System

Abstract

An object of the system according to the embodiment is to allow a user to obtain an answer that incorporates thinking of a great person.SOLUTION: A system includes a great person thinking capturing part, a question analysis part, and an answer generation part. The great person thought capturing unit captures a thought of a great person. The question analysis unit analyzes a question of a user. The answer generation unit generates an answer based on the question analyzed by the question analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for users to obtain answers that incorporate the thoughts of great people.

[0005] The system according to the embodiment aims to enable users to obtain answers that incorporate the thoughts of great people. [Means for solving the problem]

[0006] The system according to the embodiment includes a great person's thought capturing unit, a question analysis unit, and an answer generation unit. The great person's thought capturing unit captures the thoughts of great people. The question analysis unit analyzes a user's question. The answer generation unit generates an answer based on the question analyzed by the question analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows the user to obtain answers that incorporate the thoughts of great people. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A generative AI app according to an embodiment of the present invention is an application that provides answers to user questions using a generative AI that incorporates the thoughts of great people. This generative AI app reads documents and interviews of great people active in various fields and incorporates their thoughts into the app. This allows the user to experience the generative AI app as if they were receiving answers directly from great people based on their own interests.

[0029] A generative AI app according to an embodiment includes a great person's thoughts capture unit, a question analysis unit, and an answer generation unit. The great person's thoughts capture unit captures the thoughts of great people. For example, it analyzes documents and interviews of great people and allows the generative AI to learn the content. The great person's thoughts capture unit also analyzes the statements and behavioral patterns of great people and models their thoughts. For example, the great person's books and interviews are input as text data, and the generative AI analyzes and learns from the content. The question analysis unit analyzes user questions. For example, it uses natural language processing technology to analyze questions entered by users and understand their content. The question analysis unit can also analyze the intent and background of user questions. For example, if a user asks, "What is the secret to business success?", the intent of the question is analyzed and information for generating an appropriate answer is extracted. The answer generation unit generates an answer based on the question analyzed by the question analysis unit. For example, the generative AI generates an answer based on the thoughts of great people. The answer generation unit also provides the answer generated by the generative AI to the user. For example, the generation AI generates an answer such as "The secret to business success is to take risks and challenge yourself" and displays it to the user. This allows the generation AI app according to the embodiment to provide answers that incorporate the thoughts of great people. For example, if a user asks a question about business, the generation AI generates an answer based on the thoughts of a famous CEO and provides it to the user. Also, if a user asks a question about art, the generation AI generates an answer based on the thoughts of a famous author or film director and provides it to the user.

[0030] The great person's thought capturing unit can also analyze the great person's audio data or video data and learn the deepest levels of their thoughts from the tone and facial expressions of their speech. The great person's thought capturing unit, for example, analyzes the great person's audio data and learns the tone and rhythm of their speech. For example, it analyzes the tone and rhythm of the great person when they speak about a specific topic and generates answers that reflect those characteristics. The great person's thought capturing unit can also analyze the great person's video data and learn their facial expressions when they speak. For example, it can analyze the facial expressions of the great person when they speak about a specific topic and generate answers that reflect those facial expressions. The great person's thought capturing unit can also analyze the audio data and video data in combination and learn the tone and facial expressions of their speech comprehensively. For example, it analyzes the tone and facial expressions of the great person when they speak about a specific topic and generates answers that reflect those characteristics. This makes it possible to generate answers that reflect the tone and facial expressions of the great person's speech.

[0031] The great person's thinking assimilation unit simultaneously learns other great people and literature that influenced the great person, allowing for a deep understanding of the background and context of their thinking. For example, the great person's thinking assimilation unit analyzes literature and interviews of other great people who influenced the great person, and learns the background and context of their thinking. For example, it analyzes how the great person was influenced by a specific topic, and generates answers that reflect that background. The great person's thinking assimilation unit also analyzes literature that influenced the great person, and learns its content. For example, it can analyze how the great person was influenced by a specific book, and generate answers that reflect that content. The great person's thinking assimilation unit also analyzes the statements and actions of other great people who influenced the great person, and learns the background of their thinking. For example, it can analyze how the great person was influenced by a specific great person, and generate answers that reflect that background. This makes it possible to generate answers that have a deep understanding of the background and context of the great person's thinking.

[0032] The great person's thinking incorporating unit can incorporate the thoughts of great people from different cultural spheres and eras, and generate answers from a global perspective. The great person's thinking incorporating unit, for example, analyzes documents and interviews of great people from different cultural spheres and learns their thoughts. For example, it can integrate the thoughts of great people from the East and the West and generate answers from a global perspective. The great person's thinking incorporating unit can also analyze documents and interviews of great people from different eras and learns their thoughts. For example, it can integrate the thoughts of great people from ancient and modern times and generate answers from a timeless perspective. The great person's thinking incorporating unit can also comprehensively analyze the thoughts of great people from different cultural spheres and eras and generate answers from a global perspective. For example, it can integrate the thoughts of great people from different cultural spheres and eras and generate answers that reflect their perspectives. This makes it possible to generate answers from a global perspective.

[0033] The unit for incorporating the thoughts of great people can classify the thoughts of great people by theme and generate answers specialized for that particular theme. For example, the unit for incorporating the thoughts of great people can classify documents and interviews of great people by theme and learn thoughts specialized for that theme. For example, the unit for incorporating the thoughts of great people can classify the thoughts of great people by theme, such as business, art, and science, and generate answers specialized for that theme. The unit for incorporating the thoughts of great people can also classify the statements and actions of great people by theme and learn thoughts specialized for that theme. For example, the unit can analyze how great people thought about a particular theme and generate answers specialized for that theme. The unit for incorporating the thoughts of great people can also develop an algorithm for classifying the thoughts of great people by theme and generating answers specialized for that theme. For example, the unit can classify the thoughts of great people by theme and build a model for generating answers specialized for that theme. This makes it possible to generate answers specialized for a particular theme.

[0034] The question analysis unit can generate more accurate answers by referring to data on questions and answers from past users. The question analysis unit, for example, analyzes data on questions and answers from past users and generates answers based on that data. For example, it references past answers to similar questions to generate more accurate answers. The question analysis unit can also analyze data on questions and answers from past users and develop an algorithm for generating answers based on that data. For example, it builds a model for generating answers based on past data. The question analysis unit also builds a system for generating answers based on that data by referring to data on questions and answers from past users. For example, it builds a database for generating answers based on past data and generates answers by referring to that data. This makes it possible to generate highly accurate answers based on past data.

[0035] The question analysis unit enables the generative AI to generate answers by combining the thoughts of multiple great people, thereby providing a composite perspective. The question analysis unit, for example, integrates the thoughts of multiple great people and generates answers from a composite perspective. For example, it integrates the thoughts of great people in business and art to generate answers from a composite perspective. The question analysis unit can also develop an algorithm for generating answers by combining the thoughts of multiple great people. For example, it integrates the thoughts of great people in business and art and builds a model for generating answers that reflect that perspective. The question analysis unit also builds a system for generating answers by combining the thoughts of multiple great people. For example, it integrates the thoughts of great people in business and art and builds a database for generating answers that reflect that perspective. This makes it possible to generate answers from a composite perspective that combines the thoughts of multiple great people.

