person's words and actions, as well as their facial expressions and tone of voice, to be reproduced, providing a more realistic dialogue experience.

[0039] The character reenactment unit generates dialogue content that takes into account the historical and cultural background of the target character, thereby promoting deeper understanding for the user. For example, the character reenactment unit uses historical materials and literature as training data to generate dialogue content that takes into account the historical and cultural background of the target character. For example, the character reenactment unit generates dialogue content that takes into account the historical background of Shakespeare. This allows for the generation of dialogue content that takes into account the historical and cultural background of the target character, promoting deeper understanding for the user.

[0040] The person reproduction unit generates a 3D avatar of the target person, thereby providing a visually realistic conversation experience. The person reproduction unit generates, for example, a 3D avatar of the target person, thereby providing a visually realistic conversation experience. For example, it generates a 3D avatar of Napoleon and reproduces his words and actions in real time. This allows the generation of a 3D avatar of the target person, thereby providing a visually realistic conversation experience.

[0041] The person reproducing unit can integrate multiple data sources to reproduce a target person, thereby realizing a more multifaceted reproduction. For example, the person reproducing unit can integrate multiple data sources, such as books, videos, and audio, to reproduce a target person, thereby realizing a more multifaceted reproduction. For example, to reproduce Napoleon's words and actions, books, videos, and audio data related to him are integrated. This allows the integration of multiple data sources to realize a more multifaceted reproduction.

[0042] The dialogue generation unit can automatically present related academic papers and research materials based on the content of the dialogue to support the user's learning. For example, if the user shows interest in a particular topic during the dialogue, the dialogue generation unit automatically presents related academic papers and research materials. For example, if the user says, "I want to know more about this theory," academic papers related to that theory are displayed. In this way, related academic papers and research materials can be automatically presented based on the content of the dialogue to support the user's learning.

[0043] The dialogue generation unit can provide answers from multiple perspectives to a user's question, thereby promoting deeper understanding. The dialogue generation unit can provide answers from multiple perspectives to a user's question. For example, if a user is asked about a historical event, the dialogue generation unit can provide explanations of the event from different perspectives. This allows the user to provide answers from multiple perspectives to a user's question, thereby promoting deeper understanding.

[0044] The dialogue generation unit can automatically evaluate the user's learning progress based on the dialogue content and suggest what content should be learned next. The dialogue generation unit can automatically evaluate the user's learning progress based on the dialogue content and suggest what content should be learned next. For example, if the user has a sufficient understanding of a particular topic, the dialogue generation unit can suggest the next topic. In this way, the user's learning progress can be automatically evaluated and what content should be learned next can be suggested.

[0045] The dialogue generation unit can share the dialogue content with other users and provide opportunities for collaborative learning. The dialogue generation unit, for example, shares the dialogue content with other users and provides opportunities for collaborative learning. For example, users who are interested in the same topic share the dialogue content and exchange opinions. This allows the dialogue content to be shared with other users and provides opportunities for collaborative learning.

[0046] The dialogue generation unit can automatically generate interactive quizzes and tests based on the dialogue content to check the user's level of understanding. The dialogue generation unit can, for example, automatically generate interactive quizzes based on the dialogue content to check the user's level of understanding. For example, important points that came up during the dialogue can be presented in the form of a quiz. In this way, interactive quizzes and tests can be automatically generated based on the dialogue content to check the user's level of understanding.

[0047] The dialogue generation unit can analyze the dialogue history of the user and propose an individual study plan. The dialogue generation unit, for example, analyzes the dialogue history of the user and proposes an individual study plan. For example, it suggests what content the user should learn next based on a topic in which the user is interested. In this way, it is possible to analyze the dialogue history of the user and propose an individual study plan.

[0048] The dialogue generation unit can suggest related events or seminars that the user may be interested in based on the dialogue content. The dialogue generation unit, for example, suggests related events or seminars that the user may be interested in based on the dialogue content. For example, if the user shows interest in a particular topic, events or seminars related to that topic are suggested. In this way, related events or seminars that the user may be interested in can be suggested.

