system

Abstract

The system according to this embodiment aims to allow users to interact with historical figures in real time and deepen their knowledge. [Solution] The system according to this embodiment comprises a selection unit, a reception unit, a generation unit, and a provision unit. The selection unit selects a historical figure that the user wishes to interact with. The reception unit receives the user's question. The generation unit dynamically generates a response based on the question received by the reception unit. The provision unit provides the response generated by the generation unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that a user cannot obtain in real time an experience of interacting with a historical figure, and the opportunity to deepen knowledge is limited.

[0005] The system according to the embodiment aims to enable a user to interact with a historical figure in real time and deepen knowledge.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a selection unit, a reception unit, a generation unit, and a provision unit. The selection unit selects a historical figure that the user wishes to interact with. The reception unit receives the user's question. The generation unit dynamically generates a response based on the question received by the reception unit. The provision unit provides the response generated by the generation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment allows users to interact with historical figures in real time and deepen their knowledge. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0028] (Example of form 1) An AI application according to an embodiment of the present invention is a system that provides a user with the experience of interacting in real time with famous historical figures and other historical figures in a chat format. This system recreates each figure using an advanced AI based on deep learning, allowing the user to deepen their knowledge of their way of thinking, statements, and historical context. Specifically, first, the user selects a historical figure they wish to interact with and enters a question. Next, the AI ​​dynamically generates a response based on the question and provides it to the user. This response is based on historical context and can provide an educational dialogue. This allows the user to deepen their historical knowledge. For example, the user selects a historical figure they wish to interact with. For example, they can select a famous historical figure. This selection is made through the application interface. Next, the user enters a question for the selected figure. For example, they might enter a specific question such as, "Why did you launch the Russian campaign?" This question is entered into the AI. The AI ​​dynamically generates a response based on the entered question. By utilizing deep learning, it recreates the personality and knowledge of the selected figure and generates a response based on historical context. For example, it might generate a response such as, "The reason for the Russian campaign was part of a strategy to dominate all of Europe." The generated response is provided to the user. Through AI-generated responses, users can deepen their knowledge of the thoughts, statements, and historical context of a selected person. This allows users to deepen their historical knowledge. This AI application is expected to be used in the education sector, contributing to the dissemination and deepening of historical knowledge and improving user engagement. It is also expected to be introduced in educational institutions, museums, and cultural centers, enabling improved access to and engagement in history education. In this way, the AI ​​application can provide users with an experience of interacting with famous figures and historical figures of the past in real time.

[0029] The AI ​​application according to this embodiment comprises a selection unit, a reception unit, a generation unit, and a provision unit. The selection unit allows the user to select a historical figure with whom they wish to interact. The selection unit allows the user to select a famous historical figure through the application's interface, for example. The selection unit provides a function for the user to select a figure with whom they wish to interact from a list. The selection unit allows the user to select a figure from a specific era or region, for example. The selection unit can also provide a function for the user to search for a figure of interest. The reception unit receives questions from the user. The reception unit allows the user to input specific questions about the figure they have selected, for example. The reception unit provides a function for sending the questions entered by the user to the AI. The reception unit allows the user to input questions such as, for example, "Why did they launch the Russian campaign?" The reception unit can also provide a function for the user to input questions using voice input. The generation unit dynamically generates responses based on the questions received by the reception unit. The generation unit, for example, utilizes deep learning to reproduce the personality and knowledge of the selected figure and generates responses based on the historical context. The generation unit provides a function for the AI ​​to generate responses based on the input questions. The generation unit generates responses such as, for example, "The reason for the Russian campaign was part of a strategy to dominate all of Europe." The generation unit can also provide the function of reproducing the statements and actions of a selected person using AI. The delivery unit provides the responses generated by the generation unit to the user. The delivery unit provides, for example, the function of displaying the generated responses on the user's screen. The delivery unit provides the function of allowing the user to view the generated responses. The delivery unit can also provide, for example, the function of playing the generated responses as audio. The delivery unit can also provide the function of notifying the user of the generated responses. As a result, the AI ​​application according to this embodiment can provide the user with an experience in which they can interact in real time with famous people and historical figures from the past.

[0030] The selection section allows users to choose historical figures they wish to interact with. For example, the selection section allows users to select prominent historical figures through the application's interface. Specifically, the selection section provides a function for users to select a figure they wish to interact with from a list. The list includes figures from a wide range of eras and regions, from ancient times to the present, allowing users to freely choose according to their interests. For example, users can choose from various figures such as ancient Egyptian pharaohs, medieval European kings, and modern scientists and politicians. The selection section also provides a filtering function to allow users to select figures from specific eras or regions. For example, if a user selects "Renaissance Italian figures," only figures related to that era and region will be displayed. Furthermore, the selection section can also provide a search function for figures of interest to the user. Users can search for specific figures by entering names or keywords and quickly select them. In addition, the selection section includes a function to recommend relevant figures based on the user's past selection history and interests. This allows users to enjoy new discoveries and gain a richer conversational experience. The selection section also prioritizes user-friendliness, designed for easy and intuitive selection of figures. This allows the selection unit to quickly and accurately select the historical figure the user wants to interact with, thereby improving the application's user experience.

[0031] The reception desk receives user questions. For example, the reception desk can input specific questions for a person selected by the user. Specifically, the reception desk provides a function to send user-entered questions to the AI. Users can submit questions by entering them in the application's text input field and pressing the submit button. For example, a user could enter a question such as, "Why did they undertake the Russian campaign?" The reception desk can also provide a function for users to input questions using voice input. Using the voice input function, users can input questions simply by speaking into the microphone. The voice input is converted into text using speech recognition technology and sent to the AI. This makes it easy for users to input questions and improves convenience. Furthermore, the reception desk also has a function to appropriately categorize user questions so that the AI ​​can process them efficiently. For example, questions can be categorized into different categories depending on their content, such as questions about historical events or questions about a person's personality. This allows the AI ​​to accurately understand the intent of the question and generate an appropriate response. By quickly and accurately receiving user questions and sending them to the AI, the reception desk can provide a smooth conversational experience.

