System

Abstract

To provide a system that provides an answer on the basis of the knowledge and experience of a specific person in real time.SOLUTION: A system includes a collection section, an analysis section, and a generation section. The collection section collects data on the specific person to cause the AI to learn. The analysis section analyzes a question or consultation from a user on the basis of the data collected by the collection section. The generation section outputs the answer generated by the analysis section as a video or a voice.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have the problem that it is difficult to provide answers based on a specific person's knowledge and experience in real time.

[0005] The system according to the embodiment aims to provide answers based on the knowledge and experience of a particular person in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects data on a specific person and allows an AI to learn the data. The analysis unit analyzes questions and inquiries from users based on the data collected by the collection unit and generates answers. The generation unit outputs the answers generated by the analysis unit as video and audio. Effect of the Invention

[0007] The system according to the embodiment can provide answers based on the knowledge and experience of a particular person in real time. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the 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 specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The system according to the embodiment of the present invention is a system for developing an AI that has learned the words and deeds, guidelines, life philosophies, and past books of a specific person (for example, a famous businessperson). This system can generate video and audio that sounds like a specific person is actually speaking in response to a question or consultation from a user. For example, the system first collects data such as the words and deeds, guidelines, life philosophies, and past books of a specific person, and makes the AI ​​learn from them. At this time, the system analyzes the patterns of the specific person's remarks and actions in detail so that the AI ​​can imitate them. For example, by collecting interview videos and lecture recordings of a specific person and making the AI ​​learn from them, it is possible to reproduce the specific person's way of speaking and expression. Next, the system accepts questions and consultations from users as input. For example, a question such as "What is the secret to business success?" is input. This input is made to the AI. The AI ​​analyzes the input question or consultation, and generates an appropriate answer based on the words and deeds and guidelines of the specific person. For example, it generates an answer about the secret to business success by referring to the specific person's past remarks and the contents of his / her books. The generated answer is output as video and audio that sounds like the specific person is actually speaking. For example, based on the answer generated by AI, a video of a specific person is generated, and the video answers the user. In this case, by reproducing the specific person's way of speaking and expression, the user can have an experience as if they were actually talking to the specific person. This mechanism allows the user to directly learn the knowledge and experience of a specific person and receive business and life advice. For example, a young entrepreneur can get specific guidelines for business success by receiving advice from a specific person. In addition, ordinary people can get hints for self-development and growth by learning the life philosophy and code of conduct of a specific person. This allows the system to generate answers to the user's questions and inquiries in the form of video and audio that sound like a specific person.

[0029] The system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects data of a specific person and makes the AI ​​learn. The data of a specific person includes, for example, interview footage, lecture recordings, and book contents, but is not limited to such examples. The collection unit, for example, collects interview footage and makes the AI ​​learn. The collection unit can also collect lecture recordings and make the AI ​​learn. Furthermore, the collection unit can collect book contents and make the AI ​​learn. For example, the collection unit collects interview footage in high resolution and makes the AI ​​learn. The lecture recordings are collected as audio data and made to learn by the AI. The book contents are collected as text data and made to learn by the AI. The analysis unit analyzes questions and consultations from users based on the data collected by the collection unit and generates appropriate answers. The analysis unit analyzes user questions using, for example, natural language processing technology. The analysis unit can also analyze questions using machine learning algorithms. Furthermore, the analysis unit can perform analysis based on past question data. For example, the analysis unit uses natural language processing technology to perform morphological analysis of the user's question and analyze the meaning. The machine learning algorithm learns past question data and generates an appropriate answer to a new question. The generation unit outputs the answer generated by the analysis unit as video and audio. The generation unit generates video, for example, using deep fake technology. The generation unit can also generate audio using voice synthesis technology. Furthermore, the generation unit can generate video and audio in real time. For example, the generation unit generates video of a specific person using deep fake technology. Voice synthesis technology generates audio by imitating the voice of a specific person. Real-time generation can instantly generate video and audio in response to a user's question. As a result, the system according to the embodiment can generate an answer to a user's question or consultation with video and audio that sounds like a specific person.

[0030] The collection unit collects data of a specific person and makes the AI ​​learn. The data of a specific person includes, for example, interview footage, lecture recordings, and book contents, but is not limited to such examples. The collection unit, for example, collects interview footage and makes the AI ​​learn. The collection unit can also collect lecture recordings and make the AI ​​learn. Furthermore, the collection unit can collect book contents and make the AI ​​learn. For example, the collection unit collects interview footage in high resolution and makes the AI ​​learn. The lecture recordings are collected as audio data and made to learn by the AI. The book contents are collected as text data and made to learn by the AI. The collection unit makes full use of various technologies to efficiently collect these data. For example, the interview footage can be collected in high resolution using video analysis technology, and important scenes in the video can be automatically extracted. The lecture recordings can be converted into text using voice recognition technology to make the contents easier to analyze. The book contents can be extracted from paper books using OCR (optical character recognition) technology and digitized as text data. As a result, the collection unit can centrally manage data in various formats and efficiently train the AI. The collection unit also pre-processes the collected data to ensure the quality of the data. For example, it performs noise removal and resolution improvement of video data, noise removal and volume adjustment of audio data, and correction of typos and omissions in text data. This improves the quality of the data that the AI ​​learns from, enabling more accurate analysis and generation. Furthermore, the collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and range of data collection. For example, when a specific event or lecture is held, data can be collected in real time and the AI ​​can learn immediately. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system.

[0031] The analysis unit analyzes questions and consultations from users based on the data collected by the collection unit, and generates appropriate answers. The analysis unit, for example, uses natural language processing technology to analyze user questions. The analysis unit can also analyze questions using machine learning algorithms. Furthermore, the analysis unit can perform analysis based on past question data. For example, the analysis unit uses natural language processing technology to perform morphological analysis of user questions and analyze their meaning. The machine learning algorithm learns past question data and generates appropriate answers to new questions. The analysis unit combines these technologies to achieve more accurate analysis. Specifically, the analysis unit uses natural language processing technology to perform morphological analysis of user questions and understand the context and intent. Next, the analysis unit uses a machine learning algorithm to derive optimal answers while checking against past question data. Furthermore, the analysis unit continuously learns to improve the accuracy of answers to user questions. For example, by collecting new question data and answer data and regularly updating the model, analysis based on the latest information is always possible. The analysis unit can also use an anomaly detection algorithm to detect unusual patterns and abnormal questions and take appropriate measures. This allows the analysis unit to respond quickly and accurately to various questions and inquiries from users. Furthermore, the analysis unit can collect user feedback and continuously improve the accuracy and effectiveness of the analysis results. For example, based on feedback provided by the user, the parameters of the analysis algorithm are adjusted to generate more appropriate answers. Furthermore, the analysis unit can improve the accuracy of the analysis by combining multiple analysis methods. For example, by combining natural language processing technology and machine learning algorithms, it is possible to more accurately understand the intention of the user's question and generate appropriate answers. This allows the analysis unit to respond quickly and accurately to user questions and inquiries, and improve the reliability and effectiveness of the entire system.

[0032] The generation unit outputs the answer generated by the analysis unit as video and audio. The generation unit generates video using, for example, deep fake technology. The generation unit can also generate audio using voice synthesis technology. Furthermore, the generation unit can generate video and audio in real time. For example, the generation unit generates video of a specific person using deep fake technology. Voice synthesis technology generates audio by imitating the voice of a specific person. Real-time generation can instantly generate video and audio in response to a user's question. The generation unit makes full use of these technologies to provide a realistic answer to the user. Specifically, deep fake technology is used to realistically reproduce the facial expressions and mouth movements of a specific person, providing a natural video to the user. Voice synthesis technology learns the characteristics of a specific person's voice and reproduces natural intonation and intonation to provide a realistic voice to the user. Furthermore, the generation unit has high-speed processing capabilities in order to instantly generate video and audio in response to a user's question. For example, deep fake technology is executed in real time using a high-performance GPU, and video is instantly generated in response to a user's question. In addition, the voice synthesis technology also has high-speed processing capabilities and can instantly generate voice in response to a user's question. This allows the generation unit to provide a quick and natural answer to the user. Furthermore, the generation unit continuously improves the technology to improve the quality of the generated video and audio. For example, new deep fake technology and voice synthesis technology are introduced to improve the quality of the generated video and audio. In addition, based on user feedback, the parameters of the generation algorithm can be adjusted to generate more natural video and audio. This allows the generation unit to provide the user with high-quality video and audio, improving the reliability and effectiveness of the entire system.