[0049] The dialogue generation unit can link the dialogue content with other learning applications to provide a comprehensive learning experience. The dialogue generation unit, for example, can link the dialogue content with other learning applications to provide a comprehensive learning experience. For example, the content learned during the dialogue can be reflected in other learning applications to advance learning. In this way, the dialogue content can be linked with other learning applications to provide a comprehensive learning experience.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The dialogue generation unit can automatically present relevant academic papers and research materials based on the content of the user's dialogue. For example, if a user asks a question about a particular historical event, academic papers and research materials related to that event will be displayed. This allows the user to gain deeper knowledge through dialogue. The dialogue generation unit can also provide the latest research results on topics that interest the user. For example, if a user expresses interest in a particular theory during a dialogue with a scientist, the latest research results on that theory will be displayed. Furthermore, the dialogue generation unit can provide answers from multiple perspectives to questions raised by the user during the dialogue. For example, it can provide explanations of historical events from different perspectives to deepen the user's understanding.

[0052] The dialogue generation unit can automatically generate interactive quizzes and tests based on the content of the user's dialogue to check the user's level of understanding. For example, it can present important points that came up during the dialogue in the form of a quiz to check how much the user understands. This allows the user to review what they learned through the dialogue and deepen their understanding. The dialogue generation unit can also suggest what content to study next based on the user's answers. For example, if the user has a sufficient understanding of a particular topic, it will suggest the next topic. Furthermore, the dialogue generation unit can automatically evaluate the user's learning progress and suggest an individual learning plan. This allows the user to study at their own pace.

[0053] The dialogue generation unit can share the content of the dialogue with other users, providing opportunities for collaborative learning. For example, users who are interested in the same topic can share the content of the dialogue and exchange opinions. This allows users to cooperate with other users in learning. The dialogue generation unit can also collect and provide answers from other users to questions expressed by the user during the dialogue. For example, it can collect other users' opinions on a particular historical event and provide them to the user. Furthermore, the dialogue generation unit can suggest related online communities and forums based on the interests expressed by the user during the dialogue. This allows users to share the knowledge they have gained through the dialogue with other users and further deepen their understanding.

[0054] The dialogue generation unit can suggest related events or seminars that may interest the user based on the content of the dialogue. For example, if the user shows interest in a particular topic, the dialogue generation unit can suggest events or seminars related to that topic. This allows the user to further deepen the knowledge they have gained through the dialogue. The dialogue generation unit can also suggest related online courses or workshops based on the interests the user has expressed during the dialogue. For example, if the user has expressed interest in a particular historical event, the dialogue generation unit can suggest online courses related to that event. The dialogue generation unit can also suggest related books or materials based on the interests the user has expressed during the dialogue. This allows the user to further deepen the knowledge they have gained through the dialogue.

[0055] The dialogue generation unit can automatically evaluate the user's learning progress based on the dialogue content and suggest the next content to study. For example, if the user has a sufficient understanding of a particular topic, the next topic can be suggested. This allows the user to proceed with their learning at their own pace. The dialogue generation unit can also analyze the user's dialogue history and suggest an individual learning plan. For example, the next content to study can be suggested based on topics that interest the user. Furthermore, the dialogue generation unit can suggest related learning content based on the user's learning progress. For example, if the user is learning about a particular historical event, learning content related to that event can be suggested. This allows the user to proceed with their learning at their own pace.

[0056] The dialogue generation unit can link the dialogue content with other learning applications to provide a comprehensive learning experience. For example, the content learned during the dialogue can be reflected in other learning applications to advance learning. This allows the user to further deepen the knowledge gained through the dialogue using other learning applications. The dialogue generation unit can also suggest related learning applications based on the interests the user expressed during the dialogue. For example, if the user expressed interest in a particular topic, the dialogue generation unit can suggest learning applications related to that topic. Furthermore, the dialogue generation unit can share the user's learning progress with other learning applications and suggest a comprehensive learning plan. This allows the user to further deepen the knowledge gained through the dialogue using other learning applications.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The deployment platform provides an interface appropriate for the user's device. For example, it provides a simple interface that is easy to operate with one hand for smartphones, and a multifunctional interface that takes advantage of the large screen for tablets. The deployment platform also enables seamless data synchronization between devices, allowing users to access the same interaction history from any device. For example, it uses cloud storage to synchronize the user's interaction history and settings information in real time. Step 2: The dialogue generation unit generates dialogue content based on the user's input. For example, the generation AI receives a prompt containing the name of the person the user wants to talk to and the question, and generates dialogue content based on that prompt. The generation AI also generates dialogue content based on the user's emotions, providing a more personalized experience. For example, if the user is excited, it will provide an interesting anecdote. Step 3: The character reproduction unit reproduces the target person. For example, the generative AI learns data such as the target person's words, actions, and historical background, and then conducts a real-time dialogue based on that data. The character reproduction unit also reproduces not only the target person's words and actions, but also their facial expressions and tone of voice, providing a more realistic dialogue experience. For example, Napoleon's words are reproduced in his tone of voice, and facial expressions are also displayed.

