system

Abstract

To provide a system that imitates features of an influential person to create utterance and presentation materials.SOLUTION: A system comprises a reception part, an analysis part, a creation part, and a providing part. The reception part inputs information on a person to be imitated. The analysis part analyzes the information input by the reception part to extract features of the person. The creation part creates utterance or presentation materials based upon the features extracted by the analysis part. The providing part provides the utterance or presentation materials created by the creation part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to create statements and presentation materials that imitate the characteristics of influential people.

[0005] The system according to the embodiment aims to create statements and presentation materials by imitating the characteristics of influential people. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a generating unit, and a providing unit. The receiving unit inputs information about a person to be imitated. The analyzing unit analyzes the information input by the receiving unit and extracts characteristics of the person. The generating unit generates utterances or presentation materials based on the characteristics extracted by the analyzing unit. The providing unit provides the utterances or presentation materials generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can create statements and presentation materials by imitating the characteristics of influential people. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention imitates the way great or influential people communicate and creates speeches and presentation materials that emulate that person. This system allows users to input information about the person they want to emulate, and a generation AI analyzes that person's characteristics and generates speeches and presentation materials. For example, when a user inputs information about an influential person, this information is input into the generation AI. The generation AI then analyzes the input information and extracts the person's characteristics. The generation AI analyzes the person's speaking style, presentation style, and word choice. For example, for an influential person, the generation AI analyzes their famous speeches and writings to extract their characteristics. Based on the extracted characteristics, the generation AI generates speeches and presentation materials that emulate the person the user wants to emulate. For example, it generates speech manuscripts and presentation slides that reflect the speaking style and style of the person. This system allows users to communicate like great or influential people. For example, in business presentations and lectures, users can communicate effectively in the style of the person they want to emulate. This system can also be used in education and training settings to help users acquire effective communication skills. This allows the system to communicate things like a great or influential person.

[0029] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs information about a person the user wants to imitate. The information about the person the user wants to imitate includes, but is not limited to, their speaking style, presentation style, and the words they use. The reception unit provides, for example, an interface through which a user can input information about an influential person. The analysis unit uses a generation AI to analyze the information input by the reception unit and extract characteristics of the person. The analysis unit analyzes, for example, the influential person's speaking style, presentation style, and choice of words. The generation AI extracts characteristics based on the input information using a text generation AI (e.g., LLM). For example, the generation AI analyzes famous speeches and writings of the influential person and extracts characteristics. The generation unit uses the generation AI to generate remarks and presentation materials based on the characteristics extracted by the analysis unit. For example, the generation unit generates a speech manuscript based on the analyzed characteristics. The generation unit can also generate presentation slides based on the analyzed characteristics. Based on the extracted features, the generation AI generates speeches and presentation materials that embody the person the user wants to emulate. The providing unit provides the generated speeches and presentation materials to the user. The providing unit provides, for example, an interface for providing the generated speech manuscript or presentation slides to the user. This allows the system according to the embodiment to enable the user to communicate like a great or influential person. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may store the generated speeches and presentation materials in cloud storage so that the user can access them. The providing unit may also send the generated speeches and presentation materials to the user by email. This allows the user to easily receive the generated materials.

[0030] The reception unit inputs information about the person the user wants to imitate. Information about the person the user wants to imitate includes, but is not limited to, their speaking style, presentation style, and the words they use. The reception unit provides, for example, an interface through which the user can input information about influential people. Specifically, the reception unit provides forms and questionnaires for the user to input detailed information about the person the user wants to imitate. This information may include the name of the person the user wants to imitate, their occupation, links to specific speeches or presentations, and a list of specific phrases and words they use. The reception unit also provides a function for the user to upload audio or video clips, which allows the user to collect detailed characteristics of the person the user wants to imitate, such as their speaking style, tone of voice, and rhythm. The reception unit has a database for centrally managing the information entered by the user and sending it to the analysis unit. This ensures that the information entered by the user is passed to the analysis unit accurately and efficiently, ensuring smooth subsequent processing. Furthermore, the reception unit provides a preview and editing function for the information entered by the user, allowing the user to check the input and make corrections if necessary. This allows the reception unit to help the user enter accurate and detailed information about the person the user wants to imitate, thereby improving the accuracy and reliability of the entire system.

[0031] The analysis unit uses the generation AI to analyze the information entered by the reception unit and extract the person's characteristics. For example, the analysis unit analyzes the influential person's speaking style, presentation style, and word choice. The generation AI uses a text generation AI (e.g., LLM) to extract characteristics based on the entered information. Specifically, the generation AI analyzes the influential person's famous speeches and writings and extracts characteristics. For example, the generation AI uses natural language processing technology to perform a detailed analysis of characteristics such as speech and sentence structure, grammar, vocabulary choice, rhythm, and intonation. Furthermore, the generation AI uses speech recognition technology to extract speaking characteristics from audio data, analyzing voice tone, pitch, speaking speed, etc. Based on these analysis results, the analysis unit stores the characteristics of the person to be imitated in a database and makes them available to the generation unit. The analysis unit can utilize past data and statistical information to perform more accurate feature extraction. For example, by collecting a large amount of data on past speeches and presentations by influential people and extracting features from this data, more accurate analysis results can be obtained. This allows the analysis unit to extract with high accuracy the features of the person the user wants to imitate, providing a foundation for the generation unit to generate remarks and presentation materials based on these features.

