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 a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it was difficult to create statements or presentation materials that imitated 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 analysis unit, a generation unit, and a provision unit. The receiving unit inputs information about a person to be imitated. The analysis unit analyzes the information input by the receiving unit and extracts characteristics of the person. The generation unit generates a statement or a presentation material based on the characteristics extracted by the analysis unit. The provision unit provides the statement or the presentation material generated by the generation unit. Effect of the Invention

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

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The system according to the embodiment of the present invention is a system that imitates how great or influential people communicate things, and creates remarks and presentation materials that fully emulate that person. In this system, a user inputs information about a person they want to imitate, and a generation AI analyzes the characteristics of that person and generates remarks and presentation materials. For example, when a user inputs information about an influential person, this information is input to the generation AI. The generation AI then analyzes the input information and extracts the characteristics of that person. The generation AI analyzes the person's speaking style, presentation style, and choice of words used. For example, if the person is influential, the generation AI analyzes the person's famous speeches and writings and extracts characteristics. Based on the extracted characteristics, the generation AI generates remarks and presentation materials that fully emulate the person the user wants to imitate. 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 things 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 imitate. This system can also be used in education and training, helping users acquire effective communication skills. This allows the system to communicate things like the user is 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 to be imitated. The information about the person to be imitated includes, but is not limited to, for example, a way of speaking, a presentation style, and words used. The reception unit provides, for example, an interface for a user to input information about an influential person. The analysis unit uses a generation AI to analyze the information input by the reception unit and extracts characteristics of the person. The analysis unit analyzes, for example, the influential person's way of speaking, presentation style, and choice of words used. The generation AI uses a text generation AI (for example, LLM) to extract characteristics based on the input information. 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. The generation unit generates, for example, a speech manuscript based on the analyzed characteristics. The generation unit can also generate presentation slides based on the analyzed characteristics. The generation AI generates remarks and presentation materials that fully emulate the person the user wants to imitate based on the extracted features. The providing unit provides the generated remarks and presentation materials to the user. The providing unit provides, for example, an interface for providing the generated speech manuscript and presentation slides to the user. This allows the system according to the embodiment to communicate things like a great person or an influential person. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, 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. This allows the user to easily receive the generated materials.

[0030] The reception unit inputs information about a person to be imitated. The information about a person to be imitated includes, but is not limited to, for example, a way of speaking, a presentation style, and words used. The reception unit provides, for example, an interface for a user to input information about an influential person. Specifically, the reception unit provides a form or questions for a user to input detailed information about a person to be imitated. This includes the name of the person to be imitated, an occupation, a link to a specific speech or presentation, and a list of specific phrases and words used. Furthermore, the reception unit also provides a function for a user to upload audio or video clips, which allows detailed characteristics of the person to be imitated, such as the way of speaking, tone of voice, and rhythm, to be collected. The reception unit has a database for centrally managing information input by the user and transmitting it to the analysis unit. This allows the information input by the user to be passed to the analysis unit accurately and efficiently, and subsequent processing is performed smoothly. Furthermore, the reception unit provides a preview and editing function for the information input by the user, allowing the user to check the input contents and correct them as necessary. This allows the reception unit to provide support for the user to input information about the person to be imitated accurately and in detail, thereby improving the accuracy and reliability of the entire system.

[0031] The analysis unit uses the generation AI to analyze the information input by the reception unit and extracts the characteristics of the person. For example, the analysis unit analyzes the influential person's speaking style, presentation style, and choice of words. The generation AI uses a text generation AI (for example, LLM) to extract characteristics based on the input information. Specifically, the generation AI analyzes famous speeches and writings of influential people and extracts characteristics. For example, the generation AI uses natural language processing technology to analyze in detail characteristics such as the structure of speech and sentences, grammar, vocabulary choice, rhythm, and intonation. In addition, the generation AI uses voice recognition technology to extract speaking characteristics from voice data and analyze 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 extract features with higher accuracy by utilizing past data and statistical information. For example, more accurate analysis results can be obtained by collecting a large amount of data on past speeches and presentations by influential people and extracting features based on this data. 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 the generation AI to generate remarks 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. The generation AI generates remarks and presentation materials that fully emulate the person the user wants to imitate based on the extracted features. Specifically, the generation AI uses natural language generation technology to create a speech manuscript that matches the speaking style and style 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 the design and content of the presentation slides to match the style of the person the user wants to imitate. This includes the slide layout, color usage, font selection, and the placement of charts and graphs. The generation unit also provides an interface that allows the user to easily edit the generated speech manuscript and presentation slides. This allows the user to customize the generated materials to suit their needs. In addition, the generation unit also has a feedback function to evaluate the quality of the generated materials and make corrections and improvements as necessary. This enables the generation unit to generate high-quality statements and presentation materials that faithfully reproduce the characteristics of the person the user wishes to imitate, thereby supporting effective communication according to the user's objectives.