[0059] (Example 2) The dialogue application according to an embodiment of the present invention is a system that provides a user with the experience of having real-time dialogue with famous people and historical figures from the past, thereby enabling the user to deepen their knowledge of the thoughts, statements, and historical background of famous people and historical figures from the past.

[0060] A dialogue application according to an embodiment includes a deployment platform, a dialogue generation unit, and a character reproduction unit. The deployment platform provides an interface tailored to the user's device. For example, it provides a simple interface for one-handed operation for smartphones, and a multifunctional interface that takes advantage of the user's large screen for tablets. The deployment platform also provides seamless data synchronization between devices, allowing users to access the same dialogue history from any device. For example, it synchronizes the user's dialogue history and settings information in real time using cloud storage. The dialogue generation unit generates dialogue content based on user input. For example, the generation AI receives prompts including the name of the person the user wants to interact with and questions, and generates dialogue content based on the prompts. The generation AI also generates dialogue content based on the user's emotions, providing a more personalized experience. For example, if the user is excited, it provides an interesting anecdote. The character reproduction unit reproduces the target character. For example, the generation AI learns data such as the target character's statements, actions, and historical background, and then conducts a dialogue in real time based on that data. The character reproduction unit also reproduces not only the target character's statements and actions, but also their facial expressions and tone of voice, providing a more realistic dialogue experience. For example, Napoleon's words can be reproduced in his tone of voice and displayed along with his facial expressions. This allows the dialogue application according to the embodiment to provide users with an experience of interacting with famous people and historical figures in real time. For example, by interacting with historical figures, users can gain a deeper understanding of their thoughts and historical background. Furthermore, by interacting with scientists and artists, users can learn about their achievements and influences.

[0061] The deployment platform provides a user interface optimized for each device, enabling operability that takes advantage of the characteristics of each device. For example, the deployment platform provides a simple interface that is easy to operate with one hand for smartphones, and a multifunctional interface that takes advantage of the large screen for tablets. For example, the design for smartphones is centered around swipe operations, while the design for tablets is centered around drag-and-drop operations. This makes it possible to provide operability that is optimized for each device.

[0062] The deployment platform realizes seamless data synchronization between devices, allowing users to access the same interaction history from any device. The deployment platform uses, for example, cloud storage to synchronize the user's interaction history and setting information in real time. For example, it allows a user to continue an interaction started on a smartphone on a PC. This makes it possible to realize seamless data synchronization between devices.

[0063] The deployment platform can use the emotion estimation function to automatically suggest the optimal interface according to the user's device usage. For example, the deployment platform analyzes the user's usage status in real time and suggests the optimal interface. For example, if the device is used for a long period of time, it can automatically switch to a dark mode that is easy on the eyes. This makes it possible to provide the optimal interface according to the user's device usage status.

[0064] The deployment platform is also compatible with wearable devices, enabling use on a wider variety of devices. For example, the deployment platform provides simple interaction and notification functions for smartwatches, allowing users to easily start interactions. For example, questions can be asked using voice input and simple answers displayed. This allows support for wearable devices, enabling use on a wider variety of devices.

[0065] The deployment platform provides an offline mode, allowing it to be used even in environments with unstable internet connections. For example, the deployment platform locally stores the interaction history in offline mode and automatically synchronizes it when the internet connection is restored. For example, the interaction history can be continued while using the platform on an airplane and synchronized after landing. This allows it to be used even in environments with unstable internet connections.

[0066] The deployment platform can use its emotion estimation function to suggest device selection according to the user's emotional state and provide an optimal learning experience. For example, the deployment platform can analyze the user's emotional state in real time and suggest the optimal device. For example, it can suggest a tablet when the user is relaxed and a PC when the user needs to concentrate. This makes it possible to suggest device selection according to the user's emotional state and provide an optimal learning experience.