[0032] The generation unit uses a generation AI to generate speeches and presentation materials based on the features extracted by the analysis unit. For example, the generation unit generates a speech manuscript based on the analyzed features. The generation unit can also generate presentation slides based on the analyzed features. Based on the extracted features, the generation AI generates speeches and presentation materials that truly emulate the person the user wants to imitate. Specifically, the generation AI uses natural language generation technology to create a speech manuscript that matches the speaking style and manner of speaking of the person the user wants to imitate. For example, the generation AI incorporates specific phrases and expressions of the person the user wants to imitate and reproduces the tone and rhythm of the entire speech. The generation AI also generates presentation slides with a design and content that matches the style of the person the user wants to imitate. This includes the slide layout, color usage, font selection, and placement of charts and graphs. The generation unit also provides an interface that allows users to easily edit the generated speech manuscript and presentation slides. This allows users to customize the generated materials to suit their needs. Furthermore, the generation unit also has a feedback function that evaluates the quality of the generated materials and makes corrections and improvements as necessary. This allows the generation unit to generate high-quality statements and presentation materials that faithfully reproduce the characteristics of the person the user wants to imitate, and to support effective communication according to the user's purpose.

[0033] The providing unit provides the generated speech and presentation materials to the user. For example, the providing unit provides an interface for providing the generated speech manuscript and presentation slides to the user. Specifically, the providing unit stores the generated materials in cloud storage and allows the user to access them. The user can log in to the cloud storage and download the generated materials or view them online. The providing unit can also send the generated speech and presentation materials to the user via email, allowing the user to easily receive the generated materials. Furthermore, the providing unit also provides a function for generating a link for sharing the generated materials, allowing the user to easily share them with others. For example, a user can send a link to the generated speech manuscript or presentation slides to colleagues or friends to edit and review them collaboratively. The providing unit also has a function for collecting user feedback and continuously improving the quality of the generated materials and the method for providing them. This allows the providing unit to support the user in effectively using the generated materials and improve the overall user experience of the system. Furthermore, the providing unit also has a function for ensuring the security of the generated materials, ensuring that user data is safely protected. This allows the providing unit to provide an environment in which users can use the generated materials with peace of mind.

[0034] The analysis unit can analyze the speaking style, presentation style, and word choice of an influential person. The analysis unit, for example, analyzes the speaking style of an influential person. For example, it analyzes the speaking speed, tone of voice, intonation, etc. The analysis unit can also analyze the presentation style of an influential person. For example, it analyzes the slide design, fonts used, color usage, etc. The analysis unit can also analyze the word choice of an influential person. For example, it analyzes the frequency of use of specific keywords and phrases. This allows the analysis unit to accurately analyze the characteristics of an influential person. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input speech data of an influential person into a generation AI and have the generation AI analyze the speaking style, presentation style, and word choice of an influential person.

[0035] The generation unit can generate a speech manuscript based on the analyzed features. The generation unit generates the speech manuscript based on, for example, the analyzed features. The speech manuscript may include, but is not limited to, the structure, length, and words used. The generation unit generates, for example, a speech manuscript that reflects the speaking style of an influential person. The generation AI generates the speech manuscript based on the analyzed features using a text generation AI (e.g., LLM). For example, the generation AI may refer to a famous speech by an influential person and generate a speech manuscript in a similar style. The generation unit can also generate a speech manuscript on a specific topic based on the analyzed features. For example, the generation AI may analyze the characteristics of an influential person when speaking on a specific topic and generate a speech manuscript that reflects those characteristics. This allows the generation unit to generate a speech manuscript that reflects the characteristics of the influential person. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI. For example, the generation unit may input the analyzed features to the generation AI and have the generation AI generate a speech manuscript.

[0036] The generation unit can generate presentation slides based on the analyzed features. The generation unit generates presentation slides based on, for example, the analyzed features. Presentation slides include, but are not limited to, slide design, fonts used, and color usage. The generation unit generates slides that reflect the presentation style of an influential person. The generation AI generates presentation slides based on the analyzed features using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI may refer to a famous presentation by an influential person and generate slides in a similar style. The generation unit can also generate presentation slides on a specific topic based on the analyzed features. For example, the generation AI may analyze the characteristics of an influential person when giving a presentation on a specific topic and generate slides that reflect those characteristics. This allows the generation unit to generate presentation slides that reflect the characteristics of the influential person. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI. For example, the generation unit may input the analyzed features to the generation AI and cause the generation AI to generate presentation slides.

[0037] The providing unit may provide the generated speech manuscript or presentation slides to a user. For example, the providing unit may provide the generated speech manuscript or presentation slides to a user. Methods of providing the speech manuscript or presentation slides include, but are not limited to, email, cloud storage, printed materials, and the like. For example, the providing unit may store the generated speech manuscript or presentation slides in cloud storage to allow the user to access them. The providing unit may also send the generated speech manuscript or presentation slides to a user by email. This allows the user to easily receive the generated materials. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may store the generated remarks or presentation materials in cloud storage to allow the user to access them. The providing unit may also send the generated remarks or presentation materials to a user by email. This allows the user to easily receive the generated materials.

[0038] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit can preferentially suggest input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also automatically display personal information that the user has previously input as candidates. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. This makes it possible to select the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal input method.

[0039] When inputting information about a person to be imitated, the reception unit can filter the information based on the user's current areas of interest. For example, the reception unit can preferentially display people related to the user's current areas of interest. The reception unit can also automatically complete related keywords based on the user's areas of interest. The reception unit can also provide related materials and information based on the user's areas of interest. This makes it possible to filter information based on the user's areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's area of ​​interest data to the generation AI and have the generation AI perform the filtering.

[0040] When inputting information about a person to be imitated, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize displaying personal information related to that area. The reception unit can also provide related events and news based on the user's location information. The reception unit can also provide related materials and information based on the user's location information. This allows highly relevant information to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's location information data to the generation AI and cause the generation AI to select highly relevant information.