[0033] The providing unit provides the generated remarks and presentation materials to the user. The providing unit provides, for example, 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 remarks and presentation materials to the user by email. This allows 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 and enabling the user to easily share them with others. For example, the user can send a link of the generated speech manuscript or presentation slides to a colleague or friend to edit or review them together. The providing unit also has a function for collecting user feedback and continuously improving the quality of the generated materials and the method of providing them. This allows the providing unit to support the user in effectively using the generated materials and improve the user experience of the entire system. Furthermore, the providing unit also has a function for ensuring the security of the generated materials and ensuring that the user's data is safely protected. This allows the providing unit to provide an environment in which the user can use the generated materials with peace of mind.

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

[0035] The generation unit can generate a speech manuscript based on the analyzed features. The generation unit generates a speech manuscript based on the analyzed features, for example. The speech manuscript includes, but is not limited to, the structure, length, and words used. The generation unit generates a speech manuscript that reflects, for example, the speaking style and style of an influential person. The generation AI generates a speech manuscript based on the analyzed features using a text generation AI (for example, LLM). For example, the generation AI generates a speech manuscript in a similar style by referring to a famous speech of an influential person. The generation unit can also generate a speech manuscript on a specific theme based on the analyzed features. For example, the generation AI analyzes the characteristics of an influential person when speaking about a specific theme and generates a speech manuscript that reflects the characteristics. This allows the generation unit to generate a speech manuscript that reflects the characteristics of an influential person. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the analyzed features to the generation AI and cause the generation AI to generate a speech manuscript.

[0036] The generating unit can generate a presentation slide based on the analyzed features. The generating unit generates a presentation slide based on the analyzed features, for example. The presentation slide includes, but is not limited to, the design of the slide, the font used, and the color usage. The generating unit generates a slide that reflects, for example, the presentation style of an influential person. The generating AI generates a presentation slide based on the analyzed features using a text generating AI (for example, LLM) or a multimodal generating AI. For example, the generating AI generates a slide in a similar style by referring to a famous presentation of an influential person. The generating unit can also generate a presentation slide on a specific theme based on the analyzed features. For example, the generating AI analyzes the features of an influential person when making a presentation on a specific theme and generates a slide that reflects the features. This allows the generating unit to generate a presentation slide that reflects the features of an influential person. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, the generating AI, or may be performed without using the generating AI. For example, the generating unit can input the analyzed features to the generating AI and cause the generating AI to generate a presentation slide.

[0037] The providing unit can provide the generated speech manuscript or presentation slides to the user. The providing unit, for example, provides the generated speech manuscript or presentation slides to the user. Methods of providing include, for example, email, cloud storage, printed matter, and the like, but are not limited to such examples. The providing unit, for example, stores the generated speech manuscript or presentation slides in cloud storage so that the user can access them. The providing unit can also send the generated speech manuscript or presentation slides to the user by email. This allows the user to easily receive the generated materials. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit stores the generated remarks or presentation materials in cloud storage so that the user can access them. The providing unit can also send the generated remarks or presentation materials to the 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 preferentially suggests an input method (such as voice or text) that the user has frequently used in the past. The reception unit can also automatically display personal information that the user has input in the past as a candidate. The reception unit can also predict and suggest an input method to be used in a specific time period from the user's past input history. This makes it possible to select the optimal input method based on the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past input history data to the generation AI and cause the generation AI to select the optimal input method.