[0036] The question analysis unit can also handle questions in different languages ​​and generate answers in multiple languages. For example, the question analysis unit automatically translates a user's question into a different language and generates an answer based on the translation result. For example, answers are generated in multiple languages, such as English, French, and Chinese. The question analysis unit can also develop algorithms for handling questions in different languages. For example, the question analysis unit automatically translates a user's question and builds a model for generating an answer based on the translation result. The question analysis unit also builds a system for handling questions in different languages. For example, the question analysis unit automatically translates a user's question and builds a database for generating an answer based on the translation result. This makes it possible to generate questions and answers in multiple languages.

[0037] The question analysis unit can generate a personalized answer by taking into account background information of the question. The question analysis unit, for example, collects information on the user's occupation and interests, and generates a personalized answer based on that information. For example, if the user is interested in business, it generates an answer specialized for business. The question analysis unit can also develop an algorithm for generating an answer by taking into account the user's background information. For example, it builds a model for generating an answer based on the user's occupation and interests. The question analysis unit also builds a system for generating an answer by taking into account the user's background information. For example, it collects information on the user's occupation and interests, and builds a database for generating an answer based on that information. This makes it possible to generate a personalized answer by taking into account the user's background information.

[0038] The question analysis unit can refer to the user's past question history and select the most relevant great person. For example, the question analysis unit analyzes the user's past question history and selects the most relevant great person based on the data. For example, if the user has asked many questions about business in the past, a great person in the business field is selected. The question analysis unit can also analyze the user's past question history and develop an algorithm for selecting the most relevant great person based on the data. For example, a model for selecting the most relevant great person based on the user's past question history is constructed. The question analysis unit can also refer to the user's past question history and build a system for selecting the most relevant great person based on the data. For example, a database for selecting the most relevant great person based on the user's past question history is constructed. This makes it possible to select the most relevant great person based on the user's past question history.

[0039] The question analysis unit can provide a more multifaceted perspective by taking into account not only the great person's field of expertise but also their personal interests and hobbies. For example, the question analysis unit can analyze not only the great person's field of expertise but also their personal interests and hobbies, and select a great person based on that information. For example, the question analysis unit can generate answers from a more multifaceted perspective by taking into account the artistic hobbies of a great person in the business field. The question analysis unit can also develop an algorithm for generating answers by taking into account the great person's field of expertise and personal interests and hobbies. For example, it can build a model for generating answers based on the great person's field of expertise and personal interests and hobbies. The question analysis unit can also build a system for generating answers by taking into account the great person's field of expertise and personal interests and hobbies. For example, it can build a database for generating answers based on the great person's field of expertise and personal interests and hobbies. This makes it possible to generate answers from a multifaceted perspective by taking into account the great person's field of expertise and personal interests and hobbies.

[0040] The question analysis unit can combine great people from different fields to generate answers from a crossover perspective. For example, the question analysis unit can integrate the thoughts of great people from different fields to generate answers from a crossover perspective. For example, the question analysis unit can integrate the thoughts of great people from business and art to generate answers from a composite perspective. The question analysis unit can also develop an algorithm for generating answers by combining great people from different fields. For example, the question analysis unit can integrate the thoughts of great people from business and art and build a model for generating answers that reflect that perspective. The question analysis unit can also build a system for generating answers by combining great people from different fields. For example, the question analysis unit can integrate the thoughts of great people from business and art and build a database for generating answers that reflect that perspective. This makes it possible to generate answers from a crossover perspective that combines great people from different fields.

[0041] The question analysis unit can add a function to recommend other related great people based on the thoughts of a great person selected by a user. The question analysis unit, for example, analyzes the thoughts of the great person selected by the user and recommends other related great people based on that information. For example, if a user selects a great person in the business field, the question analysis unit can recommend related great people in other business fields. The question analysis unit can also develop an algorithm for recommending other great people based on the thoughts of the great person selected by the user. For example, the question analysis unit can analyze the thoughts of the great person selected by the user and build a model for recommending other great people based on that information. The question analysis unit can also build a system for recommending other great people based on the thoughts of the great person selected by the user. For example, the question analysis unit can analyze the thoughts of the great person selected by the user and build a database for recommending other great people based on that information. This makes it possible to recommend other related great people based on the thoughts of the great person selected by the user.

[0042] The user interface may add a voice input function to allow a user to input questions by voice. The user interface may, for example, add a voice input function to allow a user to input questions by voice. For example, a microphone may be used to recognize the user's voice and convert the voice into text. The user interface may also add a voice input function and develop an algorithm to allow a user to input questions by voice. For example, a model may be built using voice recognition technology to convert the user's voice into text. The user interface may also add a voice input function and build a system to allow a user to input questions by voice. For example, a database may be built using voice recognition technology to convert the user's voice into text. This allows a user to input questions by voice.

[0043] The user interface can add a function that saves a user's question history and allows easy reference to past questions and answers. For example, the user interface adds a function that saves a user's question history and allows easy reference to past questions and answers. For example, it displays a list of questions that the user has previously asked and their answers. The user interface can also develop an algorithm that saves a user's question history and allows easy reference to past questions and answers. For example, it saves a user's question history and builds a model for referencing past questions and answers based on that data. The user interface also builds a system that saves a user's question history and allows easy reference to past questions and answers. For example, it saves a user's question history and builds a database for referencing past questions and answers based on that data. This allows the user to easily refer to past questions and answers.

[0044] The user interface can employ a responsive design that is compatible with different devices (smartphones, tablets, PCs). For example, the user interface can employ a responsive design that is compatible with different devices, allowing users to use the app comfortably on any device. For example, the display can be optimized for smartphones, tablets, and PCs. The user interface can also develop an algorithm to employ a responsive design that is compatible with different devices. For example, a model can be built to adjust the layout for each device. The user interface can also build a system to employ a responsive design that is compatible with different devices. For example, a database can be built to adjust the layout for each device. This allows the application to employ a responsive design that is compatible with different devices.

[0045] The user interface can provide themes and layouts that can be customized by users, thereby realizing an interface that suits individual preferences. The user interface, for example, can provide themes and layouts that can be customized by users, thereby realizing an interface that suits individual preferences. For example, a function can be added that allows colors, fonts, and layouts to be freely changed. The user interface can also develop an algorithm for providing themes and layouts that can be customized by users. For example, a model can be built for changing themes and layouts according to user preferences. The user interface can also build a system for providing themes and layouts that can be customized by users. For example, a database can be built for changing themes and layouts according to user preferences. This allows the user to provide themes and layouts that can be customized by users, thereby realizing an interface that suits individual preferences.

[0046] The Great Person's Thoughts Incorporation Unit can also analyze the social media posts and blog posts of great people and incorporate their latest thoughts and values. For example, the Great Person's Thoughts Incorporation Unit analyzes the social media posts and blog posts of great people and has the generation AI learn their content. For example, it analyzes what opinions the great people have on the latest topics and generates answers that reflect that content. The Great Person's Thoughts Incorporation Unit can also analyze the social media posts and blog posts of great people and develop algorithms to incorporate their latest thoughts and values. For example, it can analyze the social media posts and blog posts of great people and build a model to generate answers that reflect their content. The Great Person's Thoughts Incorporation Unit can also analyze the social media posts and blog posts of great people and build a system to incorporate their latest thoughts and values. For example, it can analyze the social media posts and blog posts of great people and build a database to generate answers that reflect their content. This makes it possible to analyze the social media posts and blog posts of great people and incorporate their latest thoughts and values.