[0041] When inputting information about a person to be imitated, the reception unit can analyze the user's social media activity and input related information. For example, the reception unit can automatically display people of interest as candidates based on the user's social media activity. The reception unit can also automatically complete related keywords based on the content of the user's social media posts. The reception unit can also provide related person information based on information about the user's social media followers and friends. This makes it possible to input related information based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to select related information.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the person. For example, in the case of an influential person, the analysis unit performs a detailed analysis. In addition, in the case of an ordinary person, the analysis unit can perform a brief analysis. In addition, in the case of a person who is famous in a particular field, the analysis unit can perform an analysis specialized to that field. This makes it possible to adjust the level of detail of the analysis based on the importance of the person. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the importance data of the person into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the person's category. For example, in the case of a politician, the analysis unit can apply an algorithm that analyzes political speeches and statements. In the case of a scientist, the analysis unit can also apply an algorithm that analyzes academic papers and lectures. In the case of an artist, the analysis unit can also apply an algorithm that analyzes artworks and interviews. This makes it possible to apply an appropriate analysis algorithm depending on the person's category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI, for example. For example, the analysis unit can input person category data into the generation AI and have the generation AI apply the analysis algorithm.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time when the person's information was input. For example, the analysis unit prioritizes analysis of recently input person information. The analysis unit can also prioritize analysis of person information that the user frequently references. The analysis unit can also perform analysis at an appropriate timing based on the user's schedule. This makes it possible to determine the analysis priority based on the time when the person's information was input. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the time when the person's information was input to the generation AI and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of people. For example, the analysis unit prioritizes analysis of people in whom the user is interested. The analysis unit can also prioritize analysis of people related to the user's work. The analysis unit can also prioritize analysis of people related to the user's projects. This makes it possible to adjust the order of analysis based on the relevance of people. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input person relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0046] The generation unit can adjust the level of detail of the generated content based on the importance of the analyzed features during generation. For example, the generation unit generates detailed statements and presentation materials based on important features. The generation unit can also generate concise statements and presentation materials based on general features. The generation unit can also generate statements and presentation materials specialized to a specific field based on features important in that field. This allows the level of detail of the generated content to be adjusted based on the importance of the analyzed features. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input importance data of the analyzed features into the generation AI and cause the generation AI to adjust the level of detail of the generated content.

[0047] During generation, the generation unit can apply different generation algorithms depending on the category of the analyzed features. For example, the generation unit can apply an algorithm that generates political speeches based on the features of politicians. The generation unit can also apply an algorithm that generates academic presentation materials based on the features of scientists. The generation unit can also apply an algorithm that generates creative presentation materials based on the features of artists. This allows an appropriate generation algorithm to be applied depending on the category of the analyzed features. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input category data of the analyzed features into the generation AI and cause the generation AI to apply the generation algorithm.

[0048] At the time of generation, the generation unit can determine the priority of generation based on the input time of the analyzed features. The generation unit, for example, prioritizes generating utterances and presentation materials based on recently analyzed features. The generation unit can also prioritize generating utterances and presentation materials based on features frequently referenced by the user. The generation unit can also generate utterances and presentation materials at the necessary timing based on the user's schedule. This makes it possible to determine the priority of generation based on the input time of the analyzed features. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input input time data of the analyzed features to the generation AI and have the generation AI determine the priority of generation.

[0049] During generation, the generation unit can adjust the order of generation based on the relevance of the analyzed features. For example, the generation unit prioritizes generating features that the user is interested in. The generation unit can also prioritize generating features related to the user's work. The generation unit can also prioritize generating features related to the user's projects. This makes it possible to adjust the order of generation based on the relevance of the analyzed features. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input relevance data of the analyzed features into the generation AI and cause the generation AI to adjust the order of generation.

[0050] When providing the display method, the providing unit can select the optimal display method by referring to the user's past usage history. For example, the providing unit can prioritize providing display methods that the user has used in the past. The providing unit can also predict and provide the optimal display method from the user's past usage history. The providing unit can also analyze the user's past usage history and provide a display method that suits a specific situation. This makes it possible to select the optimal display method based on the user's past usage history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past usage history data into the generation AI and cause the generation AI to select the optimal display method.

[0051] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to select the optimal display method based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

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

[0053] The reception unit can analyze the user's past input history and select the optimal input method. For example, it can preferentially suggest input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also automatically display personal information that the user has previously input as candidates. Furthermore, the reception unit can predict and suggest an input method to be used during a specific time period based on the user's past input history. This makes it possible to select the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal input method.

[0054] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the person. For example, in the case of an influential person, a detailed analysis is performed. The analysis unit can also perform a concise analysis for an ordinary person. Furthermore, in the case of a person who is famous in a particular field, the analysis unit can perform an analysis specialized for that field. This makes it possible to adjust the level of detail of the analysis based on the importance of the person. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the importance data of the person into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0055] During generation, the generation unit can adjust the level of detail of the generated data based on the importance of the analyzed features. For example, detailed statements or presentation materials are generated based on important features. The generation unit can also generate concise statements or presentation materials based on general features. Furthermore, the generation unit can generate statements or presentation materials specialized to a specific field based on features important in that field. This allows the level of detail of the generated data to be adjusted based on the importance of the analyzed features. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input importance data of the analyzed features into the generation AI and cause the generation AI to adjust the level of detail of the generated data.

[0056] When providing the display method, the providing unit can select the optimal display method by referring to the user's past usage history. For example, the providing unit can prioritize the display method that the user has used in the past. The providing unit can also predict and provide the optimal display method from the user's past usage history. Furthermore, the providing unit can analyze the user's past usage history and provide a display method that suits a specific situation. This makes it possible to select the optimal display method based on the user's past usage history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past usage history data into the generation AI and cause the generation AI to select the optimal display method.

[0057] When providing the display information, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to select the optimal display method based on the user's device information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

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

[0059] Step 1: The reception unit inputs information about the person the user wants to imitate. This information includes the way the person speaks, their presentation style, and the words they use. The reception unit provides an interface for the user to input information about the influential person. Step 2: The analysis unit uses a generation AI to analyze the information entered by the reception unit and extract the person's characteristics. The analysis unit analyzes the influential person's speaking style, presentation style, and choice of words. The generation AI uses a text generation AI (e.g., LLM) to extract characteristics based on the entered information. Step 3: The generator uses the generative AI to generate speeches and presentation materials based on the features extracted by the analyzer. The generator generates speech manuscripts and presentation slides based on the analyzed features. Step 4: The providing unit provides the generated remarks and presentation materials to the user. The providing unit provides an interface for providing the generated speech manuscript and presentation slides to the user. The providing unit stores the generated remarks and presentation materials in cloud storage so that the user can access them. The providing unit can also send the generated remarks and presentation materials to the user by email.