[0039] The reception unit can perform filtering based on the user's current field of interest when inputting information on a person to be imitated. For example, the reception unit preferentially displays people related to the field in which the user is currently interested. The reception unit can also automatically complete related keywords based on the user's field of interest. The reception unit can also provide related materials and information based on the user's field of interest. This makes it possible to filter information based on the user's field of interest. A part or all of the above-mentioned 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 field of interest data to the generation AI and cause the generation AI to perform filtering.

[0040] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the user when inputting information on a person to be imitated. For example, when the user is in a specific area, the reception unit preferentially displays person information related to the 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 makes it possible to preferentially input highly relevant information based on the user's geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using 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] The reception unit can analyze the user's social media activities and input related information when inputting information on a person to be imitated. For example, the reception unit can automatically display people of interest as candidates based on the user's social media activities. 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 on the user's followers and friends on social media. This makes it possible to input related information based on the user's social media activities. A part or all of the above-mentioned 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] The analysis unit can adjust the level of detail of the analysis based on the importance of the person during the analysis. For example, in the case of an influential person, the analysis unit performs a detailed analysis. In addition, in the case of a general person, the analysis unit can also perform a concise analysis. In addition, in the case of a person famous in a specific field, the analysis unit can also perform an analysis specialized in that field. This makes it possible to adjust the level of detail of the analysis based on the importance of the person. A part 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 to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the person during analysis. For example, in the case of a politician, the analysis unit applies an algorithm that analyzes political speeches and statements. In addition, in the case of a scientist, the analysis unit can also apply an algorithm that analyzes academic papers and lectures. In addition, in the case of an artist, the analysis unit can apply an algorithm that analyzes works and interviews. This makes it possible to apply an appropriate analysis algorithm depending on the category 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 person category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0044] The analysis unit can determine the priority of the analysis based on the time when the person's information is input during the analysis. For example, the analysis unit preferentially analyzes the most recently input person information. The analysis unit can also preferentially analyze the person information that the user frequently refers to. The analysis unit can also perform the analysis at a necessary timing based on the user's schedule. This makes it possible to determine the priority of the analysis based on the time when the person's information is input. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the person's information input time data to the generation AI and have the generation AI execute the determination of the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of people during analysis. For example, the analysis unit preferentially analyzes people in whom the user is interested. The analysis unit can also preferentially analyze people related to the user's work. The analysis unit can also preferentially analyze people related to the user's project. This makes it possible to adjust the order of analysis based on the relevance of people. A part 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 to the generation AI and cause the generation AI to adjust the order of analysis.

[0046] The generation unit can adjust the level of detail of the generation based on the importance of the analyzed features at the time of generation. The generation unit generates detailed remarks and presentation materials, for example, based on important features. The generation unit can also generate concise remarks and presentation materials based on general features. The generation unit can also generate remarks and presentation materials specialized in a specific field based on features important in that field. This makes it possible to adjust the level of detail of the generation based on the importance of the analyzed features. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input importance data of the analyzed features to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0047] The generating unit can apply different generating algorithms depending on the category of the analyzed features at the time of generation. For example, the generating unit applies an algorithm for generating a political speech based on the features of a politician. The generating unit can also apply an algorithm for generating academic presentation materials based on the features of a scientist. The generating unit can also apply an algorithm for generating creative presentation materials based on the features of an artist. This allows an appropriate generating algorithm to be applied depending on the category of the analyzed features. Some or all of the above-mentioned processing in the generating unit may be performed using, for example, a generating AI, or may be performed without using a generating AI. For example, the generating unit can input category data of the analyzed features to the generating AI and cause the generating AI to apply the generating algorithm.

[0048] The generation unit can determine the priority of generation based on the input time of the analyzed features at the time of generation. The generation unit generates remarks and presentation materials preferentially based on, for example, recently analyzed features. The generation unit can also generate remarks and presentation materials preferentially based on features frequently referred to by the user. The generation unit can also generate remarks and presentation materials at a 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. A part or all of the above-mentioned 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 input time data of the analyzed features to the generation AI and cause the generation AI to execute the determination of the priority of generation.