[0032] The generation unit dynamically generates responses based on questions received by the reception unit. For example, the generation unit utilizes deep learning to recreate the personality and knowledge of a selected person and generate responses based on historical context. Specifically, the generation unit provides the function of AI generating responses based on input questions. The AI ​​refers to a vast database of the selected person, considering their statements, actions, and historical background when generating responses. For example, it might generate a response such as, "The reason for the Russian campaign was part of a strategy to dominate all of Europe." The generation unit can also provide the function of AI recreating the statements and actions of the selected person. This allows the user to experience a conversation as if they were directly interacting with that person. The generation unit employs natural language processing techniques to generate appropriate and natural responses to user questions. Furthermore, the generation unit has the ability to evaluate the quality of the generated responses and correct them as needed. For example, if a response is inappropriate or misleading, the generation unit automatically corrects the response to ensure quality before providing it to the user. This allows the generation unit to provide users with high-quality responses and improve the conversational experience. The generation unit utilizes AI technology to quickly and accurately generate responses to user questions, enabling conversations with historical figures.

[0033] The provider unit provides the user with the response generated by the generator unit. For example, the provider unit provides a function to display the generated response on the user's screen. Specifically, the provider unit provides a function that allows the user to view the generated response. The user can check the response on the application interface and enter the next question. The provider unit can also provide a function to play the generated response as audio. Using speech synthesis technology, the generated response is played back in a natural voice and provided to the user. This allows the user to check the response not only visually but also aurally, resulting in a more immersive dialogue experience. The provider unit can also provide a function to notify the user of the generated response. For example, it can send a push notification when a response is generated to inform the user. This allows the user to know immediately that a response has been generated, enabling smoother dialogue. Furthermore, the provider unit also has a function to collect user feedback and provide data to continuously improve the response quality of the generator unit. Users can provide evaluations and comments on the responses, which the generator unit can use to improve the accuracy and naturalness of the responses. This allows the provider unit to provide users with high-quality responses and improve the dialogue experience. The service provider delivers the generated responses to the user quickly and appropriately, enabling interaction with historical figures.

[0034] The generation unit can utilize deep learning to reproduce the personality and knowledge of a selected person and generate responses based on historical context. For example, the generation unit uses a neural network to reproduce the personality and knowledge of a selected person. The generation unit learns from a large amount of historical data to reproduce the speeches and actions of a selected person. For example, the generation unit analyzes the letters and speeches of a selected person to learn their speech patterns. The generation unit learns from historical events and background information to reproduce the knowledge of a selected person. For example, the generation unit learns the social and political background of the era in which the selected person lived. The generation unit analyzes the actions and decisions of a selected person to reproduce their personality. For example, the generation unit learns from the strategies and decision-making patterns of a selected person. As a result, by utilizing deep learning, the generation unit can reproduce the personality and knowledge of a selected person and generate responses based on historical context. Some or all of the above processing in the generation unit may be performed using, for example, generative AI, or not using generative AI. For example, the generation unit can input a large amount of historical data into the generation AI to reproduce the personality and knowledge of a selected person, and the generation AI can then generate a response.

[0035] The service provider can provide the generated response to the user. The service provider can, for example, display the generated response on the user's screen. The service provider can also provide a function to play the generated response as audio. The service provider can, for example, notify the user of the generated response. The service provider can also provide a function to send the generated response to the user's email address. The service provider can, for example, send a push notification to the user's smartphone of the generated response. By providing the user with the generated response, the user can deepen their knowledge through dialogue. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use AI to optimize how the generated response is displayed in order to display it on the user's screen.

[0036] The selection unit can analyze the user's past selection history and recommend the most suitable person. For example, the selection unit can analyze the tendencies of people the user has selected in the past and recommend people with similar interests. The selection unit can also recommend people related to the era and region of people the user has interacted with in the past. For example, the selection unit can recommend people related to the themes of people the user has interacted with in the past. In this way, the selection unit can recommend the most suitable person by analyzing the user's past selection history. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input the user's past selection history into AI, and the AI ​​can recommend the most suitable person.

[0037] The selection unit can filter the results based on the user's current areas of interest and learning goals. For example, the selection unit may prioritize presenting individuals related to the history topic the user is currently studying. The selection unit can also present individuals related to specific fields of interest the user (e.g., science, politics, art). The selection unit may filter and present relevant individuals based on the user's learning goals. This allows for the selection of more appropriate individuals by filtering based on the user's current areas of interest and learning goals. Some or all of the above processing in the selection unit may be performed using AI, or not. For example, the selection unit can input the user's areas of interest and learning goals into an AI, which can then filter and present relevant individuals.

[0038] The selection unit can prioritize presenting highly relevant individuals by considering the user's geographical location information during the selection process. For example, the selection unit can prioritize presenting historical figures related to the region the user is currently in. If the user is traveling, the selection unit can also present figures related to the history of that region. For example, the selection unit can present figures related to the historical background of a region based on the user's geographical location information. This allows the selection unit to prioritize presenting highly relevant individuals by considering the user's geographical location information. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection unit can input the user's geographical location information into a generative AI, which can then present highly relevant individuals.

[0039] The selection unit can analyze the user's social media activity and recommend relevant individuals during the selection process. For example, the selection unit can recommend individuals based on history-related accounts that the user follows on social media. The selection unit can also recommend individuals based on historical articles or posts that the user has shared on social media. For example, the selection unit can analyze the user's social media activity history and recommend individuals that might be of interest to them. In this way, relevant individuals can be recommended by analyzing the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's social media activity into AI, which can then recommend relevant individuals.

[0040] The reception desk can analyze the user's past question history to select the optimal reception method when a question is received. For example, the reception desk may prioritize suggesting question formats that the user has frequently used in the past. The reception desk can also automatically complete relevant questions from the user's past question history. For example, the reception desk analyzes the user's past question history and suggests the optimal question format. This allows the reception desk to select the optimal question reception method by analyzing the user's past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past question history into AI, and the AI ​​can select the optimal question reception method.