[0060] (Example 2) A system according to an embodiment of the present invention imitates the way great or influential people communicate and creates speeches and presentation materials that emulate that person. This system allows users to input information about the person they want to emulate, and a generation AI analyzes that person's characteristics and generates speeches and presentation materials. For example, when a user inputs information about an influential person, this information is input into the generation AI. The generation AI then analyzes the input information and extracts the person's characteristics. The generation AI analyzes the person's speaking style, presentation style, and word choice. For example, for an influential person, the generation AI analyzes their famous speeches and writings to extract their characteristics. Based on the extracted characteristics, the generation AI generates speeches and presentation materials that emulate the person the user wants to emulate. For example, it generates speech manuscripts and presentation slides that reflect the speaking style and style of the person. This system allows users to communicate like great or influential people. For example, in business presentations and lectures, users can communicate effectively in the style of the person they want to emulate. This system can also be used in education and training settings to help users acquire effective communication skills. This allows the system to communicate things like a great or influential person.

[0061] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit inputs information about a person the user wants to imitate. The information about the person the user wants to imitate includes, but is not limited to, their speaking style, presentation style, and the words they use. The reception unit provides, for example, an interface through which a user can input information about an influential person. The analysis unit uses a generation AI to analyze the information input by the reception unit and extract characteristics of the person. The analysis unit analyzes, for example, the influential person's speaking style, presentation style, and choice of words. The generation AI extracts characteristics based on the input information using a text generation AI (e.g., LLM). For example, the generation AI analyzes famous speeches and writings of the influential person and extracts characteristics. The generation unit uses the generation AI to generate remarks and presentation materials based on the characteristics extracted by the analysis unit. For example, the generation unit generates a speech manuscript based on the analyzed characteristics. The generation unit can also generate presentation slides based on the analyzed characteristics. Based on the extracted features, the generation AI generates speeches and presentation materials that embody the person the user wants to emulate. The providing unit provides the generated speeches and presentation materials to the user. The providing unit provides, for example, an interface for providing the generated speech manuscript or presentation slides to the user. This allows the system according to the embodiment to enable the user to communicate like a great or influential person. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may store the generated speeches and presentation materials in cloud storage so that the user can access them. The providing unit may also send the generated speeches and presentation materials to the user by email. This allows the user to easily receive the generated materials.

[0062] The reception unit inputs information about the person the user wants to imitate. Information about the person the user wants to imitate includes, but is not limited to, their speaking style, presentation style, and the words they use. The reception unit provides, for example, an interface through which the user can input information about influential people. Specifically, the reception unit provides forms and questionnaires for the user to input detailed information about the person the user wants to imitate. This information may include the name of the person the user wants to imitate, their occupation, links to specific speeches or presentations, and a list of specific phrases and words they use. The reception unit also provides a function for the user to upload audio or video clips, which allows the user to collect detailed characteristics of the person the user wants to imitate, such as their speaking style, tone of voice, and rhythm. The reception unit has a database for centrally managing the information entered by the user and sending it to the analysis unit. This ensures that the information entered by the user is passed to the analysis unit accurately and efficiently, ensuring smooth subsequent processing. Furthermore, the reception unit provides a preview and editing function for the information entered by the user, allowing the user to check the input and make corrections if necessary. This allows the reception unit to help the user enter accurate and detailed information about the person the user wants to imitate, thereby improving the accuracy and reliability of the entire system.

[0063] The analysis unit uses the generation AI to analyze the information entered by the reception unit and extract the person's characteristics. For example, the analysis unit analyzes the influential person's speaking style, presentation style, and word choice. The generation AI uses a text generation AI (e.g., LLM) to extract characteristics based on the entered information. Specifically, the generation AI analyzes the influential person's famous speeches and writings and extracts characteristics. For example, the generation AI uses natural language processing technology to perform a detailed analysis of characteristics such as speech and sentence structure, grammar, vocabulary choice, rhythm, and intonation. Furthermore, the generation AI uses speech recognition technology to extract speaking characteristics from audio data, analyzing voice tone, pitch, speaking speed, etc. Based on these analysis results, the analysis unit stores the characteristics of the person to be imitated in a database and makes them available to the generation unit. The analysis unit can utilize past data and statistical information to perform more accurate feature extraction. For example, by collecting a large amount of data on past speeches and presentations by influential people and extracting features from this data, more accurate analysis results can be obtained. This allows the analysis unit to extract with high accuracy the features of the person the user wants to imitate, providing a foundation for the generation unit to generate remarks and presentation materials based on these features.

[0064] The generation unit uses a generation AI to generate speeches and presentation materials based on the features extracted by the analysis unit. For example, the generation unit generates a speech manuscript based on the analyzed features. The generation unit can also generate presentation slides based on the analyzed features. Based on the extracted features, the generation AI generates speeches and presentation materials that truly emulate the person the user wants to imitate. Specifically, the generation AI uses natural language generation technology to create a speech manuscript that matches the speaking style and manner of speaking of the person the user wants to imitate. For example, the generation AI incorporates specific phrases and expressions of the person the user wants to imitate and reproduces the tone and rhythm of the entire speech. The generation AI also generates presentation slides with a design and content that matches the style of the person the user wants to imitate. This includes the slide layout, color usage, font selection, and placement of charts and graphs. The generation unit also provides an interface that allows users to easily edit the generated speech manuscript and presentation slides. This allows users to customize the generated materials to suit their needs. Furthermore, the generation unit also has a feedback function that evaluates the quality of the generated materials and makes corrections and improvements as necessary. This allows the generation unit to generate high-quality statements and presentation materials that faithfully reproduce the characteristics of the person the user wants to imitate, and to support effective communication according to the user's purpose.