[0067] The dialogue generation unit can automatically present related historical materials and literature based on the dialogue content, thereby deepening the user's understanding. For example, if the user shows interest in a particular statement or event during the dialogue, the dialogue generation unit automatically presents related historical materials and literature. For example, if the user shows interest in a statement made by Napoleon, the dialogue generation unit displays materials on wars and policies related to that statement. This allows the automatic presentation of related historical materials and literature based on the dialogue content, thereby deepening the user's understanding.

[0068] The dialogue generation unit can provide detailed information about a topic in which the user is interested during the dialogue in real time. For example, if the user shows interest in a particular topic during the dialogue, the dialogue generation unit provides detailed information about the topic in real time. For example, if the user says, "I want to know more about this person's life," detailed information about the person's life is displayed. This makes it possible to provide detailed information about a topic in which the user is interested during the dialogue in real time.

[0069] The dialogue generation unit uses the emotion estimation function to generate dialogue content according to the user's emotions, thereby providing a more personalized experience. The dialogue generation unit, for example, analyzes the user's emotional state in real time and generates dialogue content according to the emotions. For example, if the user is excited, it provides an interesting episode. This allows dialogue content to be generated according to the user's emotions, providing a more personalized experience.

[0070] The dialogue generation unit supports voice input and voice output of dialogue, making it possible to provide an interface that is easy to use for people with visual or hearing impairments. The dialogue generation unit, for example, supports voice input and voice output of dialogue, allowing people with visual impairments to engage in dialogue by voice. For example, it uses voice recognition technology to convert a user's question into text, and AI responds by voice. This makes it possible to provide an interface that is easy to use for people with visual or hearing impairments.

[0071] The dialogue generation unit automatically translates the dialogue content, allowing users who speak different languages ​​to converse with the same person. The dialogue generation unit, for example, automatically translates the dialogue content in real time, allowing users who speak different languages ​​to converse with the same person. For example, a question asked in Japanese can be translated into English, and an answer can be obtained in English. This allows users who speak different languages ​​to converse with the same person.

[0072] The dialogue generation unit uses the emotion estimation function to suggest a dialogue progression based on the user's emotion, thereby providing a more natural dialogue experience. The dialogue generation unit, for example, uses the emotion estimation function to suggest a dialogue progression based on the user's emotion. For example, if the user is excited, an interesting episode is provided. This makes it possible to suggest a dialogue progression based on the user's emotion and provide a more natural dialogue experience.

[0073] The character reproduction unit reproduces not only the target person's words and actions, but also their facial expressions and tone of voice, providing a more realistic dialogue experience. For example, the character reproduction unit combines speech synthesis technology and facial recognition technology to reproduce the target person's words and actions, as well as their facial expressions and tone of voice. For example, Napoleon's words are reproduced in his tone of voice and his facial expressions are also displayed. This allows the target person's words and actions, as well as their facial expressions and tone of voice, to be reproduced, providing a more realistic dialogue experience.

[0074] The character reenactment unit generates dialogue content that takes into account the historical and cultural background of the target character, thereby promoting deeper understanding for the user. For example, the character reenactment unit uses historical materials and literature as training data to generate dialogue content that takes into account the historical and cultural background of the target character. For example, the character reenactment unit generates dialogue content that takes into account the historical background of Shakespeare. This allows for the generation of dialogue content that takes into account the historical and cultural background of the target character, promoting deeper understanding for the user.

[0075] The person reproduction unit generates a 3D avatar of the target person, thereby providing a visually realistic conversation experience. The person reproduction unit generates, for example, a 3D avatar of the target person, thereby providing a visually realistic conversation experience. For example, it generates a 3D avatar of Napoleon and reproduces his words and actions in real time. This allows the generation of a 3D avatar of the target person, thereby providing a visually realistic conversation experience.

[0076] The person reproducing unit can integrate multiple data sources to reproduce a target person, thereby realizing a more multifaceted reproduction. For example, the person reproducing unit can integrate multiple data sources, such as books, videos, and audio, to reproduce a target person, thereby realizing a more multifaceted reproduction. For example, to reproduce Napoleon's words and actions, books, videos, and audio data related to him are integrated. This allows the integration of multiple data sources to realize a more multifaceted reproduction.

[0077] The person reproduction unit uses the emotion estimation function to generate facial expressions and gestures of a person based on the user's emotion, thereby providing a more interactive experience. The person reproduction unit, for example, uses the emotion estimation function to generate facial expressions and gestures of a person based on the user's emotion. For example, if the user is excited, the target person also shows excited facial expressions and gestures. This allows the generation of facial expressions and gestures of a person based on the user's emotion, thereby providing a more interactive experience.