[0049] The generation unit can adjust the order of generation based on the relevance of the analyzed features at the time of generation. For example, the generation unit preferentially generates features in which the user is interested. The generation unit can also preferentially generate features related to the user's work. The generation unit can also preferentially generate features related to the user's project. This makes it possible to adjust the order of generation based on the relevance of the analyzed features. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using 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 to the generation AI and cause the generation AI to adjust the order of generation.

[0050] The providing unit can select the optimal display method by referring to the user's past usage history at the time of providing. For example, the providing unit preferentially provides the display method used by the user 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 according to a specific situation. This makes it possible to select the optimal display method based on the user's past usage history. A part or all of the above-mentioned 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 past usage history data to the generating AI and cause the generating AI to select the optimal display method.

[0051] The providing unit can select the optimal display method in consideration of the user's device information at the time of providing. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. In addition, when 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. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's device information data to the generating AI and cause the generating 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, the reception unit preferentially suggests 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 in a specific time period from the user's past input history. This makes it possible to select the optimal input method based on the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past input history data to the generation AI and cause the generation AI to select the optimal input method.

[0054] The analysis unit can adjust the level of detail of the analysis based on the importance of the person during the analysis. For example, in the case of an influential person, a detailed analysis is performed. In addition, the analysis unit can also perform a concise analysis in the case of a general person. Furthermore, in the case of a person famous in a specific field, the analysis unit can also perform an analysis specialized in that field. This makes it possible to adjust the level of detail of the analysis based on the importance of the person. A part 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 to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0055] The generation unit can adjust the level of detail of the generation based on the importance of the analyzed features at the time of generation. For example, detailed remarks and presentation materials are generated based on important features. The generation unit can also generate concise remarks and presentation materials based on general features. Furthermore, the generation unit can generate remarks and presentation materials specialized in a specific field based on features important in that field. This makes it possible to adjust the level of detail of the generation based on the importance of the analyzed features. A part or all of the above-mentioned 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 importance data of the analyzed features to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0056] The providing unit can select the optimal display method by referring to the user's past usage history at the time of providing. For example, the display method used by the user in the past is preferentially provided. 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 according to a specific situation. This makes it possible to select the optimal display method based on the user's past usage history. A part or all of the above-mentioned 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 past usage history data to the generating AI and cause the generating AI to select the optimal display method.

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

[0058] The process flow of the first embodiment will be briefly described below.

[0059] Step 1: The reception unit inputs information about the person to be imitated. The information about the person to be imitated includes the way of speaking, presentation style, words used, etc. The reception unit provides an interface for the user to input information about the influential person. Step 2: The analysis unit uses the 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 generation AI to generate speech and presentation materials based on the features extracted by the analyzer. The generator generates a speech manuscript 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 and allows the user to access them. The providing unit can also send the generated remarks and presentation materials to the user by email.

[0060] (Example 2) The system according to the embodiment of the present invention is a system that imitates how great or influential people communicate things, and creates remarks and presentation materials that fully emulate that person. In this system, a user inputs information about a person they want to imitate, and a generation AI analyzes the characteristics of that person and generates remarks and presentation materials. For example, when a user inputs information about an influential person, this information is input to the generation AI. The generation AI then analyzes the input information and extracts the characteristics of that person. The generation AI analyzes the person's speaking style, presentation style, and choice of words used. For example, if the person is influential, the generation AI analyzes the person's famous speeches and writings and extracts characteristics. Based on the extracted characteristics, the generation AI generates remarks and presentation materials that fully emulate the person the user wants to imitate. 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 things 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 imitate. This system can also be used in education and training, helping users acquire effective communication skills. This allows the system to communicate things like the user is 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 to be imitated. The information about the person to be imitated includes, but is not limited to, for example, a way of speaking, a presentation style, and words used. The reception unit provides, for example, an interface for a user to input information about an influential person. The analysis unit uses a generation AI to analyze the information input by the reception unit and extracts characteristics of the person. The analysis unit analyzes, for example, the influential person's way of speaking, presentation style, and choice of words used. The generation AI uses a text generation AI (for example, LLM) to extract characteristics based on the input information. 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. The generation unit generates, for example, a speech manuscript based on the analyzed characteristics. The generation unit can also generate presentation slides based on the analyzed characteristics. The generation AI generates remarks and presentation materials that fully emulate the person the user wants to imitate based on the extracted features. The providing unit provides the generated remarks and presentation materials to the user. The providing unit provides, for example, an interface for providing the generated speech manuscript and presentation slides to the user. This allows the system according to the embodiment to communicate things like a great person or an influential person. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, 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. This allows the user to easily receive the generated materials.