[0047] The unit for capturing the thoughts of great people can track the evolution of the thoughts of great people over time and reflect the evolution of their thinking from the past to the present. For example, the unit for capturing the thoughts of great people can analyze documents and interviews of great people over time and track the evolution of their thinking. For example, the unit can analyze how a great person has changed their opinion on a particular topic and generate an answer that reflects those changes. The unit for capturing the thoughts of great people can also develop an algorithm for analyzing the evolution of the thoughts of great people over time and generating an answer that reflects those changes. For example, the unit can analyze the evolution of the thoughts of great people over time and build a model for generating an answer that reflects those changes. The unit for capturing the thoughts of great people can also analyze the evolution of the thoughts of great people over time and build a system for generating an answer that reflects those changes. For example, the unit can analyze the evolution of the thoughts of great people over time and build a database for generating an answer that reflects those changes. This makes it possible to track the evolution of the thoughts of great people over time and reflect the evolution of their thinking from the past to the present.

[0048] The Great Figures Thoughts Intake Unit can cross-reference the thoughts of great people from different fields to gain new insights. For example, the Great Figures Thoughts Intake Unit cross-references literature and interviews of great people from different fields and integrates their thoughts. For example, it can integrate the thoughts of great people from business and art to gain new insights. The Great Figures Thoughts Intake Unit can also develop algorithms for cross-referencing the thoughts of great people from different fields to gain new insights. For example, it can integrate the thoughts of great people from different fields and build a model for generating answers that reflect their perspectives. The Great Figures Thoughts Intake Unit can also build a system for cross-referencing the thoughts of great people from different fields to gain new insights. For example, it can build a database for integrating the thoughts of great people from different fields and generating answers that reflect their perspectives. This makes it possible to cross-reference the thoughts of great people from different fields to gain new insights.

[0049] The great person's thought incorporating unit can improve the great person's thought model based on feedback from the user. The great person's thought incorporating unit, for example, collects feedback from the user and improves the great person's thought model based on that data. For example, it analyzes feedback provided by the user and reflects it in the great person's thought model. The great person's thought incorporating unit can also develop an algorithm for improving the great person's thought model based on user feedback. For example, it analyzes user feedback and builds a model for improving the great person's thought model based on that information. The great person's thought incorporating unit also builds a system for improving the great person's thought model based on user feedback. For example, it collects user feedback and builds a database for improving the great person's thought model based on that information. In this way, the great person's thought model can be improved based on user feedback.

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

[0051] The great person's thinking capture unit captures the thoughts of great people. For example, it analyzes the literature and interviews of great people and has the generation AI learn from them. The great person's thinking capture unit also analyzes the statements and behavioral patterns of great people and models their thinking. For example, the great person's books and interviews are input as text data, and the generation AI analyzes and learns from the content. The question analysis unit analyzes the user's question. For example, it uses natural language processing technology to analyze the question entered by the user and understands its content. The question analysis unit can also analyze the intent and background of the user's question. For example, if a user asks, "What is the secret to business success?", the question analysis unit analyzes the intent of the question and extracts information to generate an appropriate answer. The answer generation unit generates an answer based on the question analyzed by the question analysis unit. For example, the generation AI generates an answer based on the thoughts of great people. The answer generation unit also provides the answer generated by the generation AI to the user. For example, the generation AI generates an answer such as, "The secret to business success is to take risks and challenge yourself" and displays it to the user. As a result, the generative AI app according to the embodiment can provide answers that incorporate the thoughts of great people. For example, if a user asks a question about business, the generative AI generates an answer based on the thoughts of a famous CEO and provides it to the user. Also, if a user asks a question about art, the generative AI generates an answer based on the thoughts of a famous author or film director and provides it to the user.

[0052] The great person's thought capturing unit can also analyze the great person's audio data or video data and learn the deepest levels of their thoughts from the tone and facial expressions of their speech. The great person's thought capturing unit, for example, analyzes the great person's audio data and learns the tone and rhythm of their speech. For example, it analyzes the tone and rhythm of the great person when they speak about a specific topic and generates answers that reflect those characteristics. The great person's thought capturing unit can also analyze the great person's video data and learn their facial expressions when they speak. For example, it can analyze the facial expressions of the great person when they speak about a specific topic and generate answers that reflect those facial expressions. The great person's thought capturing unit can also analyze the audio data and video data in combination and learn the tone and facial expressions of their speech comprehensively. For example, it analyzes the tone and facial expressions of the great person when they speak about a specific topic and generates answers that reflect those characteristics. This makes it possible to generate answers that reflect the tone and facial expressions of the great person's speech.

[0053] The great person's thinking assimilation unit simultaneously learns other great people and literature that influenced the great person, allowing for a deep understanding of the background and context of their thinking. For example, the great person's thinking assimilation unit analyzes literature and interviews of other great people who influenced the great person, and learns the background and context of their thinking. For example, it analyzes how the great person was influenced by a specific topic, and generates answers that reflect that background. The great person's thinking assimilation unit also analyzes literature that influenced the great person, and learns its content. For example, it can analyze how the great person was influenced by a specific book, and generate answers that reflect that content. The great person's thinking assimilation unit also analyzes the statements and actions of other great people who influenced the great person, and learns the background of their thinking. For example, it can analyze how the great person was influenced by a specific great person, and generate answers that reflect that background. This makes it possible to generate answers that have a deep understanding of the background and context of the great person's thinking.

[0054] The great person's thinking incorporating unit can incorporate the thoughts of great people from different cultural spheres and eras, and generate answers from a global perspective. The great person's thinking incorporating unit, for example, analyzes documents and interviews of great people from different cultural spheres and learns their thoughts. For example, it can integrate the thoughts of great people from the East and the West and generate answers from a global perspective. The great person's thinking incorporating unit can also analyze documents and interviews of great people from different eras and learns their thoughts. For example, it can integrate the thoughts of great people from ancient and modern times and generate answers from a timeless perspective. The great person's thinking incorporating unit can also comprehensively analyze the thoughts of great people from different cultural spheres and eras and generate answers from a global perspective. For example, it can integrate the thoughts of great people from different cultural spheres and eras and generate answers that reflect their perspectives. This makes it possible to generate answers from a global perspective.

[0055] The unit for incorporating the thoughts of great people can classify the thoughts of great people by theme and generate answers specialized for that particular theme. For example, the unit for incorporating the thoughts of great people can classify documents and interviews of great people by theme and learn thoughts specialized for that theme. For example, the unit for incorporating the thoughts of great people can classify the thoughts of great people by theme, such as business, art, and science, and generate answers specialized for that theme. The unit for incorporating the thoughts of great people can also classify the statements and actions of great people by theme and learn thoughts specialized for that theme. For example, the unit can analyze how great people thought about a particular theme and generate answers specialized for that theme. The unit for incorporating the thoughts of great people can also develop an algorithm for classifying the thoughts of great people by theme and generating answers specialized for that theme. For example, the unit can classify the thoughts of great people by theme and build a model for generating answers specialized for that theme. This makes it possible to generate answers specialized for a particular theme.