[0033] The collection unit can collect data of interview footage or lecture recordings of a specific person. The collection unit, for example, collects interview footage of a specific person. The interview footage includes, for example, television interviews, online interviews, etc., but is not limited to such examples. The collection unit, for example, collects television interviews in high resolution and makes the AI ​​learn. The collection unit can also collect online interviews and make the AI ​​learn. Furthermore, the collection unit can collect lecture recordings of a specific person. The lecture recordings include, for example, academic conference lectures, seminar lectures, etc., but are not limited to such examples. The collection unit, for example, collects academic conference lectures as audio data and makes the AI ​​learn. The collection unit can also collect seminar lectures and make the AI ​​learn. As a result, the collection unit can collect interview footage and lecture recordings of a specific person, so that the AI ​​can more accurately imitate the words and actions of a specific person.

[0034] The analysis unit can analyze the user's question using natural language processing technology. The analysis unit, for example, analyzes the user's question using natural language processing technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, and the like, but is not limited to such examples. The analysis unit, for example, analyzes the user's question using morphological analysis. The analysis unit can also analyze the question using grammatical analysis. Furthermore, the analysis unit can analyze the question using semantic analysis. For example, the analysis unit breaks down the user's question into words and analyzes the meaning using morphological analysis. The grammatical analysis analyzes the grammatical structure of the question and understands the meaning. The semantic analysis analyzes the context of the question and generates an appropriate answer. As a result, the analysis unit can accurately analyze the user's question and generate an appropriate answer by using natural language processing technology.

[0035] The generation unit can generate video or audio in real time using deep fake technology or voice synthesis technology. The generation unit generates video using, for example, deep fake technology. Deep fake technology includes, for example, face synthesis, voice synthesis, etc., but is not limited to such examples. The generation unit generates video of a specific person using, for example, face synthesis. The generation unit can also generate audio using voice synthesis technology. Voice synthesis technology includes, for example, text voice synthesis, voice imitation, etc., but is not limited to such examples. The generation unit generates the voice of a specific person using, for example, text voice synthesis. The generation unit can also generate the voice of a specific person using voice imitation. Furthermore, the generation unit can generate video and audio in real time. For example, the generation unit uses deep fake technology to instantly generate video of a specific person in response to a user's question. The voice synthesis technology instantly generates the voice of a specific person in response to a user's question. As a result, the generation unit can generate video and audio like a specific person in real time by using deep fake technology or voice synthesis technology.

[0036] The collection unit can analyze the background and context of a statement made by a specific person and determine the priority of data to be collected. The collection unit, for example, analyzes the background and context of a statement made by a specific person and determines the priority of data to be collected. The analysis of the background and context includes, for example, previous and following statements, related topics, and the like, but is not limited to such examples. The collection unit, for example, analyzes the previous and following statements and preferentially collects the contents of a statement made by a specific person in a specific business scene. In addition, the collection unit can analyze related topics and, if a statement made by a specific person is related to a specific time or event, analyze and collect the background information. Furthermore, if a statement made by a specific person is related to a specific theme, the collection unit can also preferentially collect data related to the theme. For example, the collection unit preferentially collects the contents of a statement made by a specific person in a specific business scene. In addition, if a statement made by a specific person is related to a specific time or event, the collection unit can also analyze and collect the background information. Furthermore, if a statement made by a specific person is related to a specific theme, the collection unit can also preferentially collect data related to the theme. This enables the collection unit to prioritize the collection of more important data by analyzing the background and context of comments.

[0037] The collection unit can adjust the level of detail of the data to be collected based on the frequency and importance of the utterances of a specific person. The collection unit adjusts the level of detail of the data to be collected based on, for example, the frequency and importance of the utterances of a specific person. The frequency of utterances includes, for example, the number of occurrences, time intervals, and the like, but is not limited to such examples. For example, the collection unit collects detailed data for a theme that is frequently mentioned by a specific person. In addition, the collection unit can also collect data including detailed background information for important utterances and famous quotes of a specific person. Furthermore, if the utterances of a specific person are considered important in a specific field, the collection unit can also collect detailed data related to that field. For example, the collection unit collects detailed data for a theme that is frequently mentioned by a specific person. In addition, the collection unit can also collect data including detailed background information for important utterances and famous quotes of a specific person. Furthermore, if the utterances of a specific person are considered important in a specific field, the collection unit can also collect detailed data related to that field. As a result, the collection unit can collect more accurate data by adjusting the level of detail of the data based on the frequency and importance of the utterances.

[0038] The collection unit can select data to be collected in consideration of the geographical background of the statement of a specific person. The collection unit selects data to be collected in consideration of the geographical background of the statement of a specific person, for example. The geographical background includes, for example, a culture specific to a region, a geographical condition, and the like, but is not limited to such examples. The collection unit, for example, collects lectures and interviews given by a specific person in a specific region. In addition, when the statement of a specific person is related to a specific country or region, the collection unit can also collect data related to the region. Furthermore, when the statement of a specific person is related to a specific culture or social background, the collection unit can also collect data including the background information. For example, the collection unit collects lectures and interviews given by a specific person in a specific region. In addition, when the statement of a specific person is related to a specific country or region, the collection unit can also collect data related to the region. Furthermore, when the statement of a specific person is related to a specific culture or social background, the collection unit can also collect data including the background information. In this way, the collection unit can collect more relevant data by considering the geographical background.

[0039] The collection unit can also collect statements of other celebrities related to the statement of the specific person and perform a comparative analysis. The collection unit, for example, collects statements of other celebrities related to the statement of the specific person and performs a comparative analysis. Other celebrities include, for example, celebrities in the same industry, experts in related fields, etc., but are not limited to such examples. The collection unit, for example, collects statements of celebrities from the same era as the specific person and performs a comparative analysis. The collection unit can also collect statements of experts in related fields with the statement of the specific person and perform a comparative analysis. Furthermore, the collection unit can collect statements of celebrities with a different perspective from the statement of the specific person and perform a comparative analysis. For example, the collection unit collects statements of celebrities from the same era as the specific person and performs a comparative analysis. The collection unit can also collect statements of experts in related fields with the statement of the specific person and perform a comparative analysis. Furthermore, the collection unit can collect statements of celebrities with a different perspective from the statement of the specific person and perform a comparative analysis. As a result, the collection unit can provide a more multifaceted perspective by comparing and analyzing statements of other celebrities.

[0040] The analysis unit can analyze background information of a question and apply an analysis method for generating a more specific answer. The analysis unit, for example, analyzes background information of a question and applies an analysis method for generating a more specific answer. The background information includes, for example, past questions, related topics, and the like, but is not limited to such examples. For example, the analysis unit analyzes past questions, and if the question is related to a specific business scene, analyzes the background information to generate a specific answer. In addition, the analysis unit can analyze related topics, and if the question is related to a specific time or event, analyze the background information to generate a specific answer. In addition, the analysis unit can analyze past questions, and if the question is related to a specific theme, analyze the background information to generate a specific answer. For example, the analysis unit analyzes past questions, and if the question is related to a specific business scene, analyzes the background information to generate a specific answer. In addition, the analysis unit can analyze related topics, and if the question is related to a specific time or event, analyze the background information to generate a specific answer. In addition, the analysis unit can analyze past questions, and if the question is related to a specific topic, the analysis unit can analyze the background information to generate a specific answer. This allows the analysis unit to generate a more specific and appropriate answer by analyzing the background information of the question.

[0041] The analysis unit can apply different analysis algorithms depending on the category of the question to improve the accuracy of the answer. The analysis unit, for example, applies different analysis algorithms depending on the category of the question to improve the accuracy of the answer. The question categories include, for example, technical questions, questions related to business, and the like, but are not limited to such examples. For example, the analysis unit applies a technical analysis algorithm to technical questions. Furthermore, the analysis unit can also apply an analysis algorithm specialized for business strategies to questions related to business. Furthermore, the analysis unit can apply an analysis algorithm including philosophical insights to questions related to life philosophy. For example, the analysis unit applies a technical analysis algorithm to technical questions. Furthermore, the analysis unit can apply an analysis algorithm specialized for business strategies to questions related to business. Furthermore, the analysis unit can apply an analysis algorithm including philosophical insights to questions related to life philosophy. As a result, the analysis unit applies an analysis algorithm according to the category of the question to improve the accuracy of the answer.