[0041] The reception unit can filter questions based on the user's current learning status and areas of interest. For example, the reception unit prioritizes questions related to the topic the user is currently studying. The reception unit can also filter relevant questions based on the user's areas of interest. For example, the reception unit can suggest an appropriate question format according to the user's learning status. This allows for more appropriate question reception by filtering based on the user's current learning status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's learning status and areas of interest into an AI, which can then filter and accept relevant questions.

[0042] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location. For example, the reception desk can prioritize receiving historical questions related to the area the user is currently in. If the user is traveling, the reception desk can also prioritize receiving questions related to the history of that area. For example, the reception desk can prioritize receiving questions related to the historical background of a region based on the user's geographical location. This allows the reception desk to prioritize receiving highly relevant questions by taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location into AI, which can then prioritize receiving highly relevant questions.

[0043] The reception desk can analyze the user's social media activity when a question is received and accept relevant questions. For example, the reception desk can accept questions based on history-related accounts that the user follows on social media. The reception desk can also accept questions based on historical articles or posts that the user has shared on social media. For example, the reception desk can analyze the user's social media activity history and accept relevant questions. This allows the reception desk to accept relevant questions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity into AI, and the AI ​​can accept relevant questions.

[0044] The generation unit can adjust the level of detail in the response based on the importance of the question when generating the response. For example, the generation unit generates a detailed response for important questions. The generation unit can also generate a concise response for general questions. The generation unit adjusts the level of detail in the response according to the user's level of interest, for example. This allows for the provision of more appropriate responses by adjusting the level of detail in the response based on the importance of the question. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the question to the AI, and the AI ​​can adjust the level of detail in the response.

[0045] The generation unit can apply different response algorithms depending on the category of the question when generating a response. For example, for a historical question, the generation unit can apply a response algorithm based on the historical context. For a scientific question, the generation unit can also apply a response algorithm based on scientific knowledge. For example, for a philosophical question, the generation unit can apply a response algorithm based on philosophical thinking. By applying different response algorithms depending on the category of the question, a more appropriate response can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category of the question into the AI, and the AI ​​can apply an appropriate response algorithm.

[0046] The generation unit can determine the priority of responses based on when the question was submitted when generating responses. For example, the generation unit will prioritize responses to recently submitted questions. The generation unit can also prioritize responses to questions related to important events or anniversaries. The generation unit will adjust the priority of responses according to the user's level of interest, for example. This allows for the provision of more appropriate responses by determining the priority of responses based on when the question was submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the question submission date to the AI, and the AI ​​can determine the priority of responses.

[0047] The generation unit can adjust the order of responses based on the relevance of the questions when generating responses. For example, the generation unit will prioritize generating responses when the questions are highly relevant. The generation unit can also postpone generating responses when the questions are less relevant. The generation unit will adjust the order of responses according to the user's level of interest, for example. This allows for the provision of more appropriate responses by adjusting the order of responses based on the relevance of the questions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the questions into the AI, and the AI ​​can adjust the order of responses.

[0048] The service provider can select the optimal display method by referring to the user's past dialogue history when providing a response. For example, the service provider may prioritize displaying methods that the user has previously preferred. The service provider can also display relevant information from the user's past dialogue history. For example, the service provider may analyze the user's past dialogue history and propose the optimal display method. This allows the service provider to select the optimal display method by referring to the user's past dialogue history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's past dialogue history into AI, which can then select the optimal display method.

[0049] The service provider can adjust the display method based on the user's current learning status and areas of interest when providing a response. For example, the service provider may prioritize displaying information related to the topic the user is currently studying. The service provider can also display relevant information based on the user's areas of interest. For example, the service provider may suggest an appropriate display method according to the user's learning status. This allows for a more appropriate display by adjusting the display method based on the user's current learning status and areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's learning status and areas of interest into the AI, which can then display relevant information.

[0050] The service provider can select the optimal display method when providing a response, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. If the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to select the optimal display method by taking into account the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into the AI, and the AI ​​can select the optimal display method.

[0051] The service provider can provide multilingual displays according to the user's language settings when providing a response. For example, the service provider can automatically set the display language based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. For example, if the user selects a specific language, the service provider will provide the display in that language. This enables more appropriate displays by providing multilingual displays according to the user's language settings. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's language settings into AI, and the AI ​​can provide multilingual displays.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The selection unit can analyze the user's past selection history and recommend the most suitable person. For example, it can analyze the tendencies of people the user has selected in the past and recommend people with similar interests. It can also recommend people related to the era and region of people the user has previously interacted with. Furthermore, it can recommend people related to the themes of people the user has previously interacted with. In this way, by analyzing the user's past selection history, the most suitable person can be recommended. Some or all of the above processing in the selection unit may be performed using AI or not.

[0054] The selection unit can filter the results based on the user's current areas of interest and learning goals. For example, it can prioritize presenting individuals related to the history topics the user is currently studying. It can also present individuals related to specific fields the user is interested in (e.g., science, politics, art). Furthermore, it can filter and present relevant individuals based on the user's learning goals. This allows for the selection of more appropriate individuals by filtering based on the user's current areas of interest and learning goals. Some or all of the above processing in the selection unit may be performed using AI or not.

[0055] The selection unit can prioritize presenting highly relevant individuals by considering the user's geographical location during the selection process. For example, it can prioritize presenting historical figures related to the user's current location. If the user is traveling, it can also present figures related to the history of that region. Furthermore, based on the user's geographical location, it can present figures related to the historical background of the region. This allows for the prioritization of highly relevant individuals by considering the user's geographical location. Some or all of the above processing in the selection unit may be performed using generative AI, or it may be performed without using generative AI.

[0056] The selection unit can analyze the user's social media activity during the selection process and recommend relevant individuals. For example, it can recommend individuals based on history-related accounts the user follows on social media. It can also recommend individuals based on historical articles or posts the user has shared on social media. Furthermore, it can analyze the user's social media activity history and recommend individuals who might be of interest. In this way, relevant individuals can be recommended by analyzing the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI or not.