[0056] The question analysis unit can generate more accurate answers by referring to data on questions and answers from past users. The question analysis unit, for example, analyzes data on questions and answers from past users and generates answers based on that data. For example, it references past answers to similar questions to generate more accurate answers. The question analysis unit can also analyze data on questions and answers from past users and develop an algorithm for generating answers based on that data. For example, it builds a model for generating answers based on past data. The question analysis unit also builds a system for generating answers based on that data by referring to data on questions and answers from past users. For example, it builds a database for generating answers based on past data and generates answers by referring to that data. This makes it possible to generate highly accurate answers based on past data.

[0057] The question analysis unit enables the generative AI to generate answers by combining the thoughts of multiple great people, thereby providing a composite perspective. The question analysis unit, for example, integrates the thoughts of multiple great people and generates answers from a composite perspective. For example, it integrates the thoughts of great people in business and art to generate answers from a composite perspective. The question analysis unit can also develop an algorithm for generating answers by combining the thoughts of multiple great people. For example, it integrates the thoughts of great people in business and art and builds a model for generating answers that reflect that perspective. The question analysis unit also builds a system for generating answers by combining the thoughts of multiple great people. For example, it integrates the thoughts of great people in business and art and builds a database for generating answers that reflect that perspective. This makes it possible to generate answers from a composite perspective that combines the thoughts of multiple great people.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The Great Person's Thought Acquisition Unit acquires the thoughts of great people. For example, it analyzes documents and interviews of great people and has the generation AI learn from them. It also analyzes the great person's statements and behavioral patterns and models their thoughts. Specifically, the great person's books and interviews are input as text data, and the generation AI analyzes and learns from the content. Step 2: The question analysis unit analyzes the user's question. For example, it uses natural language processing technology to analyze the question entered by the user and understand its content. It can also analyze the intention and background of the user's question. For example, if a user asks, "What is the secret to business success?", the intent of the question is analyzed and information is extracted to generate an appropriate answer. Step 3: The answer generation unit generates an answer based on the question analyzed by the question analysis unit. For example, the generation AI generates an answer based on the thoughts of great people. The answer generated by the generation AI is then provided to the user. Specifically, the generation AI generates an answer such as "The secret to business success is to take on challenges without fear of risk" and displays it to the user.

[0060] (Example 2) A generative AI app according to an embodiment of the present invention is an application that provides answers to user questions using a generative AI that incorporates the thoughts of great people. This generative AI app reads documents and interviews of great people active in various fields and incorporates their thoughts into the app. This allows the user to experience the generative AI app as if they were receiving answers directly from great people based on their own interests.

[0061] A generative AI app according to an embodiment includes a great person's thoughts capture unit, a question analysis unit, and an answer generation unit. The great person's thoughts capture unit captures the thoughts of great people. For example, it analyzes documents and interviews of great people and allows the generative AI to learn the content. The great person's thoughts capture unit also analyzes the statements and behavioral patterns of great people and models their thoughts. For example, the great person's books and interviews are input as text data, and the generative AI analyzes and learns from the content. The question analysis unit analyzes user questions. For example, it uses natural language processing technology to analyze questions entered by users and understand their content. The question analysis unit can also analyze the intent and background of user questions. For example, if a user asks, "What is the secret to business success?", the intent of the question is analyzed and information for generating an appropriate answer is extracted. The answer generation unit generates an answer based on the question analyzed by the question analysis unit. For example, the generative AI generates an answer based on the thoughts of great people. The answer generation unit also provides the answer generated by the generative AI to the user. For example, the generation AI generates an answer such as "The secret to business success is to take risks and challenge yourself" and displays it to the user. This allows the generation AI app according to the embodiment to provide answers that incorporate the thoughts of great people. For example, if a user asks a question about business, the generation AI generates an answer based on the thoughts of a famous CEO and provides it to the user. Also, if a user asks a question about art, the generation AI generates an answer based on the thoughts of a famous author or film director and provides it to the user.

[0062] The Great Person's Thoughts Capture Unit can also learn the emotional nuances of great people using the emotion estimation function. For example, when analyzing documents or interviews about great people, the Great Person's Thoughts Capture Unit uses the emotion estimation function to extract emotional nuances from the text and has the generation AI learn them. For example, it can analyze how great people felt about a particular topic and generate answers that reflect those emotions. The Great Person's Thoughts Capture Unit can also use the emotion estimation function to analyze the emotional tone of a great person's statements and generate answers that reflect that tone. For example, for a topic that a great person spoke about with passion, the generation AI will generate answers that reflect that passion. This makes it possible to generate answers that reflect the emotional nuances of great people.

[0063] The great person's thought capturing unit can also analyze the great person's audio data or video data and learn the deepest levels of their thoughts from the tone and facial expressions of their speech. The great person's thought capturing unit, for example, analyzes the great person's audio data and learns the tone and rhythm of their speech. For example, it analyzes the tone and rhythm of the great person when they speak about a specific topic and generates answers that reflect those characteristics. The great person's thought capturing unit can also analyze the great person's video data and learn their facial expressions when they speak. For example, it can analyze the facial expressions of the great person when they speak about a specific topic and generate answers that reflect those facial expressions. The great person's thought capturing unit can also analyze the audio data and video data in combination and learn the tone and facial expressions of their speech comprehensively. For example, it analyzes the tone and facial expressions of the great person when they speak about a specific topic and generates answers that reflect those characteristics. This makes it possible to generate answers that reflect the tone and facial expressions of the great person's speech.

[0064] The great person's thinking assimilation unit simultaneously learns other great people and literature that influenced the great person, allowing for a deep understanding of the background and context of their thinking. For example, the great person's thinking assimilation unit analyzes literature and interviews of other great people who influenced the great person, and learns the background and context of their thinking. For example, it analyzes how the great person was influenced by a specific topic, and generates answers that reflect that background. The great person's thinking assimilation unit also analyzes literature that influenced the great person, and learns its content. For example, it can analyze how the great person was influenced by a specific book, and generate answers that reflect that content. The great person's thinking assimilation unit also analyzes the statements and actions of other great people who influenced the great person, and learns the background of their thinking. For example, it can analyze how the great person was influenced by a specific great person, and generate answers that reflect that background. This makes it possible to generate answers that have a deep understanding of the background and context of the great person's thinking.

[0065] The great person's thinking incorporating unit can incorporate the thoughts of great people from different cultural spheres and eras, and generate answers from a global perspective. The great person's thinking incorporating unit, for example, analyzes documents and interviews of great people from different cultural spheres and learns their thoughts. For example, it can integrate the thoughts of great people from the East and the West and generate answers from a global perspective. The great person's thinking incorporating unit can also analyze documents and interviews of great people from different eras and learns their thoughts. For example, it can integrate the thoughts of great people from ancient and modern times and generate answers from a timeless perspective. The great person's thinking incorporating unit can also comprehensively analyze the thoughts of great people from different cultural spheres and eras and generate answers from a global perspective. For example, it can integrate the thoughts of great people from different cultural spheres and eras and generate answers that reflect their perspectives. This makes it possible to generate answers from a global perspective.