[0065] The providing unit provides the generated speech and presentation materials to the user. For example, the providing unit provides an interface for providing the generated speech manuscript and presentation slides to the user. Specifically, the providing unit stores the generated materials in cloud storage and allows the user to access them. The user can log in to the cloud storage and download the generated materials or view them online. The providing unit can also send the generated speech and presentation materials to the user via email, allowing the user to easily receive the generated materials. Furthermore, the providing unit also provides a function for generating a link for sharing the generated materials, allowing the user to easily share them with others. For example, a user can send a link to the generated speech manuscript or presentation slides to colleagues or friends to edit and review them collaboratively. The providing unit also has a function for collecting user feedback and continuously improving the quality of the generated materials and the method for providing them. This allows the providing unit to support the user in effectively using the generated materials and improve the overall user experience of the system. Furthermore, the providing unit also has a function for ensuring the security of the generated materials, ensuring that user data is safely protected. This allows the providing unit to provide an environment in which users can use the generated materials with peace of mind.

[0066] The analysis unit can analyze the speaking style, presentation style, and word choice of an influential person. The analysis unit, for example, analyzes the speaking style of an influential person. For example, it analyzes the speaking speed, tone of voice, intonation, etc. The analysis unit can also analyze the presentation style of an influential person. For example, it analyzes the slide design, fonts used, color usage, etc. The analysis unit can also analyze the word choice of an influential person. For example, it analyzes the frequency of use of specific keywords and phrases. This allows the analysis unit to accurately analyze the characteristics of an influential person. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input speech data of an influential person into a generation AI and have the generation AI analyze the speaking style, presentation style, and word choice of an influential person.

[0067] The generation unit can generate a speech manuscript based on the analyzed features. The generation unit generates the speech manuscript based on, for example, the analyzed features. The speech manuscript may include, but is not limited to, the structure, length, and words used. The generation unit generates, for example, a speech manuscript that reflects the speaking style of an influential person. The generation AI generates the speech manuscript based on the analyzed features using a text generation AI (e.g., LLM). For example, the generation AI may refer to a famous speech by an influential person and generate a speech manuscript in a similar style. The generation unit can also generate a speech manuscript on a specific topic based on the analyzed features. For example, the generation AI may analyze the characteristics of an influential person when speaking on a specific topic and generate a speech manuscript that reflects those characteristics. This allows the generation unit to generate a speech manuscript that reflects the characteristics of the influential person. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI. For example, the generation unit may input the analyzed features to the generation AI and have the generation AI generate a speech manuscript.

[0068] The generation unit can generate presentation slides based on the analyzed features. The generation unit generates presentation slides based on, for example, the analyzed features. Presentation slides include, but are not limited to, slide design, fonts used, and color usage. The generation unit generates slides that reflect the presentation style of an influential person. The generation AI generates presentation slides based on the analyzed features using a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation AI may refer to a famous presentation by an influential person and generate slides in a similar style. The generation unit can also generate presentation slides on a specific topic based on the analyzed features. For example, the generation AI may analyze the characteristics of an influential person when giving a presentation on a specific topic and generate slides that reflect those characteristics. This allows the generation unit to generate presentation slides that reflect the characteristics of the influential person. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI. For example, the generation unit may input the analyzed features to the generation AI and cause the generation AI to generate presentation slides.

[0069] The providing unit may provide the generated speech manuscript or presentation slides to a user. For example, the providing unit may provide the generated speech manuscript or presentation slides to a user. Methods of providing the speech manuscript or presentation slides include, but are not limited to, email, cloud storage, printed materials, and the like. For example, the providing unit may store the generated speech manuscript or presentation slides in cloud storage to allow the user to access them. The providing unit may also send the generated speech manuscript or presentation slides to a user by email. This allows the user to easily receive the generated materials. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may store the generated remarks or presentation materials in cloud storage to allow the user to access them. The providing unit may also send the generated remarks or presentation materials to a user by email. This allows the user to easily receive the generated materials.

[0070] The reception unit can estimate the user's emotions and adjust the timing of information input of the person to be imitated based on the estimated user emotions. For example, if the user is nervous, the reception unit can adjust the timing to wait until the user is relaxed before inputting. Furthermore, if the user is excited, the reception unit can also adjust the timing to prompt the user to input immediately. Furthermore, if the user is tired, the reception unit can also adjust the timing to suggest a break and resume input later. This allows the timing of input to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0071] The reception unit can analyze the user's past input history and select the optimal input method. For example, the reception unit can preferentially suggest input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also automatically display personal information that the user has previously input as candidates. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. This makes it possible to select the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal input method.

[0072] When inputting information about a person to be imitated, the reception unit can filter the information based on the user's current areas of interest. For example, the reception unit can preferentially display people related to the user's current areas of interest. The reception unit can also automatically complete related keywords based on the user's areas of interest. The reception unit can also provide related materials and information based on the user's areas of interest. This makes it possible to filter information based on the user's areas of interest. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's area of ​​interest data to the generation AI and have the generation AI perform the filtering.

[0073] The reception unit can estimate the user's emotions and determine the priority of information to be input based on the estimated user emotions. For example, if the user is nervous, the reception unit can adjust the input so that important information is prioritized. Furthermore, if the user is relaxed, the reception unit can also adjust the input so that detailed information is input. Furthermore, if the user is in a hurry, the reception unit can also adjust the input so that only the most important information is input. This allows the priority of input information to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0074] When inputting information about a person to be imitated, the reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize displaying personal information related to that area. The reception unit can also provide related events and news based on the user's location information. The reception unit can also provide related materials and information based on the user's location information. This allows highly relevant information to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the user's location information data to the generation AI and cause the generation AI to select highly relevant information.

[0075] When inputting information about a person to be imitated, the reception unit can analyze the user's social media activity and input related information. For example, the reception unit can automatically display people of interest as candidates based on the user's social media activity. The reception unit can also automatically complete related keywords based on the content of the user's social media posts. The reception unit can also provide related person information based on information about the user's social media followers and friends. This makes it possible to input related information based on the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to select related information.