[0062] The reception unit inputs information about a person to be imitated. The information about a person to be imitated includes, but is not limited to, for example, a way of speaking, a presentation style, and words used. The reception unit provides, for example, an interface for a user to input information about an influential person. Specifically, the reception unit provides a form or questions for a user to input detailed information about a person to be imitated. This includes the name of the person to be imitated, an occupation, a link to a specific speech or presentation, and a list of specific phrases and words used. Furthermore, the reception unit also provides a function for a user to upload audio or video clips, which allows detailed characteristics of the person to be imitated, such as the way of speaking, tone of voice, and rhythm, to be collected. The reception unit has a database for centrally managing information input by the user and transmitting it to the analysis unit. This allows the information input by the user to be passed to the analysis unit accurately and efficiently, and subsequent processing is performed smoothly. Furthermore, the reception unit provides a preview and editing function for the information input by the user, allowing the user to check the input contents and correct them as necessary. This allows the reception unit to provide support for the user to input information about the person to be imitated accurately and in detail, thereby improving the accuracy and reliability of the entire system.

[0063] The analysis unit uses the generation AI to analyze the information input by the reception unit and extracts the characteristics of the person. For example, the analysis unit analyzes the influential person's speaking style, presentation style, and choice of words. The generation AI uses a text generation AI (for example, LLM) to extract characteristics based on the input information. Specifically, the generation AI analyzes famous speeches and writings of influential people and extracts characteristics. For example, the generation AI uses natural language processing technology to analyze in detail characteristics such as the structure of speech and sentences, grammar, vocabulary choice, rhythm, and intonation. In addition, the generation AI uses voice recognition technology to extract speaking characteristics from voice data and analyze 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 extract features with higher accuracy by utilizing past data and statistical information. For example, more accurate analysis results can be obtained by collecting a large amount of data on past speeches and presentations by influential people and extracting features based on this data. 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 the generation AI to generate remarks 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. The generation AI generates remarks and presentation materials that fully emulate the person the user wants to imitate based on the extracted features. Specifically, the generation AI uses natural language generation technology to create a speech manuscript that matches the speaking style and style 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 the design and content of the presentation slides to match the style of the person the user wants to imitate. This includes the slide layout, color usage, font selection, and the placement of charts and graphs. The generation unit also provides an interface that allows the user to easily edit the generated speech manuscript and presentation slides. This allows the user to customize the generated materials to suit their needs. In addition, the generation unit also has a feedback function to evaluate the quality of the generated materials and make corrections and improvements as necessary. This enables the generation unit to generate high-quality statements and presentation materials that faithfully reproduce the characteristics of the person the user wishes to imitate, thereby supporting effective communication according to the user's objectives.

[0065] The providing unit provides the generated remarks and presentation materials to the user. The providing unit provides, for example, 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 remarks and presentation materials to the user by email. This allows 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 and enabling the user to easily share them with others. For example, the user can send a link of the generated speech manuscript or presentation slides to a colleague or friend to edit or review them together. The providing unit also has a function for collecting user feedback and continuously improving the quality of the generated materials and the method of providing them. This allows the providing unit to support the user in effectively using the generated materials and improve the user experience of the entire system. Furthermore, the providing unit also has a function for ensuring the security of the generated materials and ensuring that the user's data is safely protected. This allows the providing unit to provide an environment in which the user can use the generated materials with peace of mind.

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

[0067] The generation unit can generate a speech manuscript based on the analyzed features. The generation unit generates a speech manuscript based on the analyzed features, for example. The speech manuscript includes, but is not limited to, the structure, length, and words used. The generation unit generates a speech manuscript that reflects, for example, the speaking style and style of an influential person. The generation AI generates a speech manuscript based on the analyzed features using a text generation AI (for example, LLM). For example, the generation AI generates a speech manuscript in a similar style by referring to a famous speech of an influential person. The generation unit can also generate a speech manuscript on a specific theme based on the analyzed features. For example, the generation AI analyzes the characteristics of an influential person when speaking about a specific theme and generates a speech manuscript that reflects the characteristics. This allows the generation unit to generate a speech manuscript that reflects the characteristics of an influential person. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the analyzed features to the generation AI and cause the generation AI to generate a speech manuscript.