[0078] The dialogue generation unit can automatically present related academic papers and research materials based on the content of the dialogue to support the user's learning. For example, if the user shows interest in a particular topic during the dialogue, the dialogue generation unit automatically presents related academic papers and research materials. For example, if the user says, "I want to know more about this theory," academic papers related to that theory are displayed. In this way, related academic papers and research materials can be automatically presented based on the content of the dialogue to support the user's learning.

[0079] The dialogue generation unit can provide answers from multiple perspectives to a user's question, thereby promoting deeper understanding. The dialogue generation unit can provide answers from multiple perspectives to a user's question. For example, if a user is asked about a historical event, the dialogue generation unit can provide explanations of the event from different perspectives. This allows the user to provide answers from multiple perspectives to a user's question, thereby promoting deeper understanding.

[0080] The dialogue generation unit can use the emotion estimation function to suggest learning content that matches the user's interests and concerns. The dialogue generation unit, for example, uses the emotion estimation function to suggest learning content that matches the user's interests and concerns. For example, if the user is excited, the dialogue generation unit provides learning content that piques the user's interest. This makes it possible to suggest learning content that matches the user's interests and concerns.

[0081] The dialogue generation unit can automatically evaluate the user's learning progress based on the dialogue content and suggest what content should be learned next. The dialogue generation unit can automatically evaluate the user's learning progress based on the dialogue content and suggest what content should be learned next. For example, if the user has a sufficient understanding of a particular topic, the dialogue generation unit can suggest the next topic. In this way, the user's learning progress can be automatically evaluated and what content should be learned next can be suggested.

[0082] The dialogue generation unit can share the dialogue content with other users and provide opportunities for collaborative learning. The dialogue generation unit, for example, shares the dialogue content with other users and provides opportunities for collaborative learning. For example, users who are interested in the same topic share the dialogue content and exchange opinions. This allows the dialogue content to be shared with other users and provides opportunities for collaborative learning.

[0083] The dialogue generation unit uses the emotion estimation function to suggest a learning method based on the user's emotion, thereby enabling more effective learning. The dialogue generation unit, for example, uses the emotion estimation function to suggest a learning method based on the user's emotion. For example, if the user is excited, the dialogue generation unit suggests an interesting learning method. In this way, a learning method based on the user's emotion is suggested, enabling more effective learning.

[0084] The dialogue generation unit can automatically generate interactive quizzes and tests based on the dialogue content to check the user's level of understanding. The dialogue generation unit can, for example, automatically generate interactive quizzes based on the dialogue content to check the user's level of understanding. For example, important points that came up during the dialogue can be presented in the form of a quiz. In this way, interactive quizzes and tests can be automatically generated based on the dialogue content to check the user's level of understanding.

[0085] The dialogue generation unit can analyze the dialogue history of the user and propose an individual study plan. The dialogue generation unit, for example, analyzes the dialogue history of the user and proposes an individual study plan. For example, it suggests what content the user should learn next based on a topic in which the user is interested. In this way, it is possible to analyze the dialogue history of the user and propose an individual study plan.

[0086] The dialogue generation unit can use the emotion estimation function to adjust the learning pace and content according to the user's emotions. The dialogue generation unit, for example, uses the emotion estimation function to adjust the learning pace and content according to the user's emotions. For example, if the user is excited, the dialogue generation unit can speed up the learning pace and provide interesting content. This makes it possible to adjust the learning pace and content according to the user's emotions.

[0087] The dialogue generation unit can suggest related events or seminars that the user may be interested in based on the dialogue content. The dialogue generation unit, for example, suggests related events or seminars that the user may be interested in based on the dialogue content. For example, if the user shows interest in a particular topic, events or seminars related to that topic are suggested. In this way, related events or seminars that the user may be interested in can be suggested.

[0088] The dialogue generation unit can link the dialogue content with other learning applications to provide a comprehensive learning experience. The dialogue generation unit, for example, can link the dialogue content with other learning applications to provide a comprehensive learning experience. For example, the content learned during the dialogue can be reflected in other learning applications to advance learning. In this way, the dialogue content can be linked with other learning applications to provide a comprehensive learning experience.