[0076] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is nervous, the analysis unit can provide concise and to-the-point analysis results. If the user is excited, the analysis unit can provide analysis results with visually stimulating effects. This allows the presentation method of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or can be performed without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0077] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the person. For example, in the case of an influential person, the analysis unit performs a detailed analysis. In addition, in the case of an ordinary person, the analysis unit can perform a brief analysis. In addition, in the case of a person who is famous in a particular field, the analysis unit can perform an analysis specialized to that field. This makes it possible to adjust the level of detail of the analysis based on the importance of the person. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the importance data of the person into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0078] During analysis, the analysis unit can apply different analysis algorithms depending on the person's category. For example, in the case of a politician, the analysis unit can apply an algorithm that analyzes political speeches and statements. In the case of a scientist, the analysis unit can also apply an algorithm that analyzes academic papers and lectures. In the case of an artist, the analysis unit can also apply an algorithm that analyzes artworks and interviews. This makes it possible to apply an appropriate analysis algorithm depending on the person's category. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI, for example. For example, the analysis unit can input person category data into the generation AI and have the generation AI apply the analysis algorithm.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and to-the-point analysis. If the user is relaxed, the analysis unit can also provide a detailed analysis. If the user is excited, the analysis unit can also provide an analysis with a visually stimulating effect. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI adjust the length of the analysis.

[0080] During analysis, the analysis unit can determine the analysis priority based on the time when the person's information was input. For example, the analysis unit prioritizes analysis of recently input person information. The analysis unit can also prioritize analysis of person information that the user frequently references. The analysis unit can also perform analysis at an appropriate timing based on the user's schedule. This makes it possible to determine the analysis priority based on the time when the person's information was input. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit can input data on the time when the person's information was input to the generation AI and have the generation AI determine the analysis priority.

[0081] During analysis, the analysis unit can adjust the order of analysis based on the relevance of people. For example, the analysis unit prioritizes analysis of people in whom the user is interested. The analysis unit can also prioritize analysis of people related to the user's work. The analysis unit can also prioritize analysis of people related to the user's projects. This makes it possible to adjust the order of analysis based on the relevance of people. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input person relevance data into the generation AI and have the generation AI adjust the order of analysis.

[0082] The generation unit can estimate the user's emotions and adjust the expression style of the generated utterances and presentation materials based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate utterances and presentation materials that include detailed explanations. Furthermore, if the user is nervous, the generation unit can generate utterances and presentation materials that are concise and to the point. Furthermore, if the user is excited, the generation unit can generate utterances and presentation materials that add visually stimulating effects. This allows the expression style of the utterances and presentation materials to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the expression style of the utterances and presentation materials.

[0083] The generation unit can adjust the level of detail of the generated content based on the importance of the analyzed features during generation. For example, the generation unit generates detailed statements and presentation materials based on important features. The generation unit can also generate concise statements and presentation materials based on general features. The generation unit can also generate statements and presentation materials specialized to a specific field based on features important in that field. This allows the level of detail of the generated content to be adjusted based on the importance of the analyzed features. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input importance data of the analyzed features into the generation AI and cause the generation AI to adjust the level of detail of the generated content.

[0084] During generation, the generation unit can apply different generation algorithms depending on the category of the analyzed features. For example, the generation unit can apply an algorithm that generates political speeches based on the features of politicians. The generation unit can also apply an algorithm that generates academic presentation materials based on the features of scientists. The generation unit can also apply an algorithm that generates creative presentation materials based on the features of artists. This allows an appropriate generation algorithm to be applied depending on the category of the analyzed features. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input category data of the analyzed features into the generation AI and cause the generation AI to apply the generation algorithm.

[0085] The generation unit can estimate the user's emotions and adjust the length of the generated remarks and presentation materials based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate short, to-the-point remarks and presentation materials. If the user is relaxed, the generation unit can generate longer remarks and presentation materials with detailed explanations. If the user is excited, the generation unit can generate remarks and presentation materials with visually stimulating effects. This allows the length of the remarks and presentation materials to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the length of the remarks and presentation materials.

[0086] At the time of generation, the generation unit can determine the priority of generation based on the input time of the analyzed features. The generation unit, for example, prioritizes generating utterances and presentation materials based on recently analyzed features. The generation unit can also prioritize generating utterances and presentation materials based on features frequently referenced by the user. The generation unit can also generate utterances and presentation materials at the necessary timing based on the user's schedule. This makes it possible to determine the priority of generation based on the input time of the analyzed features. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input input time data of the analyzed features to the generation AI and have the generation AI determine the priority of generation.

[0087] During generation, the generation unit can adjust the order of generation based on the relevance of the analyzed features. For example, the generation unit prioritizes generating features that the user is interested in. The generation unit can also prioritize generating features related to the user's work. The generation unit can also prioritize generating features related to the user's projects. This makes it possible to adjust the order of generation based on the relevance of the analyzed features. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input relevance data of the analyzed features into the generation AI and cause the generation AI to adjust the order of generation.

[0088] The providing unit can estimate the user's emotions and adjust the display method of the provided remarks and presentation materials based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can also provide a display method including detailed information. If the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the display method.

[0089] When providing the display method, the providing unit can select the optimal display method by referring to the user's past usage history. For example, the providing unit can prioritize providing display methods that the user has used in the past. The providing unit can also predict and provide the optimal display method from the user's past usage history. The providing unit can also analyze the user's past usage history and provide a display method that suits a specific situation. This makes it possible to select the optimal display method based on the user's past usage history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past usage history data into the generation AI and cause the generation AI to select the optimal display method.

[0090] The providing unit can estimate the user's emotions and adjust the provided remarks and operation procedures of presentation materials based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide simple and intuitive operation procedures. Furthermore, if the user is relaxed, the providing unit can provide detailed operation procedures. Furthermore, if the user is in a hurry, the providing unit can provide procedures that allow quick operation. This allows the operation procedures to be adjusted according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the operation procedures.