[0057] The reception desk can analyze the user's past question history to select the optimal reception method when a question is received. For example, it can prioritize suggesting question formats that the user has frequently used in the past. It can also automatically complete relevant questions from the user's past question history. Furthermore, it can analyze the user's past question history and suggest the optimal question format. In this way, the optimal question reception method can be selected by analyzing the user's past question history. Some or all of the above processing in the reception desk may be performed using AI, or it may be performed without using AI.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The selection section allows the user to choose a historical figure they wish to interact with. Through the application interface, users can select prominent historical figures from a list. The application also provides features for selecting figures from specific eras or regions, and for searching for figures of interest. Step 2: The reception desk receives the user's question. The user can enter a specific question for a selected person, and the question is sent to the AI. A voice input function is also provided for entering questions. Step 3: The generation unit dynamically generates responses based on the questions received by the reception unit. It utilizes deep learning to recreate the personality and knowledge of the selected person and generates responses based on historical context. Step 4: The providing unit provides the user with the response generated by the generating unit. The generated response is displayed on the user's screen, and functions for audio playback and notification are also provided.

[0060] (Example of form 2) An AI application according to an embodiment of the present invention is a system that provides a user with the experience of interacting in real time with famous historical figures and other historical figures in a chat format. This system recreates each figure using an advanced AI based on deep learning, allowing the user to deepen their knowledge of their way of thinking, statements, and historical context. Specifically, first, the user selects a historical figure they wish to interact with and enters a question. Next, the AI ​​dynamically generates a response based on the question and provides it to the user. This response is based on historical context and can provide an educational dialogue. This allows the user to deepen their historical knowledge. For example, the user selects a historical figure they wish to interact with. For example, they can select a famous historical figure. This selection is made through the application interface. Next, the user enters a question for the selected figure. For example, they might enter a specific question such as, "Why did you launch the Russian campaign?" This question is entered into the AI. The AI ​​dynamically generates a response based on the entered question. By utilizing deep learning, it recreates the personality and knowledge of the selected figure and generates a response based on historical context. For example, it might generate a response such as, "The reason for the Russian campaign was part of a strategy to dominate all of Europe." The generated response is provided to the user. Through AI-generated responses, users can deepen their knowledge of the thoughts, statements, and historical context of a selected person. This allows users to deepen their historical knowledge. This AI application is expected to be used in the education sector, contributing to the dissemination and deepening of historical knowledge and improving user engagement. It is also expected to be introduced in educational institutions, museums, and cultural centers, enabling improved access to and engagement in history education. In this way, the AI ​​application can provide users with an experience of interacting with famous figures and historical figures of the past in real time.

[0061] The AI ​​application according to this embodiment comprises a selection unit, a reception unit, a generation unit, and a provision unit. The selection unit allows the user to select a historical figure with whom they wish to interact. The selection unit allows the user to select a famous historical figure through the application's interface, for example. The selection unit provides a function for the user to select a figure with whom they wish to interact from a list. The selection unit allows the user to select a figure from a specific era or region, for example. The selection unit can also provide a function for the user to search for a figure of interest. The reception unit receives questions from the user. The reception unit allows the user to input specific questions about the figure they have selected, for example. The reception unit provides a function for sending the questions entered by the user to the AI. The reception unit allows the user to input questions such as, for example, "Why did they launch the Russian campaign?" The reception unit can also provide a function for the user to input questions using voice input. The generation unit dynamically generates responses based on the questions received by the reception unit. The generation unit, for example, utilizes deep learning to reproduce the personality and knowledge of the selected figure and generates responses based on the historical context. The generation unit provides a function for the AI ​​to generate responses based on the input questions. The generation unit generates responses such as, for example, "The reason for the Russian campaign was part of a strategy to dominate all of Europe." The generation unit can also provide the function of reproducing the statements and actions of a selected person using AI. The delivery unit provides the responses generated by the generation unit to the user. The delivery unit provides, for example, the function of displaying the generated responses on the user's screen. The delivery unit provides the function of allowing the user to view the generated responses. The delivery unit can also provide, for example, the function of playing the generated responses as audio. The delivery unit can also provide the function of notifying the user of the generated responses. As a result, the AI ​​application according to this embodiment can provide the user with an experience in which they can interact in real time with famous people and historical figures from the past.

[0062] The selection section allows users to choose historical figures they wish to interact with. For example, the selection section allows users to select prominent historical figures through the application's interface. Specifically, the selection section provides a function for users to select a figure they wish to interact with from a list. The list includes figures from a wide range of eras and regions, from ancient times to the present, allowing users to freely choose according to their interests. For example, users can choose from various figures such as ancient Egyptian pharaohs, medieval European kings, and modern scientists and politicians. The selection section also provides a filtering function to allow users to select figures from specific eras or regions. For example, if a user selects "Renaissance Italian figures," only figures related to that era and region will be displayed. Furthermore, the selection section can also provide a search function for figures of interest to the user. Users can search for specific figures by entering names or keywords and quickly select them. In addition, the selection section includes a function to recommend relevant figures based on the user's past selection history and interests. This allows users to enjoy new discoveries and gain a richer conversational experience. The selection section also prioritizes user-friendliness, designed for easy and intuitive selection of figures. This allows the selection unit to quickly and accurately select the historical figure the user wants to interact with, thereby improving the application's user experience.

[0063] The reception desk receives user questions. For example, the reception desk can input specific questions for a person selected by the user. Specifically, the reception desk provides a function to send user-entered questions to the AI. Users can submit questions by entering them in the application's text input field and pressing the submit button. For example, a user could enter a question such as, "Why did they undertake the Russian campaign?" The reception desk can also provide a function for users to input questions using voice input. Using the voice input function, users can input questions simply by speaking into the microphone. The voice input is converted into text using speech recognition technology and sent to the AI. This makes it easy for users to input questions and improves convenience. Furthermore, the reception desk also has a function to appropriately categorize user questions so that the AI ​​can process them efficiently. For example, questions can be categorized into different categories depending on their content, such as questions about historical events or questions about a person's personality. This allows the AI ​​to accurately understand the intent of the question and generate an appropriate response. By quickly and accurately receiving user questions and sending them to the AI, the reception desk can provide a smooth conversational experience.