[0089] The dialogue generation unit can use the emotion estimation function to automatically adjust the learning environment (music, background color, etc.) based on the user's emotion. The dialogue generation unit, for example, uses the emotion estimation function to automatically adjust the learning environment based on the user's emotion. For example, if the user is relaxed, relaxing music and background color are provided. This makes it possible to automatically adjust the learning environment based on the user's emotion.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The dialogue generation unit can automatically present relevant academic papers and research materials based on the content of the user's dialogue. For example, if a user asks a question about a particular historical event, academic papers and research materials related to that event will be displayed. This allows the user to gain deeper knowledge through dialogue. The dialogue generation unit can also provide the latest research results on topics that interest the user. For example, if a user expresses interest in a particular theory during a dialogue with a scientist, the latest research results on that theory will be displayed. Furthermore, the dialogue generation unit can provide answers from multiple perspectives to questions raised by the user during the dialogue. For example, it can provide explanations of historical events from different perspectives to deepen the user's understanding.

[0092] The dialogue generation unit can automatically generate interactive quizzes and tests based on the content of the user's dialogue to check the user's level of understanding. For example, it can present important points that came up during the dialogue in the form of a quiz to check how much the user understands. This allows the user to review what they learned through the dialogue and deepen their understanding. The dialogue generation unit can also suggest what content to study next based on the user's answers. For example, if the user has a sufficient understanding of a particular topic, it will suggest the next topic. Furthermore, the dialogue generation unit can automatically evaluate the user's learning progress and suggest an individual learning plan. This allows the user to study at their own pace.

[0093] The dialogue generation unit can use the emotion estimation function to generate dialogue content based on the user's emotions, providing a more personalized experience. For example, if the user is excited, it can provide an interesting anecdote, and if the user is relaxed, it can provide calm dialogue content. This allows the user to enjoy a dialogue that suits their emotions. The dialogue generation unit can also adjust the progress of the dialogue based on the user's emotions. For example, if the user is tired, it can slow down the pace of the dialogue and provide relaxing content. Furthermore, the dialogue generation unit can suggest learning content based on the user's emotions. For example, if the user is excited, it can provide interesting learning content.

[0094] The dialogue generation unit can share the content of the dialogue with other users, providing opportunities for collaborative learning. For example, users who are interested in the same topic can share the content of the dialogue and exchange opinions. This allows users to cooperate with other users in learning. The dialogue generation unit can also collect and provide answers from other users to questions expressed by the user during the dialogue. For example, it can collect other users' opinions on a particular historical event and provide them to the user. Furthermore, the dialogue generation unit can suggest related online communities and forums based on the interests expressed by the user during the dialogue. This allows users to share the knowledge they have gained through the dialogue with other users and further deepen their understanding.

[0095] The dialogue generation unit can use the emotion estimation function to automatically adjust the learning environment (music, background color, etc.) based on the user's emotions. For example, if the user is relaxed, it provides relaxing music and background colors, and if the user is concentrating, it provides music and background colors that enhance concentration. This allows the user to study in an optimal learning environment that suits their emotions. The dialogue generation unit can also adjust the learning pace and content based on the user's emotions. For example, if the user is excited, it can speed up the learning pace and provide interesting content. Furthermore, the dialogue generation unit can suggest learning methods based on the user's emotions. For example, if the user is relaxed, it can suggest a relaxing learning method.

[0096] The dialogue generation unit can suggest related events or seminars that may interest the user based on the content of the dialogue. For example, if the user shows interest in a particular topic, the dialogue generation unit can suggest events or seminars related to that topic. This allows the user to further deepen the knowledge they have gained through the dialogue. The dialogue generation unit can also suggest related online courses or workshops based on the interests the user has expressed during the dialogue. For example, if the user has expressed interest in a particular historical event, the dialogue generation unit can suggest online courses related to that event. The dialogue generation unit can also suggest related books or materials based on the interests the user has expressed during the dialogue. This allows the user to further deepen the knowledge they have gained through the dialogue.

[0097] The dialogue generation unit can use the emotion estimation function to suggest a learning method based on the user's emotions, thereby achieving more effective learning. For example, if the user is excited, an interesting learning method is suggested, and if the user is relaxed, a calm learning method is suggested. This allows the user to proceed with learning using the optimal learning method according to their emotions. The dialogue generation unit can also adjust the learning pace based on the user's emotions. For example, if the user is concentrating, the learning pace is increased, and if the user is relaxed, the learning pace is decreased. Furthermore, the dialogue generation unit can adjust the learning content based on the user's emotions. For example, if the user is excited, interesting content is provided.