[0091] The providing unit can select the optimal display method by taking into consideration the user's device information when providing the display. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to select the optimal display method based on the user's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

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

[0093] The reception unit can analyze the user's past input history and select the optimal input method. For example, it can preferentially suggest input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also automatically display personal information that the user has previously input as candidates. Furthermore, the reception unit can predict and suggest an input method to be used during a specific time period based on the user's past input history. This makes it possible to select the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input history data into a generation AI and have the generation AI select the optimal input method.

[0094] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the person. For example, in the case of an influential person, a detailed analysis is performed. The analysis unit can also perform a concise analysis for an ordinary person. Furthermore, in the case of a person who is famous in a particular field, the analysis unit can perform an analysis specialized for that field. This makes it possible to adjust the level of detail of the analysis based on the importance of the person. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the importance data of the person into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0095] During generation, the generation unit can adjust the level of detail of the generated data based on the importance of the analyzed features. For example, detailed statements or presentation materials are generated based on important features. The generation unit can also generate concise statements or presentation materials based on general features. Furthermore, the generation unit can generate statements or presentation materials specialized to a specific field based on features important in that field. This allows the level of detail of the generated data to be adjusted based on the importance of the analyzed features. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input importance data of the analyzed features into the generation AI and cause the generation AI to adjust the level of detail of the generated data.

[0096] When providing the display method, the providing unit can select the optimal display method by referring to the user's past usage history. For example, the providing unit can prioritize the display method that the user has used in the past. The providing unit can also predict and provide the optimal display method from the user's past usage history. Furthermore, the providing unit can analyze the user's past usage history and provide a display method that suits a specific situation. This makes it possible to select the optimal display method based on the user's past usage history. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past usage history data into the generation AI and cause the generation AI to select the optimal display method.

[0097] When providing the display information, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a display method that is simple and highly visible. This makes it possible to select the optimal display method based on the user's device information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's device information data into the generation AI and cause the generation AI to select the optimal display method.

[0098] The reception unit can estimate the user's emotions and adjust the timing of information input of the person to be imitated based on the estimated user emotions. For example, if the user is nervous, the reception unit can adjust the timing to wait until the user is relaxed before inputting. The reception unit can also adjust the timing to prompt the user to input immediately if the user is excited. Furthermore, if the user is tired, the reception unit can also adjust the timing to suggest taking a break and resume input later. This allows the timing of input to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI estimate the user's emotions.

[0099] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is nervous, the analysis unit can provide concise and to-the-point analysis results. If the user is excited, the analysis unit can provide analysis results with visually stimulating effects. This allows the presentation method of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0100] The generation unit can estimate the user's emotions and adjust the expression style of the generated utterances and presentation materials based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate utterances and presentation materials that include detailed explanations. If the user is nervous, the generation unit can generate utterances and presentation materials that are concise and to the point. If the user is excited, the generation unit can generate utterances and presentation materials that add visually stimulating effects. This allows the expression style of the utterances and presentation materials to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI, or without an AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to adjust the expression style of the utterances and presentation materials.

[0101] The providing unit can estimate the user's emotions and adjust the display method of the provided remarks and presentation materials based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible display method. If the user is relaxed, the providing unit can also provide a display method including detailed information. If the user is in a hurry, the providing unit can also provide a display method that focuses on the main points. This allows the display method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's facial expression data into the generation AI and have the generation AI adjust the display method.

[0102] The providing unit can estimate the user's emotions and adjust the provided remarks and operation procedures for presentation materials based on the estimated user emotions. For example, if the user is nervous, the providing unit can provide simple and intuitive operation procedures. Furthermore, if the user is relaxed, the providing unit can also provide detailed operation procedures. Furthermore, if the user is in a hurry, the providing unit can also provide procedures that allow for quick operation. This allows the operation procedures to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's facial expression data into the generation AI and have the generation AI adjust the operation procedures.

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

[0104] Step 1: The reception unit inputs information about the person the user wants to imitate. This information includes the way the person speaks, their presentation style, and the words they use. The reception unit provides an interface for the user to input information about the influential person. Step 2: The analysis unit uses a generation AI to analyze the information entered by the reception unit and extract the person's characteristics. The analysis unit analyzes the influential person's speaking style, presentation style, and choice of words. The generation AI uses a text generation AI (e.g., LLM) to extract characteristics based on the entered information. Step 3: The generator uses the generative AI to generate speeches and presentation materials based on the features extracted by the analyzer. The generator generates speech manuscripts and presentation slides based on the analyzed features. Step 4: The providing unit provides the generated remarks and presentation materials to the user. The providing unit provides an interface for providing the generated speech manuscript and presentation slides to the user. The providing unit stores the generated remarks and presentation materials in cloud storage so that the user can access them. The providing unit can also send the generated remarks and presentation materials to the user by email.

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

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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. The data generation model 58 receives a prompt including an instruction, as well as inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats including 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. The AIs other than the generation AI are, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but are not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or in whole by AI, but are not limited to these 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.

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

[0108] Each of the multiple elements, including the above-described reception unit, analysis unit, generation unit, and provision unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and provides an interface for the user to input information about a person they wish to imitate. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI to extract characteristics of the person. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates statements and presentation materials based on the extracted characteristics. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides an interface for providing the generated statements and presentation materials to the user. The correspondence between each unit and the device or control unit is not limited to the above example and various modifications are possible.

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

[0110] 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.

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

[0112] 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.

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

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

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] 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.

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

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

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0124] Each of the multiple elements, including the above-described reception unit, analysis unit, generation unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for the user to input information about a person they wish to imitate. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI and extracts characteristics of the person. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates statements and presentation materials based on the extracted characteristics. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides an interface for providing the generated statements and presentation materials to the user. The correspondence between each unit and the device or control unit is not limited to the above example and various modifications are possible.

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

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

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

[0128] 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.

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

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

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] 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.