[0042] The analysis unit can determine the priority of the analysis based on the time when the question is submitted. The analysis unit determines the priority of the analysis based on, for example, the time when the question is submitted. The submission time includes, for example, the submission date and time, the urgency, and the like, but is not limited to such examples. The analysis unit determines the priority of the question based on, for example, the submission date and time. The analysis unit can also determine the priority of the question based on the urgency. Furthermore, the analysis unit can determine the priority of the question related to a specific event or time. For example, the analysis unit determines the priority of the question based on the submission date and time. The analysis unit can also determine the priority of the question based on the urgency. Furthermore, the analysis unit can determine the priority of the question related to a specific event or time. In this way, the analysis unit can quickly respond to questions with high urgency by determining the priority of the analysis based on the time when the question is submitted.

[0043] The analysis unit can adjust the order of analysis based on the relevance of the questions. The analysis unit adjusts the order of analysis based on, for example, the relevance of the questions. The relevance includes, for example, the degree of matching of the topic, the relevance to past questions, and the like, but is not limited to such examples. The analysis unit adjusts the order of questions based on, for example, the degree of matching of the topic. The analysis unit can also adjust the order of questions based on the relevance to past questions. Furthermore, the analysis unit can adjust the order of questions related to a specific theme. For example, the analysis unit adjusts the order of questions based on the degree of matching of the topic. The analysis unit can also adjust the order of questions based on the relevance to past questions. Furthermore, the analysis unit can adjust the order of questions related to a specific theme. In this way, the analysis unit can provide consistent answers to related questions by adjusting the order of analysis based on the relevance of the questions.

[0044] The generation unit can analyze the tone and pace of a specific person's speech and reflect them in the video and audio to be generated. The generation unit can, for example, analyze the tone and pace of a specific person's speech and reflect them in the video and audio to be generated. The analysis of tone and pace includes, for example, voice analysis and rhythm analysis, but is not limited to such examples. The generation unit can, for example, analyze the tone of a specific person's speech using voice analysis. The generation unit can also analyze the pace of a specific person's speech using rhythm analysis. Furthermore, the generation unit can analyze the tone and pace of a specific person's speech by combining voice analysis and rhythm analysis. For example, when a specific person speaks in a gentle tone, the generation unit can generate video and audio that reproduce the tone using voice analysis. Furthermore, when a specific person speaks quickly, the generation unit can generate video and audio that reproduce the pace using rhythm analysis. Furthermore, the generation unit can combine voice analysis and rhythm analysis to generate video and audio that reproduce the emphasized part when a specific person speaks with emphasis. This enables the generation unit to generate more realistic images and audio by reflecting the tone and pace of a specific person's speech.

[0045] The generation unit can adjust the level of detail of the video and audio to be generated based on the context of the specific person's utterance. The generation unit adjusts the level of detail of the video and audio to be generated based on, for example, the context of the specific person's utterance. The analysis of the context includes, for example, previous and following utterances, related topics, etc., but is not limited to such examples. For example, the generation unit analyzes the previous and following utterances, and when the specific person's utterance includes a specific example, generates video and audio that reproduces the example in detail. In addition, the generation unit can also analyze related topics, and when the specific person's utterance includes an abstract concept, generate video and audio that visually expresses the concept. Furthermore, the generation unit can combine the previous and following utterances with related topics, and when the specific person's utterance includes an emotional element, generate video and audio that emphasizes the emotion. For example, the generation unit analyzes the previous and following utterances, and when the specific person's utterance includes a specific example, generates video and audio that reproduces the example in detail. In addition, the generation unit can analyze related topics, and when the specific person's utterance includes an abstract concept, generate video and audio that visually expresses the concept. Furthermore, the generator can combine previous and subsequent utterances and related topics, and if a particular person's utterance contains an emotional element, generate video and audio that emphasizes that emotion. This allows the generator to generate more appropriate video and audio by adjusting the level of detail based on the context of the utterance.

[0046] The generation unit can customize the generated video and audio in consideration of the geographical background of the speech of a specific person. The generation unit customizes the generated video and audio in consideration of the geographical background of the speech of a specific person, for example. The geographical background includes, for example, a culture specific to a region, a geographical condition, and the like, but is not limited to such examples. The generation unit generates, for example, video and audio reproducing a lecture given by a specific person in a specific region. In addition, when the speech of a specific person is related to a specific country or region, the generation unit can also generate video and audio including the background of the region. Furthermore, when the speech of a specific person is related to a specific culture or social background, the generation unit can also generate video and audio including the background information of the region. For example, the generation unit generates video and audio reproducing a lecture given by a specific person in a specific region. In addition, when the speech of a specific person is related to a specific country or region, the generation unit can also generate video and audio including the background of the region. Furthermore, when the speech of a specific person is related to a specific culture or social background, the generation unit can also generate video and audio including the background information of the region. This allows the generation unit to generate more relevant video and audio by taking the geographical background into account.

[0047] The generation unit can also generate statements of other celebrities related to the statement of the specific person and perform a comparative display. The generation unit can, for example, generate statements of other celebrities related to the statement of the specific person and perform a comparative display. The other celebrities include, for example, celebrities in the same industry, experts in related fields, and the like, but are not limited to such examples. The generation unit, for example, reproduces statements of celebrities from the same era as the specific person and generates video and audio for comparative display. The generation unit can also reproduce statements of experts in related fields with the statement of the specific person and generate video and audio for comparative display. Furthermore, the generation unit can also reproduce statements of celebrities with a different perspective from the statement of the specific person and generate video and audio for comparative display. For example, the generation unit reproduces statements of celebrities from the same era as the specific person and generates video and audio for comparative display. The generation unit can also reproduce statements of experts in related fields with the statement of the specific person and generate video and audio for comparative display. Furthermore, the generation unit can also reproduce statements of celebrities with a different perspective from the statement of the specific person and generate video and audio for comparative display. This allows the generation unit to provide a more multifaceted perspective by comparing the statement with those of other famous people.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The collection unit can also customize the type of data to be collected based on the user's interests. For example, if the user is interested in a particular business field, the collection unit can prioritize collecting statements of a particular person and the contents of books related to that field. In addition, if the user is interested in self-development, data on a particular person's life philosophy and guidelines for action can also be collected. Furthermore, if the user is looking for information related to a particular time or event, the collection unit can also collect statements and activities of a particular person related to that time or event. This allows the collection unit to provide more personalized information by customizing the collection of data according to the user's interests.

[0050] The analysis unit can also generate more appropriate answers by analyzing the tendency of the user's questions based on the user's past question history. For example, if the user has asked many business-related questions in the past, business-related answers can be generated preferentially. Also, if the user has asked many questions about self-development, answers related to self-development can be generated. Furthermore, if the user has repeatedly asked questions about a specific topic, detailed answers related to that topic can be generated. In this way, the analysis unit can provide more appropriate and personalized answers by utilizing the user's question history.

[0051] The collection unit can also customize the data to be collected taking into account the geographical background of the user. For example, if the user lives in a particular area, it preferentially collects statements and activities of a particular person related to that area. In addition, if the user has a particular culture or social background, it can also collect data related to that background. Furthermore, if the user is interested in a particular country or area, it can also collect statements and activities of a particular person related to that country or area. In this way, the collection unit can provide more relevant information by customizing the collection of data according to the geographical background of the user.

[0052] The generation unit can also generate video and audio that matches the user's preferences based on the user's past question history. For example, if the user has asked many business-related questions in the past, business-related video and audio can be preferentially generated. Also, if the user has asked many questions about self-development, video and audio related to self-development can be generated. Furthermore, if the user has repeatedly asked questions about a specific topic, detailed video and audio related to that topic can be generated. In this way, the generation unit can provide more personalized video and audio by utilizing the user's question history.

[0053] The analysis unit can also customize the analysis method based on the category of the user's question. For example, a technical analysis method can be applied to a technical question. Also, an analysis method specialized for business strategies can be applied to a question about business. Furthermore, an analysis method including philosophical insights can be applied to a question about life philosophy. In this way, the analysis unit can provide a more accurate answer by applying an analysis method according to the question category.