[0064] The generation unit dynamically generates responses based on questions received by the reception unit. For example, the generation unit utilizes deep learning to recreate the personality and knowledge of a selected person and generate responses based on historical context. Specifically, the generation unit provides the function of AI generating responses based on input questions. The AI ​​refers to a vast database of the selected person, considering their statements, actions, and historical background when generating responses. For example, it might generate a response such as, "The reason for the Russian campaign was part of a strategy to dominate all of Europe." The generation unit can also provide the function of AI recreating the statements and actions of the selected person. This allows the user to experience a conversation as if they were directly interacting with that person. The generation unit employs natural language processing techniques to generate appropriate and natural responses to user questions. Furthermore, the generation unit has the ability to evaluate the quality of the generated responses and correct them as needed. For example, if a response is inappropriate or misleading, the generation unit automatically corrects the response to ensure quality before providing it to the user. This allows the generation unit to provide users with high-quality responses and improve the conversational experience. The generation unit utilizes AI technology to quickly and accurately generate responses to user questions, enabling conversations with historical figures.

[0065] The provider unit provides the user with the response generated by the generator unit. For example, the provider unit provides a function to display the generated response on the user's screen. Specifically, the provider unit provides a function that allows the user to view the generated response. The user can check the response on the application interface and enter the next question. The provider unit can also provide a function to play the generated response as audio. Using speech synthesis technology, the generated response is played back in a natural voice and provided to the user. This allows the user to check the response not only visually but also aurally, resulting in a more immersive dialogue experience. The provider unit can also provide a function to notify the user of the generated response. For example, it can send a push notification when a response is generated to inform the user. This allows the user to know immediately that a response has been generated, enabling smoother dialogue. Furthermore, the provider unit also has a function to collect user feedback and provide data to continuously improve the response quality of the generator unit. Users can provide evaluations and comments on the responses, which the generator unit can use to improve the accuracy and naturalness of the responses. This allows the provider unit to provide users with high-quality responses and improve the dialogue experience. The service provider delivers the generated responses to the user quickly and appropriately, enabling interaction with historical figures.

[0066] The generation unit can utilize deep learning to reproduce the personality and knowledge of a selected person and generate responses based on historical context. For example, the generation unit uses a neural network to reproduce the personality and knowledge of a selected person. The generation unit learns from a large amount of historical data to reproduce the speeches and actions of a selected person. For example, the generation unit analyzes the letters and speeches of a selected person to learn their speech patterns. The generation unit learns from historical events and background information to reproduce the knowledge of a selected person. For example, the generation unit learns the social and political background of the era in which the selected person lived. The generation unit analyzes the actions and decisions of a selected person to reproduce their personality. For example, the generation unit learns from the strategies and decision-making patterns of a selected person. As a result, by utilizing deep learning, the generation unit can reproduce the personality and knowledge of a selected person and generate responses based on historical context. Some or all of the above processing in the generation unit may be performed using, for example, generative AI, or not using generative AI. For example, the generation unit can input a large amount of historical data into the generation AI to reproduce the personality and knowledge of a selected person, and the generation AI can then generate a response.

[0067] The service provider can provide the generated response to the user. The service provider can, for example, display the generated response on the user's screen. The service provider can also provide a function to play the generated response as audio. The service provider can, for example, notify the user of the generated response. The service provider can also provide a function to send the generated response to the user's email address. The service provider can, for example, send a push notification to the user's smartphone of the generated response. By providing the user with the generated response, the user can deepen their knowledge through dialogue. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can use AI to optimize how the generated response is displayed in order to display it on the user's screen.

[0068] The selection unit can estimate the user's emotions and, based on the estimated emotions, present candidates for historical figures with whom the user might want to interact. For example, if the user is excited, the selection unit may prioritize presenting historical figures related to war or adventure. If the user is sad, the selection unit may also prioritize presenting historical figures related to peace or healing. If the user is curious, the selection unit may prioritize presenting historical figures related to science or invention. This allows the user to select a more appropriate conversation partner by presenting candidates for historical figures with whom they might want to interact based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit may use AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0069] The selection unit can analyze the user's past selection history and recommend the most suitable person. For example, the selection unit can analyze the tendencies of people the user has selected in the past and recommend people with similar interests. The selection unit can also recommend people related to the era and region of people the user has interacted with in the past. For example, the selection unit can recommend people related to the themes of people the user has interacted with in the past. In this way, the selection unit can recommend the most suitable person by analyzing the user's past selection history. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit can input the user's past selection history into AI, and the AI ​​can recommend the most suitable person.

[0070] The selection unit can filter the results based on the user's current areas of interest and learning goals. For example, the selection unit may prioritize presenting individuals related to the history topic the user is currently studying. The selection unit can also present individuals related to specific fields of interest the user (e.g., science, politics, art). The selection unit may filter and present relevant individuals based on the user's learning goals. This allows for the selection of more appropriate individuals by filtering based on the user's current areas of interest and learning goals. Some or all of the above processing in the selection unit may be performed using AI, or not. For example, the selection unit can input the user's areas of interest and learning goals into an AI, which can then filter and present relevant individuals.

[0071] The selection unit can estimate the user's emotions and determine the priority of the people to select based on the estimated emotions. For example, if the user is excited, the selection unit may prioritize presenting historical figures related to war or adventure. If the user is sad, the selection unit may also prioritize presenting historical figures related to peace or healing. If the user is curious, the selection unit may prioritize presenting historical figures related to science or invention. This allows for the selection of a more appropriate conversation partner by determining the priority of the people to select based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit may use AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0072] The selection unit can prioritize presenting highly relevant individuals by considering the user's geographical location information during the selection process. For example, the selection unit can prioritize presenting historical figures related to the region the user is currently in. If the user is traveling, the selection unit can also present figures related to the history of that region. For example, the selection unit can present figures related to the historical background of a region based on the user's geographical location information. This allows the selection unit to prioritize presenting highly relevant individuals by considering the user's geographical location information. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection unit can input the user's geographical location information into a generative AI, which can then present highly relevant individuals.