[0068] The generating unit can generate a presentation slide based on the analyzed features. The generating unit generates a presentation slide based on the analyzed features, for example. The presentation slide includes, but is not limited to, the design of the slide, the font used, and the color usage. The generating unit generates a slide that reflects, for example, the presentation style of an influential person. The generating AI generates a presentation slide based on the analyzed features using a text generating AI (for example, LLM) or a multimodal generating AI. For example, the generating AI generates a slide in a similar style by referring to a famous presentation of an influential person. The generating unit can also generate a presentation slide on a specific theme based on the analyzed features. For example, the generating AI analyzes the features of an influential person when making a presentation on a specific theme and generates a slide that reflects the features. This allows the generating unit to generate a presentation slide that reflects the features of an influential person. A part or all of the above-mentioned processing in the generating unit may be performed using, for example, the generating AI, or may be performed without using the generating AI. For example, the generating unit can input the analyzed features to the generating AI and cause the generating AI to generate a presentation slide.

[0069] The providing unit can provide the generated speech manuscript or presentation slides to the user. The providing unit, for example, provides the generated speech manuscript or presentation slides to the user. Methods of providing include, for example, email, cloud storage, printed matter, and the like, but are not limited to such examples. The providing unit, for example, stores the generated speech manuscript or presentation slides in cloud storage so that the user can access them. The providing unit can also send the generated speech manuscript or presentation slides to the user by email. This allows the user to easily receive the generated materials. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit stores the generated remarks or presentation materials in cloud storage so that the user can access them. The providing unit can also send the generated remarks or presentation materials to the user by email. This allows the user to easily receive the generated materials.

[0070] The reception unit can estimate the user's emotion and adjust the timing of information input of the person to be imitated based on the estimated user's emotion. For example, when the user is nervous, the reception unit adjusts to wait for input until the user is relaxed. In addition, the reception unit can also adjust to prompt input immediately when the user is excited. In addition, when the user is tired, the reception unit can also adjust to suggest a break and resume input later. In this way, the input timing can be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of the user's emotion.

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

[0072] The reception unit can perform filtering based on the user's current field of interest when inputting information on a person to be imitated. For example, the reception unit preferentially displays people related to the field in which the user is currently interested. The reception unit can also automatically complete related keywords based on the user's field of interest. The reception unit can also provide related materials and information based on the user's field of interest. This makes it possible to filter information based on the user's field of interest. A part or all of the above-mentioned 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 field of interest data to the generation AI and cause the generation AI to perform filtering.

[0073] The reception unit can estimate the user's emotion and determine the priority of information to be input based on the estimated user's emotion. For example, when the user is nervous, the reception unit adjusts to input important information with priority. Also, the reception unit can adjust to input detailed information when the user is relaxed. Also, the reception unit can adjust to input only the most important information when the user is in a hurry. This allows the priority of input information to be determined according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of the user's emotion.

[0074] The reception unit can preferentially input highly relevant information in consideration of the geographical location information of the user when inputting information on a person to be imitated. For example, when the user is in a specific area, the reception unit preferentially displays person information related to the 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 makes it possible to preferentially input highly relevant information based on the user's geographical location information. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using 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] The reception unit can analyze the user's social media activities and input related information when inputting information on a person to be imitated. For example, the reception unit can automatically display people of interest as candidates based on the user's social media activities. 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 on the user's followers and friends on social media. This makes it possible to input related information based on the user's social media activities. A part or all of the above-mentioned 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 emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit provides a detailed analysis result. In addition, when the user is nervous, the analysis unit can also provide a concise and to the point analysis result. In addition, when the user is excited, the analysis unit can also provide an analysis result with a visually stimulating effect. This makes it possible to adjust the expression method of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data to the generation AI and cause the generation AI to adjust the expression method of the analysis.