[0054] The collection unit can also determine the priority of data to be collected based on the user's interests. For example, if the user is interested in a particular business field, the collection unit can prioritize the collection of statements by a particular person and the contents of books related to that field. In addition, if the user is interested in self-development, the collection unit can prioritize the collection of data related to a particular person's life philosophy and guidelines for action. Furthermore, if the user is looking for information related to a particular time or event, the collection unit can prioritize the collection of statements and activities of a particular person related to that time or event. This allows the collection unit to provide more personalized information by customizing the collection of data according to the user's interests.

[0055] The process flow of the first embodiment will be briefly described below.

[0056] Step 1: The collection unit collects data on a specific person (e.g., a specific person) and has the AI ​​learn from it. The collected data includes interview footage, lecture recordings, book contents, etc. The collection unit collects interview footage in high resolution and has the AI ​​learn from it. Lecture recordings are collected as audio data and have the AI ​​learn from it. Book contents are collected as text data and have the AI ​​learn from it. Step 2: The analysis unit analyzes the user's questions and inquiries based on the data collected by the collection unit, and generates appropriate answers. The analysis unit uses natural language processing technology and machine learning algorithms to analyze the user's questions, and can also perform analysis based on past question data. For example, natural language processing technology is used to perform morphological analysis of the user's questions and analyze their meaning. The machine learning algorithm learns from past question data and generates appropriate answers to new questions. Step 3: The generation unit outputs the answer generated by the analysis unit as video and audio. The generation unit can generate video using deepfake technology and generate audio using voice synthesis technology. Furthermore, the generation unit can also generate video and audio in real time. For example, a video of a specific person is generated using deepfake technology, and audio is generated by imitating the voice of a specific person using voice synthesis technology. Real-time generation can instantly generate video and audio in response to a user's question.

[0057] (Example 2) The system according to the embodiment of the present invention is a system for developing an AI that has learned the words and deeds, guidelines, life philosophies, and past books of a specific person (for example, a famous businessperson). This system can generate video and audio that sounds like a specific person is actually speaking in response to a question or consultation from a user. For example, the system first collects data such as the words and deeds, guidelines, life philosophies, and past books of a specific person, and makes the AI ​​learn from them. At this time, the system analyzes the patterns of the specific person's remarks and actions in detail so that the AI ​​can imitate them. For example, by collecting interview videos and lecture recordings of a specific person and making the AI ​​learn from them, it is possible to reproduce the specific person's way of speaking and expression. Next, the system accepts questions and consultations from users as input. For example, a question such as "What is the secret to business success?" is input. This input is made to the AI. The AI ​​analyzes the input question or consultation, and generates an appropriate answer based on the words and deeds and guidelines of the specific person. For example, it generates an answer about the secret to business success by referring to the specific person's past remarks and the contents of his / her books. The generated answer is output as video and audio that sounds like the specific person is actually speaking. For example, based on the answer generated by AI, a video of a specific person is generated, and the video answers the user. In this case, by reproducing the specific person's way of speaking and expression, the user can have an experience as if they were actually talking to the specific person. This mechanism allows the user to directly learn the knowledge and experience of a specific person and receive business and life advice. For example, a young entrepreneur can get specific guidelines for business success by receiving advice from a specific person. In addition, ordinary people can get hints for self-development and growth by learning the life philosophy and code of conduct of a specific person. This allows the system to generate answers to the user's questions and inquiries in the form of video and audio that sound like a specific person.

[0058] The system according to the embodiment includes a collection unit, an analysis unit, and a generation unit. The collection unit collects data of a specific person and makes the AI ​​learn. The data of a specific person includes, for example, interview footage, lecture recordings, and book contents, but is not limited to such examples. The collection unit, for example, collects interview footage and makes the AI ​​learn. The collection unit can also collect lecture recordings and make the AI ​​learn. Furthermore, the collection unit can collect book contents and make the AI ​​learn. For example, the collection unit collects interview footage in high resolution and makes the AI ​​learn. The lecture recordings are collected as audio data and made to learn by the AI. The book contents are collected as text data and made to learn by the AI. The analysis unit analyzes questions and consultations from users based on the data collected by the collection unit and generates appropriate answers. The analysis unit analyzes user questions using, for example, natural language processing technology. The analysis unit can also analyze questions using machine learning algorithms. Furthermore, the analysis unit can perform analysis based on past question data. For example, the analysis unit uses natural language processing technology to perform morphological analysis of the user's question and analyze the meaning. The machine learning algorithm learns past question data and generates an appropriate answer to a new question. The generation unit outputs the answer generated by the analysis unit as video and audio. The generation unit generates video, for example, using deep fake technology. The generation unit can also generate audio using voice synthesis technology. Furthermore, the generation unit can generate video and audio in real time. For example, the generation unit generates video of a specific person using deep fake technology. Voice synthesis technology generates audio by imitating the voice of a specific person. Real-time generation can instantly generate video and audio in response to a user's question. As a result, the system according to the embodiment can generate an answer to a user's question or consultation with video and audio that sounds like a specific person.

[0059] The collection unit collects data of a specific person and makes the AI ​​learn. The data of a specific person includes, for example, interview footage, lecture recordings, and book contents, but is not limited to such examples. The collection unit, for example, collects interview footage and makes the AI ​​learn. The collection unit can also collect lecture recordings and make the AI ​​learn. Furthermore, the collection unit can collect book contents and make the AI ​​learn. For example, the collection unit collects interview footage in high resolution and makes the AI ​​learn. The lecture recordings are collected as audio data and made to learn by the AI. The book contents are collected as text data and made to learn by the AI. The collection unit makes full use of various technologies to efficiently collect these data. For example, the interview footage can be collected in high resolution using video analysis technology, and important scenes in the video can be automatically extracted. The lecture recordings can be converted into text using voice recognition technology to make the contents easier to analyze. The book contents can be extracted from paper books using OCR (optical character recognition) technology and digitized as text data. As a result, the collection unit can centrally manage data in various formats and efficiently train the AI. The collection unit also pre-processes the collected data to ensure the quality of the data. For example, it performs noise removal and resolution improvement of video data, noise removal and volume adjustment of audio data, and correction of typos and omissions in text data. This improves the quality of the data that the AI ​​learns from, enabling more accurate analysis and generation. Furthermore, the collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and range of data collection. For example, when a specific event or lecture is held, data can be collected in real time and the AI ​​can learn immediately. This allows the collection unit to efficiently and effectively collect data and improve the performance of the entire system.

[0060] The analysis unit analyzes questions and consultations from users based on the data collected by the collection unit, and generates appropriate answers. The analysis unit, for example, uses natural language processing technology to analyze user questions. The analysis unit can also analyze questions using machine learning algorithms. Furthermore, the analysis unit can perform analysis based on past question data. For example, the analysis unit uses natural language processing technology to perform morphological analysis of user questions and analyze their meaning. The machine learning algorithm learns past question data and generates appropriate answers to new questions. The analysis unit combines these technologies to achieve more accurate analysis. Specifically, the analysis unit uses natural language processing technology to perform morphological analysis of user questions and understand the context and intent. Next, the analysis unit uses a machine learning algorithm to derive optimal answers while checking against past question data. Furthermore, the analysis unit continuously learns to improve the accuracy of answers to user questions. For example, by collecting new question data and answer data and regularly updating the model, analysis based on the latest information is always possible. The analysis unit can also use an anomaly detection algorithm to detect unusual patterns and abnormal questions and take appropriate measures. This allows the analysis unit to respond quickly and accurately to various questions and inquiries from users. Furthermore, the analysis unit can collect user feedback and continuously improve the accuracy and effectiveness of the analysis results. For example, based on feedback provided by the user, the parameters of the analysis algorithm are adjusted to generate more appropriate answers. Furthermore, the analysis unit can improve the accuracy of the analysis by combining multiple analysis methods. For example, by combining natural language processing technology and machine learning algorithms, it is possible to more accurately understand the intention of the user's question and generate appropriate answers. This allows the analysis unit to respond quickly and accurately to user questions and inquiries, and improve the reliability and effectiveness of the entire system.