[0073] The selection unit can analyze the user's social media activity and recommend relevant individuals during the selection process. For example, the selection unit can recommend individuals based on history-related accounts that the user follows on social media. The selection unit can also recommend individuals based on historical articles or posts that the user has shared on social media. For example, the selection unit can analyze the user's social media activity history and recommend individuals that might be of interest to them. In this way, relevant individuals can be recommended by analyzing the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit can input the user's social media activity into AI, which can then recommend relevant individuals.

[0074] The reception desk can estimate the user's emotions and adjust the way questions are answered based on the estimated emotions. For example, if the user is nervous, the reception desk can provide a simple and intuitive interface. If the user is relaxed, the reception desk can also provide detailed input options. If the user is in a hurry, the reception desk can prioritize voice input and answer questions quickly. This allows for more appropriate question answering by adjusting the way questions are answered based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can use AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0075] The reception desk can analyze the user's past question history to select the optimal reception method when a question is received. For example, the reception desk may prioritize suggesting question formats that the user has frequently used in the past. The reception desk can also automatically complete relevant questions from the user's past question history. For example, the reception desk analyzes the user's past question history and suggests the optimal question format. This allows the reception desk to select the optimal question reception method by analyzing the user's past question history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past question history into AI, and the AI ​​can select the optimal question reception method.

[0076] The reception unit can filter questions based on the user's current learning status and areas of interest. For example, the reception unit prioritizes questions related to the topic the user is currently studying. The reception unit can also filter relevant questions based on the user's areas of interest. For example, the reception unit can suggest an appropriate question format according to the user's learning status. This allows for more appropriate question reception by filtering based on the user's current learning status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's learning status and areas of interest into an AI, which can then filter and accept relevant questions.

[0077] The reception desk can estimate the user's emotions and determine the priority of questions to accept based on the estimated emotions. For example, if the user is excited, the reception desk may prioritize questions that pique their interest. If the user is sad, the reception desk may also prioritize questions related to comfort or encouragement. If the user is curious, the reception desk may prioritize academic questions. This allows for more appropriate question acceptance by prioritizing questions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may use AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0078] The reception desk can prioritize receiving questions that are highly relevant, taking into account the user's geographical location. For example, the reception desk can prioritize receiving historical questions related to the area the user is currently in. If the user is traveling, the reception desk can also prioritize receiving questions related to the history of that area. For example, the reception desk can prioritize receiving questions related to the historical background of a region based on the user's geographical location. This allows the reception desk to prioritize receiving highly relevant questions by taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location into AI, which can then prioritize receiving highly relevant questions.

[0079] The reception desk can analyze the user's social media activity when a question is received and accept relevant questions. For example, the reception desk can accept questions based on history-related accounts that the user follows on social media. The reception desk can also accept questions based on historical articles or posts that the user has shared on social media. For example, the reception desk can analyze the user's social media activity history and accept relevant questions. This allows the reception desk to accept relevant questions by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity into AI, and the AI ​​can accept relevant questions.

[0080] The generation unit can estimate the user's emotions and adjust the way it expresses its response based on the estimated emotions. For example, if the user is nervous, the generation unit will use simple and easy-to-understand language. If the user is relaxed, the generation unit may also use language that includes detailed explanations. If the user is in a hurry, the generation unit will use concise language that gets straight to the point. This allows for more appropriate responses by adjusting the way the response is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit may use AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0081] The generation unit can adjust the level of detail in the response based on the importance of the question when generating the response. For example, the generation unit generates a detailed response for important questions. The generation unit can also generate a concise response for general questions. The generation unit adjusts the level of detail in the response according to the user's level of interest, for example. This allows for the provision of more appropriate responses by adjusting the level of detail in the response based on the importance of the question. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the importance of the question to the AI, and the AI ​​can adjust the level of detail in the response.

[0082] The generation unit can apply different response algorithms depending on the category of the question when generating a response. For example, for a historical question, the generation unit can apply a response algorithm based on the historical context. For a scientific question, the generation unit can also apply a response algorithm based on scientific knowledge. For example, for a philosophical question, the generation unit can apply a response algorithm based on philosophical thinking. By applying different response algorithms depending on the category of the question, a more appropriate response can be provided. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the category of the question into the AI, and the AI ​​can apply an appropriate response algorithm.

[0083] The generation unit can estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise response. If the user is relaxed, the generation unit can also generate a longer response that includes detailed explanations. If the user is excited, the generation unit can generate a response with visually stimulating effects. This allows for the provision of more appropriate responses by adjusting the length of the response based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can use AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0084] The generation unit can determine the priority of responses based on when the question was submitted when generating responses. For example, the generation unit will prioritize responses to recently submitted questions. The generation unit can also prioritize responses to questions related to important events or anniversaries. The generation unit will adjust the priority of responses according to the user's level of interest, for example. This allows for the provision of more appropriate responses by determining the priority of responses based on when the question was submitted. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the question submission date to the AI, and the AI ​​can determine the priority of responses.

[0085] The generation unit can adjust the order of responses based on the relevance of the questions when generating responses. For example, the generation unit will prioritize generating responses when the questions are highly relevant. The generation unit can also postpone generating responses when the questions are less relevant. The generation unit will adjust the order of responses according to the user's level of interest, for example. This allows for the provision of more appropriate responses by adjusting the order of responses based on the relevance of the questions. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the relevance of the questions into the AI, and the AI ​​can adjust the order of responses.