[0066] The unit for incorporating the thoughts of great people can classify the thoughts of great people by theme and generate answers specialized for that particular theme. For example, the unit for incorporating the thoughts of great people can classify documents and interviews of great people by theme and learn thoughts specialized for that theme. For example, the unit for incorporating the thoughts of great people can classify the thoughts of great people by theme, such as business, art, and science, and generate answers specialized for that theme. The unit for incorporating the thoughts of great people can also classify the statements and actions of great people by theme and learn thoughts specialized for that theme. For example, the unit can analyze how great people thought about a particular theme and generate answers specialized for that theme. The unit for incorporating the thoughts of great people can also develop an algorithm for classifying the thoughts of great people by theme and generating answers specialized for that theme. For example, the unit can classify the thoughts of great people by theme and build a model for generating answers specialized for that theme. This makes it possible to generate answers specialized for a particular theme.

[0067] The great person's thought assimilation unit can use the emotion estimation function to track the great person's emotional changes and reflect changes in their thoughts over time. The great person's thought assimilation unit, for example, analyzes documents and interviews about the great person and uses the emotion estimation function to track the great person's emotional changes. For example, it analyzes the emotions the great person had at a specific time and generates answers that reflect those emotions. The great person's thought assimilation unit can also analyze the great person's statements and actions over time and learn the emotional changes. For example, it analyzes the emotions the great person had at a specific time and generates answers that reflect those emotions. The great person's thought assimilation unit can also analyze the great person's emotional changes over time and develop an algorithm for generating answers that reflect those changes. For example, it can analyze the great person's emotional changes over time and build a model for generating answers that reflect those changes. This makes it possible to generate answers that reflect the great person's emotional changes.

[0068] The question analysis unit can analyze the user's emotional state using the emotion estimation function and generate an answer in an optimal tone. The question analysis unit, for example, analyzes the user's question and identifies the user's emotional state using the emotion estimation function. For example, it analyzes the user's facial expression and voice when entering a question and generates an answer in an optimal tone based on that emotional state. The question analysis unit can also analyze the user's emotional state and generate an answer in an optimal tone based on that information. For example, if the user is asking a question in an anxious state, it generates an answer in a tone that gives a sense of security. The question analysis unit can also analyze the user's emotional state using the emotion estimation function and develop an algorithm for generating an answer in an optimal tone based on that information. For example, it analyzes the user's emotional state and builds a model for generating an answer in an optimal tone based on that information. This makes it possible to generate an answer in an optimal tone according to the user's emotional state.

[0069] The question analysis unit can generate more accurate answers by referring to data on questions and answers from past users. The question analysis unit, for example, analyzes data on questions and answers from past users and generates answers based on that data. For example, it references past answers to similar questions to generate more accurate answers. The question analysis unit can also analyze data on questions and answers from past users and develop an algorithm for generating answers based on that data. For example, it builds a model for generating answers based on past data. The question analysis unit also builds a system for generating answers based on that data by referring to data on questions and answers from past users. For example, it builds a database for generating answers based on past data and generates answers by referring to that data. This makes it possible to generate highly accurate answers based on past data.

[0070] The question analysis unit enables the generative AI to generate answers by combining the thoughts of multiple great people, thereby providing a composite perspective. The question analysis unit, for example, integrates the thoughts of multiple great people and generates answers from a composite perspective. For example, it integrates the thoughts of great people in business and art to generate answers from a composite perspective. The question analysis unit can also develop an algorithm for generating answers by combining the thoughts of multiple great people. For example, it integrates the thoughts of great people in business and art and builds a model for generating answers that reflect that perspective. The question analysis unit also builds a system for generating answers by combining the thoughts of multiple great people. For example, it integrates the thoughts of great people in business and art and builds a database for generating answers that reflect that perspective. This makes it possible to generate answers from a composite perspective that combines the thoughts of multiple great people.

[0071] The question analysis unit can also handle questions in different languages ​​and generate answers in multiple languages. For example, the question analysis unit automatically translates a user's question into a different language and generates an answer based on the translation result. For example, answers are generated in multiple languages, such as English, French, and Chinese. The question analysis unit can also develop algorithms for handling questions in different languages. For example, the question analysis unit automatically translates a user's question and builds a model for generating an answer based on the translation result. The question analysis unit also builds a system for handling questions in different languages. For example, the question analysis unit automatically translates a user's question and builds a database for generating an answer based on the translation result. This makes it possible to generate questions and answers in multiple languages.

[0072] The question analysis unit can generate a personalized answer by taking into account background information of the question. The question analysis unit, for example, collects information on the user's occupation and interests, and generates a personalized answer based on that information. For example, if the user is interested in business, it generates an answer specialized for business. The question analysis unit can also develop an algorithm for generating an answer by taking into account the user's background information. For example, it builds a model for generating an answer based on the user's occupation and interests. The question analysis unit also builds a system for generating an answer by taking into account the user's background information. For example, it collects information on the user's occupation and interests, and builds a database for generating an answer based on that information. This makes it possible to generate a personalized answer by taking into account the user's background information.

[0073] The question analysis unit can use the emotion estimation function to generate answers to user questions that include encouragement or advice according to the user's emotions. The question analysis unit, for example, analyzes the user's question and identifies the user's emotional state using the emotion estimation function. For example, if the user is asking the question while in an anxious state, the question analysis unit generates an answer that includes encouragement or advice. The question analysis unit can also analyze the user's emotional state and generate an answer that includes encouragement or advice based on that information. For example, if the user is depressed, the question analysis unit generates an answer that includes positive words. The question analysis unit can also analyze the user's emotional state using the emotion estimation function and develop an algorithm for generating answers that include encouragement or advice based on that information. For example, the question analysis unit analyzes the user's emotional state and builds a model for generating answers that include encouragement or advice based on that information. This makes it possible to generate answers that include encouragement or advice according to the user's emotions.

[0074] The question analysis unit can analyze the user's emotional state using the emotion estimation function and select the most appropriate great person. For example, the question analysis unit analyzes the user's question and identifies the user's emotional state using the emotion estimation function. For example, if the user is asking the question in an anxious state, the question analysis unit selects a great person who gives a sense of security. The question analysis unit can also analyze the user's emotional state and select the most appropriate great person based on that information. For example, if the user is feeling down, the question analysis unit selects a great person who has words of encouragement. The question analysis unit can also analyze the user's emotional state using the emotion estimation function and develop an algorithm for selecting the most appropriate great person based on that information. For example, the question analysis unit can analyze the user's emotional state and build a model for selecting the most appropriate great person based on that information. This makes it possible to select the most appropriate great person according to the user's emotional state.

[0075] The question analysis unit can refer to the user's past question history and select the most relevant great person. For example, the question analysis unit analyzes the user's past question history and selects the most relevant great person based on the data. For example, if the user has asked many questions about business in the past, a great person in the business field is selected. The question analysis unit can also analyze the user's past question history and develop an algorithm for selecting the most relevant great person based on the data. For example, a model for selecting the most relevant great person based on the user's past question history is constructed. The question analysis unit can also refer to the user's past question history and build a system for selecting the most relevant great person based on the data. For example, a database for selecting the most relevant great person based on the user's past question history is constructed. This makes it possible to select the most relevant great person based on the user's past question history.