[0061] The generation unit outputs the answer generated by the analysis unit as video and audio. The generation unit generates video using, for example, deep fake technology. The generation unit can also generate audio using voice synthesis technology. Furthermore, the generation unit can generate video and audio in real time. For example, the generation unit generates video of a specific person using deep fake technology. Voice synthesis technology generates audio by imitating the voice of a specific person. Real-time generation can instantly generate video and audio in response to a user's question. The generation unit makes full use of these technologies to provide a realistic answer to the user. Specifically, deep fake technology is used to realistically reproduce the facial expressions and mouth movements of a specific person, providing a natural video to the user. Voice synthesis technology learns the characteristics of a specific person's voice and reproduces natural intonation and intonation to provide a realistic voice to the user. Furthermore, the generation unit has high-speed processing capabilities in order to instantly generate video and audio in response to a user's question. For example, deep fake technology is executed in real time using a high-performance GPU, and video is instantly generated in response to a user's question. In addition, the voice synthesis technology also has high-speed processing capabilities and can instantly generate voice in response to a user's question. This allows the generation unit to provide a quick and natural answer to the user. Furthermore, the generation unit continuously improves the technology to improve the quality of the generated video and audio. For example, new deep fake technology and voice synthesis technology are introduced to improve the quality of the generated video and audio. In addition, based on user feedback, the parameters of the generation algorithm can be adjusted to generate more natural video and audio. This allows the generation unit to provide the user with high-quality video and audio, improving the reliability and effectiveness of the entire system.

[0062] The collection unit can collect data of interview footage or lecture recordings of a specific person. The collection unit, for example, collects interview footage of a specific person. The interview footage includes, for example, television interviews, online interviews, etc., but is not limited to such examples. The collection unit, for example, collects television interviews in high resolution and makes the AI ​​learn. The collection unit can also collect online interviews and make the AI ​​learn. Furthermore, the collection unit can collect lecture recordings of a specific person. The lecture recordings include, for example, academic conference lectures, seminar lectures, etc., but are not limited to such examples. The collection unit, for example, collects academic conference lectures as audio data and makes the AI ​​learn. The collection unit can also collect seminar lectures and make the AI ​​learn. As a result, the collection unit can collect interview footage and lecture recordings of a specific person, so that the AI ​​can more accurately imitate the words and actions of a specific person.

[0063] The analysis unit can analyze the user's question using natural language processing technology. The analysis unit, for example, analyzes the user's question using natural language processing technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, semantic analysis, and the like, but is not limited to such examples. The analysis unit, for example, analyzes the user's question using morphological analysis. The analysis unit can also analyze the question using grammatical analysis. Furthermore, the analysis unit can analyze the question using semantic analysis. For example, the analysis unit breaks down the user's question into words and analyzes the meaning using morphological analysis. The grammatical analysis analyzes the grammatical structure of the question and understands the meaning. The semantic analysis analyzes the context of the question and generates an appropriate answer. As a result, the analysis unit can accurately analyze the user's question and generate an appropriate answer by using natural language processing technology.

[0064] The generation unit can generate video or audio in real time using deep fake technology or voice synthesis technology. The generation unit generates video using, for example, deep fake technology. Deep fake technology includes, for example, face synthesis, voice synthesis, etc., but is not limited to such examples. The generation unit generates video of a specific person using, for example, face synthesis. The generation unit can also generate audio using voice synthesis technology. Voice synthesis technology includes, for example, text voice synthesis, voice imitation, etc., but is not limited to such examples. The generation unit generates the voice of a specific person using, for example, text voice synthesis. The generation unit can also generate the voice of a specific person using voice imitation. Furthermore, the generation unit can generate video and audio in real time. For example, the generation unit uses deep fake technology to instantly generate video of a specific person in response to a user's question. The voice synthesis technology instantly generates the voice of a specific person in response to a user's question. As a result, the generation unit can generate video and audio like a specific person in real time by using deep fake technology or voice synthesis technology.

[0065] The collection unit can estimate the user's emotion and adjust the type of data to be collected based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the type of data to be collected based on the estimated user's emotion. The estimation of emotion includes, for example, facial expression analysis, voice analysis, text analysis, and the like, but is not limited to such examples. The collection unit, for example, estimates the user's emotion using facial expression analysis. The collection unit can also estimate the user's emotion using voice analysis. Furthermore, the collection unit can estimate the user's emotion using text analysis. For example, when the user is feeling stressed, the collection unit uses facial expression analysis to preferentially collect interview videos and lecture recordings of specific people that can help the user relax. Furthermore, when the user wants to increase motivation, the collection unit can also use voice analysis to collect data containing success experiences and encouraging words. Furthermore, when the user is looking for specific advice, the collection unit can also use text analysis to collect data containing business strategies and practical advice. As a result, the collection unit can collect more appropriate data by adjusting the type of data to be collected according to the user's emotion.

[0066] The collection unit can analyze the background and context of a statement made by a specific person and determine the priority of data to be collected. The collection unit, for example, analyzes the background and context of a statement made by a specific person and determines the priority of data to be collected. The analysis of the background and context includes, for example, previous and following statements, related topics, and the like, but is not limited to such examples. The collection unit, for example, analyzes the previous and following statements and preferentially collects the contents of a statement made by a specific person in a specific business scene. In addition, the collection unit can analyze related topics and, if a statement made by a specific person is related to a specific time or event, analyze and collect the background information. Furthermore, if a statement made by a specific person is related to a specific theme, the collection unit can also preferentially collect data related to the theme. For example, the collection unit preferentially collects the contents of a statement made by a specific person in a specific business scene. In addition, if a statement made by a specific person is related to a specific time or event, the collection unit can also analyze and collect the background information. Furthermore, if a statement made by a specific person is related to a specific theme, the collection unit can also preferentially collect data related to the theme. This enables the collection unit to prioritize the collection of more important data by analyzing the background and context of comments.

[0067] The collection unit can adjust the level of detail of the data to be collected based on the frequency and importance of the utterances of a specific person. The collection unit adjusts the level of detail of the data to be collected based on, for example, the frequency and importance of the utterances of a specific person. The frequency of utterances includes, for example, the number of occurrences, time intervals, and the like, but is not limited to such examples. For example, the collection unit collects detailed data for a theme that is frequently mentioned by a specific person. In addition, the collection unit can also collect data including detailed background information for important utterances and quotes of a specific person. Furthermore, if the utterances of a specific person are considered important in a specific field, the collection unit can also collect detailed data related to that field. For example, the collection unit collects detailed data for a theme that is frequently mentioned by a specific person. In addition, the collection unit can also collect data including detailed background information for important utterances and quotes of a specific person. Furthermore, if the utterances of a specific person are considered important in a specific field, the collection unit can also collect detailed data related to that field. As a result, the collection unit can collect more accurate data by adjusting the level of detail of the data based on the frequency and importance of the utterances.

[0068] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. The collection unit, for example, estimates the user's emotion and adjusts the timing of data collection based on the estimated user's emotion. The estimation of emotion includes, for example, facial expression analysis, voice analysis, text analysis, and the like, but is not limited to such examples. The collection unit, for example, estimates the user's emotion using facial expression analysis. The collection unit can also estimate the user's emotion using voice analysis. Furthermore, the collection unit can estimate the user's emotion using text analysis. For example, the collection unit uses facial expression analysis to preferentially collect effective data in a short time when the user is in a hurry. Furthermore, the collection unit can use voice analysis to collect long lecture recordings or interview videos when the user is relaxed. Furthermore, the collection unit can use text analysis to collect data according to the timing when the user requests advice at a specific timing. As a result, the collection unit can collect data at a more appropriate timing by adjusting the collection timing of data according to the user's emotion.

[0069] The collection unit can select data to be collected in consideration of the geographical background of the statement of a specific person. The collection unit selects data to be collected in consideration of the geographical background of the statement of a specific person, for example. The geographical background includes, for example, a culture specific to a region, a geographical condition, and the like, but is not limited to such examples. The collection unit, for example, collects lectures and interviews given by a specific person in a specific region. In addition, when the statement of a specific person is related to a specific country or region, the collection unit can also collect data related to the region. Furthermore, when the statement of a specific person is related to a specific culture or social background, the collection unit can also collect data including the background information. For example, the collection unit collects lectures and interviews given by a specific person in a specific region. In addition, when the statement of a specific person is related to a specific country or region, the collection unit can also collect data related to the region. Furthermore, when the statement of a specific person is related to a specific culture or social background, the collection unit can also collect data including the background information. In this way, the collection unit can collect more relevant data by considering the geographical background.