[0098] The dialogue generation unit can automatically evaluate the user's learning progress based on the dialogue content and suggest the next content to study. For example, if the user has a sufficient understanding of a particular topic, the next topic can be suggested. This allows the user to proceed with their learning at their own pace. The dialogue generation unit can also analyze the user's dialogue history and suggest an individual learning plan. For example, the next content to study can be suggested based on topics that interest the user. Furthermore, the dialogue generation unit can suggest related learning content based on the user's learning progress. For example, if the user is learning about a particular historical event, learning content related to that event can be suggested. This allows the user to proceed with their learning at their own pace.

[0099] The dialogue generation unit can use the emotion estimation function to adjust the learning pace and content according to the user's emotions. For example, if the user is excited, the dialogue generation unit can speed up the learning pace and provide interesting content, and if the user is relaxed, the dialogue generation unit can slow down the learning pace and provide calm content. This allows the user to study at an optimal learning pace according to their emotions. The dialogue generation unit can also adjust the learning environment based on the user's emotions. For example, if the user is concentrating, the dialogue generation unit can provide music and background colors that enhance concentration, and if the user is relaxed, the dialogue generation unit can provide relaxing music and background colors. Furthermore, the dialogue generation unit can suggest learning methods based on the user's emotions. For example, if the user is excited, the dialogue generation unit can suggest an interesting learning method.

[0100] The dialogue generation unit can link the dialogue content with other learning applications to provide a comprehensive learning experience. For example, the content learned during the dialogue can be reflected in other learning applications to advance learning. This allows the user to further deepen the knowledge gained through the dialogue using other learning applications. The dialogue generation unit can also suggest related learning applications based on the interests the user expressed during the dialogue. For example, if the user expressed interest in a particular topic, the dialogue generation unit can suggest learning applications related to that topic. Furthermore, the dialogue generation unit can share the user's learning progress with other learning applications and suggest a comprehensive learning plan. This allows the user to further deepen the knowledge gained through the dialogue using other learning applications.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The deployment platform provides an interface appropriate for the user's device. For example, it provides a simple interface that is easy to operate with one hand for smartphones, and a multifunctional interface that takes advantage of the large screen for tablets. The deployment platform also enables seamless data synchronization between devices, allowing users to access the same interaction history from any device. For example, it uses cloud storage to synchronize the user's interaction history and settings information in real time. Step 2: The dialogue generation unit generates dialogue content based on the user's input. For example, the generation AI receives a prompt containing the name of the person the user wants to talk to and the question, and generates dialogue content based on that prompt. The generation AI also generates dialogue content based on the user's emotions, providing a more personalized experience. For example, if the user is excited, it will provide an interesting anecdote. Step 3: The character reproduction unit reproduces the target person. For example, the generative AI learns data such as the target person's words, actions, and historical background, and then conducts a real-time dialogue based on that data. The character reproduction unit also reproduces not only the target person's words and actions, but also their facial expressions and tone of voice, providing a more realistic dialogue experience. For example, Napoleon's words are reproduced in his tone of voice, and facial expressions are also displayed.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0129] 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.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0147] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 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 deployment platform that provides an interface tailored to the user's device; a dialogue generation unit that generates dialogue content based on a user input; a person reproducing unit that reproduces a target person; A system characterized by:

2. The deployment platform includes: Providing a user interface optimized for each device, realizing operability that takes advantage of the characteristics of each device 2. The system of claim 1.

3. The dialogue generation unit Automatically presents relevant historical documents and literature based on the content of the conversation to deepen the user's understanding 2. The system of claim 1.

4. The person reproducing unit It reproduces not only the target person's words and actions, but also their facial expressions and tone of voice, providing a more realistic conversation experience.

2. The system of claim 1.

5. The dialogue generation unit Automatically generate interactive quizzes and tests based on the dialogue to assess user comprehension 2. The system of claim 1.

6. The deployment platform includes: Using emotion estimation, we automatically suggest the optimal interface based on the user's device usage.

2. The system of claim 1.

7. The dialogue generation unit Emotion estimation functionality is used to generate dialogue content based on the user's emotions, providing a more personalized experience.

2. The system of claim 1.

8. The person reproducing unit Using emotion estimation functionality, the system generates human reactions based on the user's emotions, enabling more empathetic dialogue.

2. The system of claim 1.

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