[0076] The question analysis unit can provide a more multifaceted perspective by taking into account not only the great person's field of expertise but also their personal interests and hobbies. For example, the question analysis unit can analyze not only the great person's field of expertise but also their personal interests and hobbies, and select a great person based on that information. For example, the question analysis unit can generate answers from a more multifaceted perspective by taking into account the artistic hobbies of a great person in the business field. The question analysis unit can also develop an algorithm for generating answers by taking into account the great person's field of expertise and personal interests and hobbies. For example, it can build a model for generating answers based on the great person's field of expertise and personal interests and hobbies. The question analysis unit can also build a system for generating answers by taking into account the great person's field of expertise and personal interests and hobbies. For example, it can build a database for generating answers based on the great person's field of expertise and personal interests and hobbies. This makes it possible to generate answers from a multifaceted perspective by taking into account the great person's field of expertise and personal interests and hobbies.

[0077] The question analysis unit can combine great people from different fields to generate answers from a crossover perspective. For example, the question analysis unit can integrate the thoughts of great people from different fields to generate answers from a crossover perspective. For example, the question analysis unit can integrate the thoughts of great people from business and art to generate answers from a composite perspective. The question analysis unit can also develop an algorithm for generating answers by combining great people from different fields. For example, the question analysis unit can integrate the thoughts of great people from business and art and build a model for generating answers that reflect that perspective. The question analysis unit can also build a system for generating answers by combining great people from different fields. For example, the question analysis unit can integrate the thoughts of great people from business and art and build a database for generating answers that reflect that perspective. This makes it possible to generate answers from a crossover perspective that combines great people from different fields.

[0078] The question analysis unit can add a function to recommend other related great people based on the thoughts of a great person selected by a user. The question analysis unit, for example, analyzes the thoughts of the great person selected by the user and recommends other related great people based on that information. For example, if a user selects a great person in the business field, the question analysis unit can recommend related great people in other business fields. The question analysis unit can also develop an algorithm for recommending other great people based on the thoughts of the great person selected by the user. For example, the question analysis unit can analyze the thoughts of the great person selected by the user and build a model for recommending other great people based on that information. The question analysis unit can also build a system for recommending other great people based on the thoughts of the great person selected by the user. For example, the question analysis unit can analyze the thoughts of the great person selected by the user and build a database for recommending other great people based on that information. This makes it possible to recommend other related great people based on the thoughts of the great person selected by the user.

[0079] The question analysis unit can use the emotion estimation function to select a great person according to the user's emotions when selecting a great person for each field, and provide an answer that is easy to empathize with emotionally. The question analysis unit, for example, analyzes the user's emotional state and selects a great person who is easy to empathize with emotionally based on the information. For example, if the user is asking a question while feeling anxious, the question analysis unit selects a great person who gives a sense of security. The question analysis unit can also analyze the user's emotional state and develop an algorithm for selecting a great person who is easy to empathize with emotionally based on the information. For example, the question analysis unit can analyze the user's emotional state and build a model for selecting a great person who is easy to empathize with emotionally based on the information. The question analysis unit can also analyze the user's emotional state and build a system for selecting a great person who is easy to empathize with emotionally based on the information. For example, the question analysis unit can analyze the user's emotional state and build a database for selecting a great person who is easy to empathize with emotionally based on the information. This makes it possible to select a great person according to the user's emotions and provide an answer that is easy to empathize with emotionally.

[0080] The user interface incorporates an emotion estimation function and can change the interface design and color tone according to the user's emotional state. The user interface, for example, analyzes the user's emotional state and dynamically changes the interface design and color tone based on the information. For example, when the user is relaxed, the interface is displayed in a calm color tone. The user interface can also analyze the user's emotional state and develop an algorithm for changing the interface design and color tone based on the information. For example, the user interface can analyze the user's emotional state and build a model for changing the interface design and color tone based on the information. The user interface can also analyze the user's emotional state and build a system for changing the interface design and color tone based on the information. For example, the user interface can analyze the user's emotional state and build a database for changing the interface design and color tone based on the information. This makes it possible to dynamically change the interface design and color tone according to the user's emotional state.

[0081] The user interface may add a voice input function to allow a user to input questions by voice. The user interface may, for example, add a voice input function to allow a user to input questions by voice. For example, a microphone may be used to recognize the user's voice and convert the voice into text. The user interface may also add a voice input function and develop an algorithm to allow a user to input questions by voice. For example, a model may be built using voice recognition technology to convert the user's voice into text. The user interface may also add a voice input function and build a system to allow a user to input questions by voice. For example, a database may be built using voice recognition technology to convert the user's voice into text. This allows a user to input questions by voice.

[0082] The user interface can add a function that saves a user's question history and allows easy reference to past questions and answers. For example, the user interface adds a function that saves a user's question history and allows easy reference to past questions and answers. For example, it displays a list of questions that the user has previously asked and their answers. The user interface can also develop an algorithm that saves a user's question history and allows easy reference to past questions and answers. For example, it saves a user's question history and builds a model for referencing past questions and answers based on that data. The user interface also builds a system that saves a user's question history and allows easy reference to past questions and answers. For example, it saves a user's question history and builds a database for referencing past questions and answers based on that data. This allows the user to easily refer to past questions and answers.

[0083] The user interface can employ a responsive design that is compatible with different devices (smartphones, tablets, PCs). For example, the user interface can employ a responsive design that is compatible with different devices, allowing users to use the app comfortably on any device. For example, the display can be optimized for smartphones, tablets, and PCs. The user interface can also develop an algorithm to employ a responsive design that is compatible with different devices. For example, a model can be built to adjust the layout for each device. The user interface can also build a system to employ a responsive design that is compatible with different devices. For example, a database can be built to adjust the layout for each device. This allows the application to employ a responsive design that is compatible with different devices.

[0084] The user interface can provide themes and layouts that can be customized by users, thereby realizing an interface that suits individual preferences. The user interface, for example, can provide themes and layouts that can be customized by users, thereby realizing an interface that suits individual preferences. For example, a function can be added that allows colors, fonts, and layouts to be freely changed. The user interface can also develop an algorithm for providing themes and layouts that can be customized by users. For example, a model can be built for changing themes and layouts according to user preferences. The user interface can also build a system for providing themes and layouts that can be customized by users. For example, a database can be built for changing themes and layouts according to user preferences. This allows the user to provide themes and layouts that can be customized by users, thereby realizing an interface that suits individual preferences.

[0085] The user interface can use the emotion estimation function to add interactive animations and effects according to the user's emotions. For example, the user interface can use the emotion estimation function to add interactive animations and effects according to the user's emotional state. For example, if the user is happy, a positive animation can be displayed. The user interface can also use the emotion estimation function to develop an algorithm for adding interactive animations and effects according to the user's emotional state. For example, the user interface can analyze the user's emotional state and build a model for adding interactive animations and effects based on the information. The user interface can also use the emotion estimation function to build a system for adding interactive animations and effects according to the user's emotional state. For example, the user interface can analyze the user's emotional state and build a database for adding interactive animations and effects based on the information. This makes it possible to add interactive animations and effects according to the user's emotions.

[0086] The Great Person's Thoughts Incorporation Unit can also use the emotion estimation function to learn the emotional nuances of new literature and interviews. For example, when analyzing new literature or interviews, the Great Person's Thoughts Incorporation Unit uses the emotion estimation function to extract emotional nuances from the text and trains the generative AI. For example, it can analyze how a great person felt about a particular topic and generate answers that reflect those emotions. The Great Person's Thoughts Incorporation Unit can also develop algorithms for learning the emotional nuances of new literature and interviews using the emotion estimation function. For example, it can analyze new literature and interviews and build a model for extracting their emotional nuances. The Great Person's Thoughts Incorporation Unit can also build a system for learning the emotional nuances of new literature and interviews using the emotion estimation function. For example, it can analyze new literature and interviews and build a database for extracting their emotional nuances. This allows the system to learn the emotional nuances of new literature and interviews.