[0070] The collection unit can also collect statements of other celebrities related to the statement of the specific person and perform a comparative analysis. The collection unit, for example, collects statements of other celebrities related to the statement of the specific person and performs a comparative analysis. Other celebrities include, for example, celebrities in the same industry, experts in related fields, etc., but are not limited to such examples. The collection unit, for example, collects statements of celebrities from the same era as the specific person and performs a comparative analysis. The collection unit can also collect statements of experts in related fields with the statement of the specific person and perform a comparative analysis. Furthermore, the collection unit can collect statements of celebrities with a different perspective from the statement of the specific person and perform a comparative analysis. For example, the collection unit collects statements of celebrities from the same era as the specific person and performs a comparative analysis. The collection unit can also collect statements of experts in related fields with the statement of the specific person and perform a comparative analysis. Furthermore, the collection unit can collect statements of celebrities with a different perspective from the statement of the specific person and perform a comparative analysis. As a result, the collection unit can provide a more multifaceted perspective by comparing and analyzing statements of other celebrities.

[0071] The analysis unit can estimate the user's emotion and adjust the analysis algorithm based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the analysis algorithm based on the estimated user's emotion. The estimation of emotion includes, for example, facial expression analysis, voice analysis, text analysis, and the like, but is not limited to such examples. The analysis unit, for example, estimates the user's emotion using facial expression analysis. The analysis unit can also estimate the user's emotion using voice analysis. Furthermore, the analysis unit can estimate the user's emotion using text analysis. For example, the analysis unit can apply an algorithm that generates a concise and clear answer when the user is stressed using facial expression analysis. The analysis unit can also apply an algorithm that generates an answer with detailed and deep insights using voice analysis when the user is relaxed. Furthermore, the analysis unit can apply an algorithm that generates a quick answer when the user is in a hurry using text analysis. This allows the analysis unit to generate a more appropriate answer by adjusting the analysis algorithm according to the user's emotion.

[0072] The analysis unit can analyze background information of a question and apply an analysis method for generating a more specific answer. The analysis unit, for example, analyzes background information of a question and applies an analysis method for generating a more specific answer. The background information includes, for example, past questions, related topics, and the like, but is not limited to such examples. For example, the analysis unit analyzes past questions, and if the question is related to a specific business scene, analyzes the background information to generate a specific answer. In addition, the analysis unit can analyze related topics, and if the question is related to a specific time or event, analyze the background information to generate a specific answer. In addition, the analysis unit can analyze past questions, and if the question is related to a specific theme, analyze the background information to generate a specific answer. For example, the analysis unit analyzes past questions, and if the question is related to a specific business scene, analyzes the background information to generate a specific answer. In addition, the analysis unit can analyze related topics, and if the question is related to a specific time or event, analyze the background information to generate a specific answer. In addition, the analysis unit can analyze past questions, and if the question is related to a specific topic, the analysis unit can analyze the background information to generate a specific answer. This allows the analysis unit to generate a more specific and appropriate answer by analyzing the background information of the question.

[0073] The analysis unit can apply different analysis algorithms depending on the category of the question to improve the accuracy of the answer. The analysis unit, for example, applies different analysis algorithms depending on the category of the question to improve the accuracy of the answer. The question categories include, for example, technical questions, questions related to business, and the like, but are not limited to such examples. For example, the analysis unit applies a technical analysis algorithm to technical questions. Furthermore, the analysis unit can also apply an analysis algorithm specialized for business strategies to questions related to business. Furthermore, the analysis unit can apply an analysis algorithm including philosophical insights to questions related to life philosophy. For example, the analysis unit applies a technical analysis algorithm to technical questions. Furthermore, the analysis unit can apply an analysis algorithm specialized for business strategies to questions related to business. Furthermore, the analysis unit can apply an analysis algorithm including philosophical insights to questions related to life philosophy. As a result, the analysis unit applies an analysis algorithm according to the category of the question to improve the accuracy of the answer.

[0074] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the display method of the analysis result based on the estimated user's emotion. The estimation of emotion includes, for example, facial expression analysis, voice analysis, text analysis, etc., but is not limited to such examples. The analysis unit, for example, estimates the user's emotion using facial expression analysis. The analysis unit can also estimate the user's emotion using voice analysis. Furthermore, the analysis unit can estimate the user's emotion using text analysis. For example, the analysis unit uses facial expression analysis to provide a simple and highly visible display method when the user is nervous. The analysis unit can also use voice analysis to provide a display method including detailed information when the user is relaxed. Furthermore, the analysis unit can use text analysis to provide a display method that focuses on the main points when the user is in a hurry. This allows the analysis unit to adjust the display method of the analysis result according to the user's emotion, making it possible to display more appropriately.

[0075] The analysis unit can determine the priority of the analysis based on the time when the question is submitted. The analysis unit determines the priority of the analysis based on, for example, the time when the question is submitted. The submission time includes, for example, the submission date and time, the urgency, and the like, but is not limited to such examples. The analysis unit determines the priority of the question based on, for example, the submission date and time. The analysis unit can also determine the priority of the question based on the urgency. Furthermore, the analysis unit can determine the priority of the question related to a specific event or time. For example, the analysis unit determines the priority of the question based on the submission date and time. The analysis unit can also determine the priority of the question based on the urgency. Furthermore, the analysis unit can determine the priority of the question related to a specific event or time. In this way, the analysis unit can quickly respond to questions with high urgency by determining the priority of the analysis based on the time when the question is submitted.

[0076] The analysis unit can adjust the order of analysis based on the relevance of the questions. The analysis unit adjusts the order of analysis based on, for example, the relevance of the questions. The relevance includes, for example, the degree of matching of the topic, the relevance to past questions, and the like, but is not limited to such examples. The analysis unit adjusts the order of questions based on, for example, the degree of matching of the topic. The analysis unit can also adjust the order of questions based on the relevance to past questions. Furthermore, the analysis unit can adjust the order of questions related to a specific theme. For example, the analysis unit adjusts the order of questions based on the degree of matching of the topic. The analysis unit can also adjust the order of questions based on the relevance to past questions. Furthermore, the analysis unit can adjust the order of questions related to a specific theme. In this way, the analysis unit can provide consistent answers to related questions by adjusting the order of analysis based on the relevance of the questions.

[0077] The generation unit can estimate the user's emotion and adjust the expression method of the video and audio to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the expression method of the video and audio to be generated based on the estimated user's emotion. The estimation of emotion includes, for example, facial expression analysis, voice analysis, text analysis, and the like, but is not limited to such examples. The generation unit, for example, estimates the user's emotion using facial expression analysis. The generation unit can also estimate the user's emotion using voice analysis. Furthermore, the generation unit can estimate the user's emotion using text analysis. For example, the generation unit can use facial expression analysis to generate video and audio speaking in a gentle tone when the user is relaxed. Furthermore, the generation unit can use voice analysis to generate video and audio using a quick and concise expression method when the user is in a hurry. Furthermore, the generation unit can use text analysis to generate video and audio with a visually stimulating effect added when the user is excited. As a result, the generation unit can adjust the expression method of the video and audio according to the user's emotion, thereby enabling more appropriate expression.

[0078] The generation unit can analyze the tone and pace of a specific person's speech and reflect them in the video and audio to be generated. The generation unit can, for example, analyze the tone and pace of a specific person's speech and reflect them in the video and audio to be generated. The analysis of tone and pace includes, for example, voice analysis and rhythm analysis, but is not limited to such examples. The generation unit can, for example, analyze the tone of a specific person's speech using voice analysis. The generation unit can also analyze the pace of a specific person's speech using rhythm analysis. Furthermore, the generation unit can analyze the tone and pace of a specific person's speech by combining voice analysis and rhythm analysis. For example, when a specific person speaks in a gentle tone, the generation unit can generate video and audio that reproduce the tone using voice analysis. Furthermore, when a specific person speaks quickly, the generation unit can generate video and audio that reproduce the pace using rhythm analysis. Furthermore, the generation unit can combine voice analysis and rhythm analysis to generate video and audio that reproduce the emphasized part when a specific person speaks with emphasis. This enables the generation unit to generate more realistic images and audio by reflecting the tone and pace of a specific person's speech.