[0133] 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.

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

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

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0140] Each of the multiple elements, including the above-described reception unit, analysis unit, generation unit, and provision unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and provides an interface for the user to input information about a person they wish to imitate. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI to extract characteristics of the person. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates statements and presentation materials based on the extracted characteristics. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides an interface for providing the generated statements and presentation materials to the user. The correspondence between each unit and the device or control unit is not limited to the above example and can be changed in various ways.

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

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

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

[0144] 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.

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

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

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

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

[0149] 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.

[0150] 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.

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

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in one or more data formats, such as 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 identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

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

[0157] Each of the multiple elements, including the above-described reception unit, analysis unit, generation unit, and provision unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides an interface for the user to input information about a person they wish to imitate. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI and extracts characteristics of the person. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates statements and presentation materials based on the extracted characteristics. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides an interface for providing the generated statements and presentation materials to the user. The correspondence between each unit and the device or control unit is not limited to the above example and can be changed in various ways.

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

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

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

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

[0165] 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.

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

[0167] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0168] 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.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

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

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0176] (Appendix 1) a reception section for inputting information about a person to be imitated; an analysis unit that analyzes the information input by the reception unit and extracts characteristics of the person; a generation unit that generates statements or presentation materials based on the features extracted by the analysis unit; a providing unit that provides the remarks or presentation materials generated by the generating unit. A system characterized by: (Appendix 2) The analysis unit Analyze the speaking and presentation styles of influential people and their choice of words 2. The system of claim 1. (Appendix 3) The generation unit Generate a speech transcript based on the analyzed features 2. The system of claim 1. (Appendix 4) The generation unit Generate presentation slides based on the analyzed features 2. The system of claim 1. (Appendix 5) The providing unit Providing the generated speech script or presentation slides to the user 2. The system of claim 1. (Appendix 6) The reception unit Using a method for estimating a user's emotions, the timing of inputting information of the person to be imitated is adjusted based on the estimated user's emotions. 2. The system of claim 1. (Appendix 7) The reception unit Analyze the user's past input history and select the optimal input method 2. The system of claim 1. (Appendix 8) The reception unit When entering information about the person you want to impersonate, it filters based on the user's current interests. 2. The system of claim 1. (Appendix 9) The reception unit Using a method for estimating user emotions, the system prioritizes input information based on the estimated user emotions. 2. The system of claim 1. (Appendix 10) The reception unit When entering information about a person you want to imitate, the system takes into account the user's geographic location information and prioritizes entering information that is highly relevant. 2. The system of claim 1. (Appendix 11) The reception unit When entering information about a person you want to imitate, the app analyzes the user's social media activity and enters relevant information. 2. The system of claim 1. (Appendix 12) The analysis unit Using a method for estimating user emotions, the presentation of the analysis is adjusted based on the estimated user emotions. 2. The system of claim 1. (Appendix 13) The analysis unit During analysis, adjust the level of analysis detail based on the importance of the person 2. The system of claim 1. (Appendix 14) The analysis unit During analysis, different analysis algorithms are applied depending on the person category. 2. The system of claim 1. (Appendix 15) The analysis unit Using a method for estimating user emotions, the length of the analysis is adjusted based on the estimated user emotions. 2. The system of claim 1. (Appendix 16) The analysis unit During analysis, the analysis priority is determined based on when a person's information was entered. 2. The system of claim 1. (Appendix 17) The analysis unit During analysis, adjust the order of analysis based on person relevance 2. The system of claim 1. (Appendix 18) The generation unit Using a method for estimating user emotions, the system adjusts the way utterances and presentation materials are generated based on the estimated user emotions. 2. The system of claim 1. (Appendix 19) The generation unit At generation time, adjust the level of detail of the generation based on the importance of the analyzed features. 2. The system of claim 1. (Appendix 20) The generation unit During generation, different generation algorithms are applied depending on the category of the analyzed features. 2. The system of claim 1. (Appendix 21) The generation unit Using a method to estimate user emotions, the length of generated utterances and presentation materials is adjusted based on the estimated user emotions. 2. The system of claim 1. (Appendix 22) The generation unit At generation time, generation priorities are determined based on the input timing of the analyzed features. 2. The system of claim 1. (Appendix 23) The generation unit At generation time, adjust the generation order based on the relevance of the analyzed features. 2. The system of claim 1. (Appendix 24) The providing unit Using a method for estimating user emotions, the system adjusts the presentation method and the utterances provided based on the estimated user emotions. 2. The system of claim 1. (Appendix 25) The providing unit When providing the service, the optimal display method is selected by referring to the user's past usage history. 2. The system of claim 1. (Appendix 26) The providing unit Using a method for estimating user emotions, the system adjusts the statements provided and the operating procedures for presentation materials based on the estimated user emotions. 2. The system of claim 1. (Appendix 27) The providing unit When providing the content, the optimal display method is selected taking into account the user's device information. 2. The system of claim 1. [Explanation of symbols]

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

Claims

1. a reception section for inputting information about a person to be imitated; an analysis unit that analyzes the information input by the reception unit and extracts characteristics of the person; a generation unit that generates statements or presentation materials based on the features extracted by the analysis unit; a providing unit that provides the remarks or presentation materials generated by the generating unit, The reception unit By inputting the user's field of interest data into the generation AI, people related to the user's current field of interest are filtered as people to imitate. A system characterized by:

2. The analysis unit Analyze the speaking and presentation styles of influential people and their choice of words 2. The system of claim 1.

3. The generation unit generating a speech script or presentation slides based on the features extracted by the analysis unit; 2. The system of claim 1.

4. The providing unit Providing the generated speech script or presentation slides to the user 4. The system of claim 3.

5. The reception unit Using a method for estimating a user's emotions, the timing of inputting information of the person to be imitated is adjusted based on the estimated user's emotions.

2. The system of claim 1.

6. The reception unit Analyze the user's past input history and select the optimal input method 2. The system of claim 1.

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