[0087] The Great Person's Thoughts Incorporation Unit can also analyze the social media posts and blog posts of great people and incorporate their latest thoughts and values. For example, the Great Person's Thoughts Incorporation Unit analyzes the social media posts and blog posts of great people and has the generation AI learn their content. For example, it analyzes what opinions the great people have on the latest topics and generates answers that reflect that content. The Great Person's Thoughts Incorporation Unit can also analyze the social media posts and blog posts of great people and develop algorithms to incorporate their latest thoughts and values. For example, it can analyze the social media posts and blog posts of great people and build a model to generate answers that reflect their content. The Great Person's Thoughts Incorporation Unit can also analyze the social media posts and blog posts of great people and build a system to incorporate their latest thoughts and values. For example, it can analyze the social media posts and blog posts of great people and build a database to generate answers that reflect their content. This makes it possible to analyze the social media posts and blog posts of great people and incorporate their latest thoughts and values.

[0088] The unit for capturing the thoughts of great people can track the evolution of the thoughts of great people over time and reflect the evolution of their thinking from the past to the present. For example, the unit for capturing the thoughts of great people can analyze documents and interviews of great people over time and track the evolution of their thinking. For example, the unit can analyze how a great person has changed their opinion on a particular topic and generate an answer that reflects those changes. The unit for capturing the thoughts of great people can also develop an algorithm for analyzing the evolution of the thoughts of great people over time and generating an answer that reflects those changes. For example, the unit can analyze the evolution of the thoughts of great people over time and build a model for generating an answer that reflects those changes. The unit for capturing the thoughts of great people can also analyze the evolution of the thoughts of great people over time and build a system for generating an answer that reflects those changes. For example, the unit can analyze the evolution of the thoughts of great people over time and build a database for generating an answer that reflects those changes. This makes it possible to track the evolution of the thoughts of great people over time and reflect the evolution of their thinking from the past to the present.

[0089] The Great Figures Thoughts Intake Unit can cross-reference the thoughts of great people from different fields to gain new insights. For example, the Great Figures Thoughts Intake Unit cross-references literature and interviews of great people from different fields and integrates their thoughts. For example, it can integrate the thoughts of great people from business and art to gain new insights. The Great Figures Thoughts Intake Unit can also develop algorithms for cross-referencing the thoughts of great people from different fields to gain new insights. For example, it can integrate the thoughts of great people from different fields and build a model for generating answers that reflect their perspectives. The Great Figures Thoughts Intake Unit can also build a system for cross-referencing the thoughts of great people from different fields to gain new insights. For example, it can build a database for integrating the thoughts of great people from different fields and generating answers that reflect their perspectives. This makes it possible to cross-reference the thoughts of great people from different fields to gain new insights.

[0090] The great person's thought incorporating unit can improve the great person's thought model based on feedback from the user. The great person's thought incorporating unit, for example, collects feedback from the user and improves the great person's thought model based on that data. For example, it analyzes feedback provided by the user and reflects it in the great person's thought model. The great person's thought incorporating unit can also develop an algorithm for improving the great person's thought model based on user feedback. For example, it analyzes user feedback and builds a model for improving the great person's thought model based on that information. The great person's thought incorporating unit also builds a system for improving the great person's thought model based on user feedback. For example, it collects user feedback and builds a database for improving the great person's thought model based on that information. In this way, the great person's thought model can be improved based on user feedback.

[0091] The great person's thought incorporating unit can use the emotion estimation function to provide the latest thoughts of great people that correspond to the user's emotions when updating the thoughts of great people. The great person's thought incorporating unit can, for example, use the emotion estimation function to provide the latest thoughts of great people that correspond to the user's emotional state. For example, if the user is in an anxious state and asking a question, the unit can provide the latest thoughts of great people that give a sense of security. The great person's thought incorporating unit can also use the emotion estimation function to develop an algorithm for providing the latest thoughts of great people that correspond to the user's emotional state. For example, the unit can analyze the user's emotional state and build a model for providing the latest thoughts of great people based on that information. The great person's thought incorporating unit can also use the emotion estimation function to build a system for providing the latest thoughts of great people that correspond to the user's emotional state. For example, the unit can analyze the user's emotional state and build a database for providing the latest thoughts of great people based on that information. This makes it possible to provide the latest thoughts of great people that correspond to the user's emotions.

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

[0093] The great person's thinking capture unit captures the thoughts of great people. For example, it analyzes the literature and interviews of great people and has the generation AI learn from them. The great person's thinking capture unit also analyzes the statements and behavioral patterns of great people and models their thinking. For example, the great person's books and interviews are input as text data, and the generation AI analyzes and learns from the content. The question analysis unit analyzes the user's question. For example, it uses natural language processing technology to analyze the question entered by the user and understands its content. The question analysis unit can also analyze the intent and background of the user's question. For example, if a user asks, "What is the secret to business success?", the question analysis unit analyzes the intent of the question and extracts information to generate an appropriate answer. The answer generation unit generates an answer based on the question analyzed by the question analysis unit. For example, the generation AI generates an answer based on the thoughts of great people. The answer generation unit also provides the answer generated by the generation AI to the user. For example, the generation AI generates an answer such as, "The secret to business success is to take risks and challenge yourself" and displays it to the user. As a result, the generative AI app according to the embodiment can provide answers that incorporate the thoughts of great people. For example, if a user asks a question about business, the generative AI generates an answer based on the thoughts of a famous CEO and provides it to the user. Also, if a user asks a question about art, the generative AI generates an answer based on the thoughts of a famous author or film director and provides it to the user.

[0094] The Great Person's Thoughts Capture Unit can also learn the emotional nuances of great people using the emotion estimation function. For example, when analyzing documents or interviews about great people, the Great Person's Thoughts Capture Unit uses the emotion estimation function to extract emotional nuances from the text and has the generation AI learn them. For example, it can analyze how great people felt about a particular topic and generate answers that reflect those emotions. The Great Person's Thoughts Capture Unit can also use the emotion estimation function to analyze the emotional tone of a great person's statements and generate answers that reflect that tone. For example, for a topic that a great person spoke about with passion, the generation AI will generate answers that reflect that passion. This makes it possible to generate answers that reflect the emotional nuances of great people.

[0095] The great person's thought capturing unit can also analyze the great person's audio data or video data and learn the deepest levels of their thoughts from the tone and facial expressions of their speech. The great person's thought capturing unit, for example, analyzes the great person's audio data and learns the tone and rhythm of their speech. For example, it analyzes the tone and rhythm of the great person when they speak about a specific topic and generates answers that reflect those characteristics. The great person's thought capturing unit can also analyze the great person's video data and learn their facial expressions when they speak. For example, it can analyze the facial expressions of the great person when they speak about a specific topic and generate answers that reflect those facial expressions. The great person's thought capturing unit can also analyze the audio data and video data in combination and learn the tone and facial expressions of their speech comprehensively. For example, it analyzes the tone and facial expressions of the great person when they speak about a specific topic and generates answers that reflect those characteristics. This makes it possible to generate answers that reflect the tone and facial expressions of the great person's speech.