[0079] The generation unit can adjust the level of detail of the video and audio to be generated based on the context of the specific person's utterance. The generation unit adjusts the level of detail of the video and audio to be generated based on, for example, the context of the specific person's utterance. The analysis of the context includes, for example, previous and following utterances, related topics, etc., but is not limited to such examples. For example, the generation unit analyzes the previous and following utterances, and when the specific person's utterance includes a specific example, generates video and audio that reproduces the example in detail. In addition, the generation unit can also analyze related topics, and when the specific person's utterance includes an abstract concept, generate video and audio that visually expresses the concept. Furthermore, the generation unit can combine the previous and following utterances with related topics, and when the specific person's utterance includes an emotional element, generate video and audio that emphasizes the emotion. For example, the generation unit analyzes the previous and following utterances, and when the specific person's utterance includes a specific example, generates video and audio that reproduces the example in detail. In addition, the generation unit can analyze related topics, and when the specific person's utterance includes an abstract concept, generate video and audio that visually expresses the concept. Furthermore, the generator can combine previous and subsequent utterances and related topics, and if a particular person's utterance contains an emotional element, generate video and audio that emphasizes that emotion. This allows the generator to generate more appropriate video and audio by adjusting the level of detail based on the context of the utterance.

[0080] The generation unit can estimate the user's emotion and adjust the length of the video and audio to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the video and audio to be generated based on the estimated user's emotion. The estimation of emotion includes, for example, facial expression analysis, voice analysis, text analysis, and the like, but is not limited to such examples. The generation unit, for example, estimates the user's emotion using facial expression analysis. The generation unit can also estimate the user's emotion using voice analysis. Furthermore, the generation unit can estimate the user's emotion using text analysis. For example, the generation unit can use facial expression analysis to generate short and to the point video and audio when the user is in a hurry. Furthermore, the generation unit can use voice analysis to generate longer video and audio including detailed explanations when the user is relaxed. Furthermore, the generation unit can use text analysis to generate video and audio with visually stimulating effects when the user is excited. As a result, the generation unit can generate video and audio of a more appropriate length by adjusting the length of the video and audio according to the user's emotion.

[0081] The generation unit can customize the generated video and audio in consideration of the geographical background of the speech of a specific person. The generation unit customizes the generated video and audio in consideration of the geographical background of the speech of a specific person, for example. The geographical background includes, for example, a culture specific to a region, a geographical condition, and the like, but is not limited to such examples. The generation unit generates, for example, video and audio reproducing a lecture given by a specific person in a specific region. In addition, when the speech of a specific person is related to a specific country or region, the generation unit can also generate video and audio including the background of the region. Furthermore, when the speech of a specific person is related to a specific culture or social background, the generation unit can also generate video and audio including the background information of the region. For example, the generation unit generates video and audio reproducing a lecture given by a specific person in a specific region. In addition, when the speech of a specific person is related to a specific country or region, the generation unit can also generate video and audio including the background of the region. Furthermore, when the speech of a specific person is related to a specific culture or social background, the generation unit can also generate video and audio including the background information of the region. This allows the generation unit to generate more relevant video and audio by taking the geographical background into account.

[0082] The generation unit can also generate statements of other celebrities related to the statement of the specific person and perform a comparative display. The generation unit can, for example, generate statements of other celebrities related to the statement of the specific person and perform a comparative display. The other celebrities include, for example, celebrities in the same industry, experts in related fields, and the like, but are not limited to such examples. The generation unit, for example, reproduces statements of celebrities from the same era as the specific person and generates video and audio for comparative display. The generation unit can also reproduce statements of experts in related fields with the statement of the specific person and generate video and audio for comparative display. Furthermore, the generation unit can also reproduce statements of celebrities with a different perspective from the statement of the specific person and generate video and audio for comparative display. For example, the generation unit reproduces statements of celebrities from the same era as the specific person and generates video and audio for comparative display. The generation unit can also reproduce statements of experts in related fields with the statement of the specific person and generate video and audio for comparative display. Furthermore, the generation unit can also reproduce statements of celebrities with a different perspective from the statement of the specific person and generate video and audio for comparative display. This allows the generation unit to provide a more multifaceted perspective by comparing the statement with those of other famous people.

[0083] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0084] The collection unit can also customize the type of data to be collected based on the user's interests. For example, if the user is interested in a particular business field, the collection unit can prioritize collecting statements of a particular person and the contents of books related to that field. In addition, if the user is interested in self-development, data on a particular person's life philosophy and guidelines for action can also be collected. Furthermore, if the user is looking for information related to a particular time or event, the collection unit can also collect statements and activities of a particular person related to that time or event. This allows the collection unit to provide more personalized information by customizing the collection of data according to the user's interests.

[0085] The analysis unit can also generate more appropriate answers by analyzing the tendency of the user's questions based on the user's past question history. For example, if the user has asked many business-related questions in the past, business-related answers can be generated preferentially. Also, if the user has asked many questions about self-development, answers related to self-development can be generated. Furthermore, if the user has repeatedly asked questions about a specific topic, detailed answers related to that topic can be generated. In this way, the analysis unit can provide more appropriate and personalized answers by utilizing the user's question history.

[0086] The generation unit can also estimate the user's emotion and adjust the tone and tempo of the generated video and audio based on the estimated user's emotion. For example, when the user is relaxed, video and audio can be generated in which the user speaks slowly in a gentle tone. When the user is in a hurry, video and audio can be generated in which the user speaks quickly and concisely. Furthermore, when the user is excited, video and audio can be generated in which the user speaks in an energetic tone. This allows the generation unit to adjust the tone and tempo of the video and audio according to the user's emotion, making it possible to express the emotion more appropriately.

[0087] The collection unit can also customize the data to be collected taking into account the geographical background of the user. For example, if the user lives in a particular area, it preferentially collects statements and activities of a particular person related to that area. In addition, if the user has a particular culture or social background, it can also collect data related to that background. Furthermore, if the user is interested in a particular country or area, it can also collect statements and activities of a particular person related to that country or area. In this way, the collection unit can provide more relevant information by customizing the collection of data according to the geographical background of the user.

[0088] The analysis unit can also estimate the user's emotions and adjust the priority of analysis based on the estimated user's emotions. For example, if the user is feeling stressed, answers that reduce stress can be generated preferentially. Also, if the user wants to increase motivation, answers related to encouragement or successful experiences can be generated preferentially. Furthermore, if the user is looking for specific advice, answers including practical advice can be generated preferentially. In this way, the analysis unit can provide more appropriate answers by adjusting the priority of analysis according to the user's emotions.

[0089] The generation unit can also generate video and audio that matches the user's preferences based on the user's past question history. For example, if the user has asked many business-related questions in the past, business-related video and audio can be preferentially generated. Also, if the user has asked many questions about self-development, video and audio related to self-development can be generated. Furthermore, if the user has repeatedly asked questions about a specific topic, detailed video and audio related to that topic can be generated. In this way, the generation unit can provide more personalized video and audio by utilizing the user's question history.

[0090] The collection unit can also estimate the user's emotion and adjust the amount of data to be collected based on the estimated user's emotion. For example, when the user is stressed, a small amount of data can be collected to provide a concise answer. When the user is relaxed, a large amount of data can be collected to provide a detailed answer. Furthermore, when the user is excited, visually stimulating data can be collected. In this way, the collection unit can provide more appropriate information by adjusting the amount of data according to the user's emotion.

[0091] The analysis unit can also customize the analysis method based on the category of the user's question. For example, a technical analysis method can be applied to a technical question. Also, an analysis method specialized for business strategies can be applied to a question about business. Furthermore, an analysis method including philosophical insights can be applied to a question about life philosophy. In this way, the analysis unit can provide a more accurate answer by applying an analysis method according to the question category.

[0092] The generation unit can also estimate the user's emotion and adjust the style of the generated video and audio based on the estimated user's emotion. For example, if the user is relaxed, a calm style of video and audio can be generated. If the user is in a hurry, a quick and concise style of video and audio can be generated. Furthermore, if the user is excited, a visually stimulating style of video and audio can be generated. This allows the generation unit to adjust the style of video and audio according to the user's emotion to provide a more appropriate expression.