[0086] The service provider can estimate the user's emotions and adjust the way responses are displayed based on the estimated emotions. For example, if the user is nervous, the service provider may provide a simple and highly visible display method. If the user is relaxed, the service provider may also provide a display method that includes detailed information. If the user is in a hurry, the service provider may provide a concise display method. By adjusting the way responses are displayed based on the user's emotions, more appropriate displays become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may use AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0087] The service provider can select the optimal display method by referring to the user's past dialogue history when providing a response. For example, the service provider may prioritize displaying methods that the user has previously preferred. The service provider can also display relevant information from the user's past dialogue history. For example, the service provider may analyze the user's past dialogue history and propose the optimal display method. This allows the service provider to select the optimal display method by referring to the user's past dialogue history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's past dialogue history into AI, which can then select the optimal display method.

[0088] The service provider can adjust the display method based on the user's current learning status and areas of interest when providing a response. For example, the service provider may prioritize displaying information related to the topic the user is currently studying. The service provider can also display relevant information based on the user's areas of interest. For example, the service provider may suggest an appropriate display method according to the user's learning status. This allows for a more appropriate display by adjusting the display method based on the user's current learning status and areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's learning status and areas of interest into the AI, which can then display relevant information.

[0089] The service provider can estimate the user's emotions and adjust the display order of responses based on the estimated emotions. For example, if the user is excited, the service provider may prioritize displaying information that is of interest. If the user is sad, the service provider may also prioritize displaying information related to comfort or encouragement. For example, if the user is curious, the service provider may prioritize displaying academic information. By adjusting the display order of responses based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may use AI to analyze the user's facial expressions and voice in order to estimate the user's emotions.

[0090] The service provider can select the optimal display method when providing a response, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. If the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to select the optimal display method by taking into account the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into the AI, and the AI ​​can select the optimal display method.

[0091] The service provider can provide multilingual displays according to the user's language settings when providing a response. For example, the service provider can automatically set the display language based on the language settings of the user's device. The service provider can also provide a language switching function if the user uses multiple languages. For example, if the user selects a specific language, the service provider will provide the display in that language. This enables more appropriate displays by providing multilingual displays according to the user's language settings. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's language settings into AI, and the AI ​​can provide multilingual displays.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The selection unit can estimate the user's emotions and, based on those emotions, present candidates for historical figures with whom the user might want to interact. For example, if the user is excited, historical figures related to war or adventure can be prioritized. If the user is sad, historical figures related to peace or healing can be prioritized. Furthermore, if the user is curious, historical figures related to science or invention can be prioritized. This allows for the selection of a more appropriate conversation partner by presenting candidates for historical figures based on the user's emotions. Emotion estimation is achieved, for example, using an emotion engine or generative AI. Some or all of the processing described above in the selection unit may be performed using AI or not.

[0094] The generation unit can estimate the user's emotions and adjust the way the response is expressed based on the estimated emotions. For example, if the user is nervous, a simple and easy-to-understand expression can be used. If the user is relaxed, an expression including detailed explanations can be used. Furthermore, if the user is in a hurry, a concise expression that gets straight to the point can be used. In this way, by adjusting the way the response is expressed based on the user's emotions, a more appropriate response can be provided. Emotion estimation is achieved using an emotion engine or a generation AI, etc. Some or all of the above processing in the generation unit may be performed using AI or not using AI.

[0095] The service provider can estimate the user's emotions and adjust the display method of the response based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, adjusting the display method of the response based on the user's emotions enables more appropriate displays. Emotion estimation is achieved using an emotion engine or generative AI, etc. Some or all of the processing described above in the service provider may be performed using AI or not using AI.

[0096] The reception desk can estimate the user's emotions and adjust the way questions are answered based on those emotions. For example, if the user is nervous, a simple and intuitive interface can be provided. If the user is relaxed, more detailed input options can be provided. Furthermore, if the user is in a hurry, voice input can be prioritized to quickly answer the question. This allows for more appropriate question answering by adjusting the way questions are answered based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Some or all of the processing described above in the reception desk may be performed using AI or not.

[0097] The selection unit can estimate the user's emotions and determine the priority of the people to select based on those emotions. For example, if the user is excited, historical figures related to war or adventure can be prioritized. If the user is sad, historical figures related to peace or healing can be prioritized. Furthermore, if the user is curious, historical figures related to science or invention can be prioritized. This allows for the selection of a more appropriate conversation partner by prioritizing the people to select based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the processing described above in the selection unit may be performed using AI or not.

[0098] The selection unit can analyze the user's past selection history and recommend the most suitable person. For example, it can analyze the tendencies of people the user has selected in the past and recommend people with similar interests. It can also recommend people related to the era and region of people the user has previously interacted with. Furthermore, it can recommend people related to the themes of people the user has previously interacted with. In this way, by analyzing the user's past selection history, the most suitable person can be recommended. Some or all of the above processing in the selection unit may be performed using AI or not.

[0099] The selection unit can filter the results based on the user's current areas of interest and learning goals. For example, it can prioritize presenting individuals related to the history topics the user is currently studying. It can also present individuals related to specific fields the user is interested in (e.g., science, politics, art). Furthermore, it can filter and present relevant individuals based on the user's learning goals. This allows for the selection of more appropriate individuals by filtering based on the user's current areas of interest and learning goals. Some or all of the above processing in the selection unit may be performed using AI or not.

[0100] The selection unit can prioritize presenting highly relevant individuals by considering the user's geographical location during the selection process. For example, it can prioritize presenting historical figures related to the user's current location. If the user is traveling, it can also present figures related to the history of that region. Furthermore, based on the user's geographical location, it can present figures related to the historical background of the region. This allows for the prioritization of highly relevant individuals by considering the user's geographical location. Some or all of the above processing in the selection unit may be performed using generative AI, or it may be performed without using generative AI.

[0101] The selection unit can analyze the user's social media activity during the selection process and recommend relevant individuals. For example, it can recommend individuals based on history-related accounts the user follows on social media. It can also recommend individuals based on historical articles or posts the user has shared on social media. Furthermore, it can analyze the user's social media activity history and recommend individuals who might be of interest. In this way, relevant individuals can be recommended by analyzing the user's social media activity. Some or all of the above processing in the selection unit may be performed using AI or not.