[0096] The great person's thinking assimilation unit simultaneously learns other great people and literature that influenced the great person, allowing for a deep understanding of the background and context of their thinking. For example, the great person's thinking assimilation unit analyzes literature and interviews of other great people who influenced the great person, and learns the background and context of their thinking. For example, it analyzes how the great person was influenced by a specific topic, and generates answers that reflect that background. The great person's thinking assimilation unit also analyzes literature that influenced the great person, and learns its content. For example, it can analyze how the great person was influenced by a specific book, and generate answers that reflect that content. The great person's thinking assimilation unit also analyzes the statements and actions of other great people who influenced the great person, and learns the background of their thinking. For example, it can analyze how the great person was influenced by a specific great person, and generate answers that reflect that background. This makes it possible to generate answers that have a deep understanding of the background and context of the great person's thinking.

[0097] The great person's thinking incorporating unit can incorporate the thoughts of great people from different cultural spheres and eras, and generate answers from a global perspective. The great person's thinking incorporating unit, for example, analyzes documents and interviews of great people from different cultural spheres and learns their thoughts. For example, it can integrate the thoughts of great people from the East and the West and generate answers from a global perspective. The great person's thinking incorporating unit can also analyze documents and interviews of great people from different eras and learns their thoughts. For example, it can integrate the thoughts of great people from ancient and modern times and generate answers from a timeless perspective. The great person's thinking incorporating unit can also comprehensively analyze the thoughts of great people from different cultural spheres and eras and generate answers from a global perspective. For example, it can integrate the thoughts of great people from different cultural spheres and eras and generate answers that reflect their perspectives. This makes it possible to generate answers from a global perspective.

[0098] The unit for incorporating the thoughts of great people can classify the thoughts of great people by theme and generate answers specialized for that particular theme. For example, the unit for incorporating the thoughts of great people can classify documents and interviews of great people by theme and learn thoughts specialized for that theme. For example, the unit for incorporating the thoughts of great people can classify the thoughts of great people by theme, such as business, art, and science, and generate answers specialized for that theme. The unit for incorporating the thoughts of great people can also classify the statements and actions of great people by theme and learn thoughts specialized for that theme. For example, the unit can analyze how great people thought about a particular theme and generate answers specialized for that theme. The unit for incorporating the thoughts of great people can also develop an algorithm for classifying the thoughts of great people by theme and generating answers specialized for that theme. For example, the unit can classify the thoughts of great people by theme and build a model for generating answers specialized for that theme. This makes it possible to generate answers specialized for a particular theme.

[0099] The great person's thought assimilation unit can use the emotion estimation function to track the great person's emotional changes and reflect changes in their thoughts over time. The great person's thought assimilation unit, for example, analyzes documents and interviews about the great person and uses the emotion estimation function to track the great person's emotional changes. For example, it analyzes the emotions the great person had at a specific time and generates answers that reflect those emotions. The great person's thought assimilation unit can also analyze the great person's statements and actions over time and learn the emotional changes. For example, it analyzes the emotions the great person had at a specific time and generates answers that reflect those emotions. The great person's thought assimilation unit can also analyze the great person's emotional changes over time and develop an algorithm for generating answers that reflect those changes. For example, it can analyze the great person's emotional changes over time and build a model for generating answers that reflect those changes. This makes it possible to generate answers that reflect the great person's emotional changes.

[0100] The question analysis unit can analyze the user's emotional state using the emotion estimation function and generate an answer in an optimal tone. The question analysis unit, for example, analyzes the user's question and identifies the user's emotional state using the emotion estimation function. For example, it analyzes the user's facial expression and voice when entering a question and generates an answer in an optimal tone based on that emotional state. The question analysis unit can also analyze the user's emotional state and generate an answer in an optimal tone based on that information. For example, if the user is asking a question in an anxious state, it generates an answer in a tone that gives a sense of security. The question analysis unit can also analyze the user's emotional state using the emotion estimation function and develop an algorithm for generating an answer in an optimal tone based on that information. For example, it analyzes the user's emotional state and builds a model for generating an answer in an optimal tone based on that information. This makes it possible to generate an answer in an optimal tone according to the user's emotional state.

[0101] The question analysis unit can generate more accurate answers by referring to data on questions and answers from past users. The question analysis unit, for example, analyzes data on questions and answers from past users and generates answers based on that data. For example, it references past answers to similar questions to generate more accurate answers. The question analysis unit can also analyze data on questions and answers from past users and develop an algorithm for generating answers based on that data. For example, it builds a model for generating answers based on past data. The question analysis unit also builds a system for generating answers based on that data by referring to data on questions and answers from past users. For example, it builds a database for generating answers based on past data and generates answers by referring to that data. This makes it possible to generate highly accurate answers based on past data.

[0102] The question analysis unit enables the generative AI to generate answers by combining the thoughts of multiple great people, thereby providing a composite perspective. The question analysis unit, for example, integrates the thoughts of multiple great people and generates answers from a composite perspective. For example, it integrates the thoughts of great people in business and art to generate answers from a composite perspective. The question analysis unit can also develop an algorithm for generating answers by combining the thoughts of multiple great people. For example, it integrates the thoughts of great people in business and art and builds a model for generating answers that reflect that perspective. The question analysis unit also builds a system for generating answers by combining the thoughts of multiple great people. For example, it integrates the thoughts of great people in business and art and builds a database for generating answers that reflect that perspective. This makes it possible to generate answers from a composite perspective that combines the thoughts of multiple great people.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The Great Person's Thought Acquisition Unit acquires the thoughts of great people. For example, it analyzes documents and interviews of great people and has the generation AI learn from them. It also analyzes the great person's statements and behavioral patterns and models their thoughts. Specifically, the great person's books and interviews are input as text data, and the generation AI analyzes and learns from the content. Step 2: The question analysis unit analyzes the user's question. For example, it uses natural language processing technology to analyze the question entered by the user and understand its content. It can also analyze the intention and background of the user's question. For example, if a user asks, "What is the secret to business success?", the intent of the question is analyzed and information is extracted to generate an appropriate answer. Step 3: The answer generation unit generates an answer based on the question analyzed by the question analysis unit. For example, the generation AI generates an answer based on the thoughts of great people. The answer generated by the generation AI is then provided to the user. Specifically, the generation AI generates an answer such as "The secret to business success is to take on challenges without fear of risk" and displays it to the user.

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

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0149] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The Great People's Thoughts Intake Department, which incorporates the thoughts of great people, a question analysis unit that analyzes a user's question; an answer generation unit that generates an answer based on the question analyzed by the question analysis unit; A system characterized by:

2. The great man's thoughts intake section Generate answers from a global perspective by incorporating the thoughts of great people from different cultures and eras 2. The system of claim 1.

3. The question analysis unit Analyze the user's emotional state and generate responses in the most appropriate tone 2. The system of claim 1.

4. The question analysis unit Analyze the user's emotional state and select the most suitable great person 2. The system of claim 1.

5. The user interface is Incorporating emotion estimation functionality to change the interface design and color tone according to the user's emotional state 2. The system of claim 1.

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