[0093] The collection unit can also determine the priority of data to be collected based on the user's interests. For example, if the user is interested in a particular business field, the collection unit can prioritize the collection of statements by a particular person and the contents of books related to that field. In addition, if the user is interested in self-development, the collection unit can prioritize the collection of data related to a particular person's life philosophy and guidelines for action. Furthermore, if the user is looking for information related to a particular time or event, the collection unit can prioritize the collection of statements and activities of a particular person related to that time or event. This allows the collection unit to provide more personalized information by customizing the collection of data according to the user's interests.

[0094] The process flow of the second embodiment will be briefly described below.

[0095] Step 1: The collection unit collects data on a specific person (e.g., a specific person) and has the AI ​​learn from it. The collected data includes interview footage, lecture recordings, book contents, etc. The collection unit collects interview footage in high resolution and has the AI ​​learn from it. Lecture recordings are collected as audio data and have the AI ​​learn from it. Book contents are collected as text data and have the AI ​​learn from it. Step 2: The analysis unit analyzes the user's questions and inquiries based on the data collected by the collection unit, and generates appropriate answers. The analysis unit uses natural language processing technology and machine learning algorithms to analyze the user's questions, and can also perform analysis based on past question data. For example, natural language processing technology is used to perform morphological analysis of the user's questions and analyze their meaning. The machine learning algorithm learns from past question data and generates appropriate answers to new questions. Step 3: The generation unit outputs the answer generated by the analysis unit as video and audio. The generation unit can generate video using deepfake technology and generate audio using voice synthesis technology. Furthermore, the generation unit can also generate video and audio in real time. For example, a video of a specific person is generated using deepfake technology, and audio is generated by imitating the voice of a specific person using voice synthesis technology. Real-time generation can instantly generate video and audio in response to a user's question.

[0096] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0097] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0098] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0099] Each of the multiple elements including the collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data of a specific person using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A causes the AI ​​to learn the data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's question based on the collected data and generates an appropriate answer. The generation unit is realized, for example, by the control unit 46A of the smart device 14, and generates an image and a voice of a specific person using deep fake technology and voice synthesis technology. The correspondence between each unit and the device and the control unit is not limited to the above example, and various changes are possible.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0103] 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.

[0104] 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 the voice according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0106] 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 performed in a secure state.

[0107] Fig. 4 shows an example of 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.

[0108] 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.

[0109] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 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 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0111] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0112] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0113] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0114] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and generation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data of a specific person using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A causes the AI ​​to learn the data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's question based on the collected data and generates an appropriate answer. The generation unit is realized, for example, by the control unit 46A of the smart glasses 214, and generates an image or sound of a specific person using deep fake technology or voice synthesis technology. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0119] 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.

[0120] 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 the voice according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0122] 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 performed in a secure state.

[0123] 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.

[0124] 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.

[0125] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0126] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0127] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0128] 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 voice indicating a user input for 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.

[0129] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0130] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and generation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects data of a specific person using the camera 42 and microphone 238 of the headset type terminal 314, and the control unit 46A causes the AI ​​to learn the data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's question based on the collected data and generates an appropriate answer. The generation unit is realized, for example, by the control unit 46A of the headset type terminal 314, and generates an image and a voice of a specific person using deep fake technology and voice synthesis technology. The correspondence between each unit and the device and the control unit is not limited to the above-mentioned example, and various changes are possible.

[0132] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0133] 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.

[0134] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0135] 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.

[0136] 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 the voice according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0138] 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 performed in a secure state.

[0139] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0140] 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.

[0141] 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.

[0142] 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0144] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] 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 a voice indicating a user input for 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.

[0146] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0148] Each of the multiple elements including the above-mentioned collection unit, analysis unit, and generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data of a specific person using the camera 42 and microphone 238 of the robot 414, and the control unit 46A causes the AI ​​to learn the data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the user's question based on the collected data and generates an appropriate answer. The generation unit is realized, for example, by the control unit 46A of the robot 414, and generates an image and a voice of a specific person using deep fake technology and voice synthesis technology. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0149] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0150] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0151] These emotions are distributed in the three o'clock direction of emotion map 400 and usually 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.

[0152] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0153] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0154] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0155] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0156] 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.

[0157] 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 Universal Serial Bus (USB) 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.

[0158] In addition, 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 upon request from the data processing device 12.

[0159] 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.

[0160] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0161] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0162] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0163] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0164] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0165] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0166] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0167] (Appendix 1) A collection unit that collects data on specific individuals and trains the AI ​​to learn from them; an analysis unit that analyzes questions and inquiries from users based on the data collected by the collection unit and generates answers; A generating unit that outputs the answer generated by the analyzing unit as a video or audio. A system characterized by: (Appendix 2) The system described in Appendix 1, wherein the collection unit collects data on interview footage or lecture recordings of a specific person. (Appendix 3) The analysis unit is Analyze user questions using natural language processing technology 2. The system of claim 1, (Appendix 4) The system described in Appendix 1, characterized in that the generation unit generates video or audio in real time using deep fake technology or voice synthesis technology. (Appendix 5) The collecting unit includes: Inferring user sentiment and adjusting the type of data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 6) The collecting unit includes: Analyze the background and context of a particular person's statement and determine the priority of the data to be collected 2. The system of claim 1, (Appendix 7) The collecting unit includes: Adjust the granularity of the data you collect based on the frequency and importance of certain people's comments 2. The system of claim 1, (Appendix 8) The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1, (Appendix 9) The collecting unit includes: Select the data to be collected, taking into account the geographical context of a particular person's statements. 2. The system of claim 1, (Appendix 10) The collecting unit includes: Collect statements by other celebrities related to a particular person's statement and conduct a comparative analysis. 2. The system of claim 1, (Appendix 11) The analysis unit is Infer user sentiment and adjust analysis algorithms based on the inferred user sentiment 2. The system of claim 1, (Appendix 12) The analysis unit is Analyze the context of a question and apply analytical techniques to generate more specific answers 2. The system of claim 1, (Appendix 13) The analysis unit is Apply different analysis algorithms depending on the question category to improve the accuracy of the answer 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user emotions and adjust the way analysis results are displayed based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is Prioritize analysis based on when questions are submitted 2. The system of claim 1, (Appendix 16) The analysis unit is Order your analysis based on question relevance 2. The system of claim 1, (Appendix 17) The generation unit is Estimate the user's emotions and adjust the way the video and audio are generated based on the estimated user's emotions. 2. The system of claim 1, (Appendix 18) The generation unit is Analyze the tone and pace of a specific person's speech and reflect it in the generated video and audio 2. The system of claim 1, (Appendix 19) The generation unit is Adjust the level of detail in generated video and audio based on the context of what a particular person is saying 2. The system of claim 1, (Appendix 20) The generation unit is Estimate the user's emotions and adjust the length of the video and audio to be generated based on the estimated user emotions. 2. The system of claim 1, (Appendix 21) The generation unit is Customize the video and audio generated based on the geographic context of a particular person's speech 2. The system of claim 1, (Appendix 22) The generation unit is Generates statements by other famous people related to a specific person's statement and displays them for comparison 2. The system of claim 1, [Explanation of symbols]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects data of a specific person and has the AI ​​learn the data; an analysis unit that analyzes questions and inquiries from users based on the data collected by the collection unit and generates answers; A generating unit that outputs the answer generated by the analyzing unit as a video or audio. A system characterized by:

2. The collecting unit includes: Collect interview footage or speech recordings of specific individuals 2. The system of claim 1 .

3. The analysis unit is Analyze user questions using natural language processing technology 2. The system of claim 1 .

4. The generation unit is Using deepfake technology or voice synthesis technology to generate video or audio in real time 2. The system of claim 1 .

5. The collecting unit includes: Inferring user sentiment and adjusting the type of data collected based on the inferred user sentiment 2. The system of claim 1 .

6. The collecting unit includes: Analyze the background and context of a particular person's statement and determine the priority of the data to be collected 2. The system of claim 1 .

7. The collecting unit includes: Adjust the granularity of the data you collect based on the frequency and importance of certain people's comments 2. The system of claim 1 .

8. The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1 .

Images