[0102] The reception desk can analyze the user's past question history to select the optimal reception method when a question is received. For example, it can prioritize suggesting question formats that the user has frequently used in the past. It can also automatically complete relevant questions from the user's past question history. Furthermore, it can analyze the user's past question history and suggest the optimal question format. In this way, the optimal question reception method can be selected by analyzing the user's past question history. Some or all of the above processing in the reception desk may be performed using AI, or it may be performed without using AI.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The selection section allows the user to choose a historical figure they wish to interact with. Through the application interface, users can select prominent historical figures from a list. The application also provides features for selecting figures from specific eras or regions, and for searching for figures of interest. Step 2: The reception desk receives the user's question. The user can enter a specific question for a selected person, and the question is sent to the AI. A voice input function is also provided for entering questions. Step 3: The generation unit dynamically generates responses based on the questions received by the reception unit. It utilizes deep learning to recreate the personality and knowledge of the selected person and generates responses based on historical context. Step 4: The providing unit provides the user with the response generated by the generating unit. The generated response is displayed on the user's screen, and functions for audio playback and notification are also provided.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0107] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] Each of the multiple elements described above, including the selection unit, reception unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the selection unit is implemented by the control unit 46A of the smart device 14, allowing the user to select a famous historical figure through the application interface. The reception unit is implemented by the control unit 46A of the smart device 14, allowing the user to input specific questions about the selected person. The generation unit is implemented by the identification processing unit 290 of the data processing device 12, utilizing deep learning to reproduce the personality and knowledge of the selected person and generate a response based on the historical context. The provision unit is implemented by the control unit 46A of the smart device 14, allowing the generated response to be displayed on the user's screen. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0109] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0110] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] Each of the multiple elements described above, including the selection unit, reception unit, generation unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the selection unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to select a famous historical figure through the application interface. The reception unit is implemented by the control unit 46A of the smart glasses 214, allowing the user to input specific questions about the selected person. The generation unit is implemented by the identification processing unit 290 of the data processing device 12, utilizing deep learning to reproduce the personality and knowledge of the selected person and generate a response based on the historical context. The provision unit is implemented by the control unit 46A of the smart glasses 214, allowing the generated response to be displayed on the user's screen. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0125] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0126] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0133] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0134] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0135] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements described above, including the selection unit, reception unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to select a famous historical figure through the application interface. The reception unit is implemented by the control unit 46A of the headset terminal 314, allowing the user to input specific questions about the selected person. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, utilizing deep learning to reproduce the personality and knowledge of the selected person and generate a response based on the historical context. The provision unit is implemented by the control unit 46A of the headset terminal 314, allowing the generated response to be displayed on the user's screen. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0141] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0142] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0149] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the selection unit, reception unit, generation unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the selection unit is implemented by the control unit 46A of the robot 414, allowing the user to select a famous historical figure through the application interface. The reception unit is implemented by the control unit 46A of the robot 414, allowing the user to input specific questions about the selected person. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, utilizing deep learning to reproduce the personality and knowledge of the selected person and generate a response based on the historical context. The provision unit is implemented by the control unit 46A of the robot 414, allowing the generated response to be displayed on the user's screen. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0158] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0160] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0161] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0162] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0166] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0167] 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.

[0168] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0169] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0170] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0171] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0173] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0174] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0175] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0176] (Note 1) A selection section where the user selects the historical figure they want to interact with, A reception desk that handles user inquiries, A generation unit that dynamically generates a response based on a question received by the reception unit, The system includes a providing unit that provides the response generated by the generation unit to the user. A system characterized by the following features. (Note 2) The generating unit is Using deep learning, we recreate the personality and knowledge of selected individuals and generate responses based on historical context. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Provide the generated response to the user. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned selection unit is It estimates the user's emotions and, based on those emotions, suggests potential historical figures with whom the user might want to interact. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned selection unit is Analyze the user's past selection history and recommend the most suitable person. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned selection unit is When selecting, filtering is performed based on the user's current areas of interest and learning goals. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned selection unit is It estimates the user's emotions and determines the priority of the people to select based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned selection unit is When making a selection, the system prioritizes presenting highly relevant individuals, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned selection unit is When making a selection, the system analyzes the user's social media activity and recommends relevant individuals. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and adjusts how questions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When a question is submitted, the system analyzes the user's past question history to select the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When a question is submitted, it is filtered based on the user's current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is It estimates the user's emotions and determines the priority of questions to ask based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When receiving questions, the system prioritizes questions that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is When receiving questions, the system analyzes the user's social media activity and accepts relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the way responses are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating a response, adjust the level of detail in the response based on the importance of the question. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating responses, different response algorithms are applied depending on the category of the question. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the length of the response based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating responses, the priority of responses is determined based on when the questions were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating responses, the order of responses is adjusted based on the relevance of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts how responses are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing a response, the system selects the optimal display method by referring to the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing a response, the display method is adjusted based on the user's current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts the order in which responses are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing a response, the system selects the optimal display method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing a response, the system will offer multilingual support according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A selection section where the user selects the historical figure they want to interact with, A reception desk that handles user inquiries, A generation unit that dynamically generates a response based on a question received by the reception unit, The system includes a providing unit that provides the response generated by the generation unit to the user. A system characterized by the following features.

2. The generating unit is Using deep learning, we recreate the personality and knowledge of selected individuals and generate responses based on historical context. The system according to feature 1.

3. The aforementioned supply unit is, Provide the generated response to the user. The system according to feature 1.

4. The aforementioned selection unit is It estimates the user's emotions and, based on those emotions, suggests potential historical figures with whom the user might want to interact. The system according to feature 1.

5. The aforementioned selection unit is Analyze the user's past selection history and recommend the most suitable person. The system according to feature 1.

6. The aforementioned selection unit is When selecting, filtering is performed based on the user's current areas of interest and learning goals. The system according to feature 1.

7. The aforementioned selection unit is It estimates the user's emotions and determines the priority of the people to select based on the estimated user emotions. The system according to feature 1.

8. The aforementioned selection unit is When making a selection, the system prioritizes presenting highly relevant individuals, taking into account the user's geographical location. The system according to feature 1.

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