[0077] The analysis unit can adjust the level of detail of the analysis based on the importance of the person during the analysis. For example, in the case of an influential person, the analysis unit performs a detailed analysis. In addition, in the case of a general person, the analysis unit can also perform a concise analysis. In addition, in the case of a person famous in a specific field, the analysis unit can also perform an analysis specialized in that field. This makes it possible to adjust the level of detail of the analysis based on the importance of the person. A part 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 to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0078] The analysis unit can apply different analysis algorithms depending on the category of the person during analysis. For example, in the case of a politician, the analysis unit applies an algorithm that analyzes political speeches and statements. In addition, in the case of a scientist, the analysis unit can also apply an algorithm that analyzes academic papers and lectures. In addition, in the case of an artist, the analysis unit can apply an algorithm that analyzes works and interviews. This makes it possible to apply an appropriate analysis algorithm depending on the category 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 person category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0079] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is in a hurry, the analysis unit provides a short and to the point analysis. The analysis unit can also provide a detailed analysis when the user is relaxed. The analysis unit can also provide an analysis with a visually stimulating effect when the user is excited. This allows the length of the analysis to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data to the generation AI and cause the generation AI to adjust the length of the analysis.

[0080] The analysis unit can determine the priority of the analysis based on the time when the person's information is input during the analysis. For example, the analysis unit preferentially analyzes the most recently input person information. The analysis unit can also preferentially analyze the person information that the user frequently refers to. The analysis unit can also perform the analysis at a necessary timing based on the user's schedule. This makes it possible to determine the priority of the analysis based on the time when the person's information is input. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the person's information input time data to the generation AI and have the generation AI execute the determination of the analysis priority.

[0081] The analysis unit can adjust the order of analysis based on the relevance of people during analysis. For example, the analysis unit preferentially analyzes people in whom the user is interested. The analysis unit can also preferentially analyze people related to the user's work. The analysis unit can also preferentially analyze people related to the user's project. This makes it possible to adjust the order of analysis based on the relevance of people. A part 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 to the generation AI and cause the generation AI to adjust the order of analysis.

[0082] The generation unit can estimate the user's emotion and adjust the expression method of the generated remarks and presentation materials based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates remarks and presentation materials including detailed explanations. In addition, when the user is nervous, the generation unit can also generate remarks and presentation materials that are concise and to the point. In addition, when the user is excited, the generation unit can also generate remarks and presentation materials with visually stimulating effects. This makes it possible to adjust the expression method of the remarks and presentation materials according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's facial expression data to the generation AI and cause the generation AI to adjust the expression method of the remarks and presentation materials.

[0083] The generation unit can adjust the level of detail of the generation based on the importance of the analyzed features at the time of generation. The generation unit generates detailed remarks and presentation materials, for example, based on important features. The generation unit can also generate concise remarks and presentation materials based on general features. The generation unit can also generate remarks and presentation materials specialized in a specific field based on features important in that field. This makes it possible to adjust the level of detail of the generation based on the importance of the analyzed features. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input importance data of the analyzed features to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0084] The generating unit can apply different generating algorithms depending on the category of the analyzed features at the time of generation. For example, the generating unit applies an algorithm for generating a political speech based on the features of a politician. The generating unit can also apply an algorithm for generating academic presentation materials based on the features of a scientist. The generating unit can also apply an algorithm for generating creative presentation materials based on the features of an artist. This allows an appropriate generating algorithm to be applied depending on the category of the analyzed features. Some or all of the above-mentioned processing in the generating unit may be performed using, for example, a generating AI, or may be performed without using a generating AI. For example, the generating unit can input category data of the analyzed features to the generating AI and cause the generating AI to apply the generating algorithm.

[0085] The generation unit can estimate the user's emotion and adjust the length of the remarks and presentation materials to be generated based on the estimated user's emotion. For example, when the user is in a hurry, the generation unit generates short and to the point remarks and presentation materials. In addition, when the user is relaxed, the generation unit can also generate longer remarks and presentation materials including detailed explanations. In addition, when the user is excited, the generation unit can also generate remarks and presentation materials with visually stimulating effects. This makes it possible to adjust the length of the remarks and presentation materials according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's facial expression data to the generation AI and cause the generation AI to adjust the length of the remarks and presentation materials.

[0086] The generation unit can determine the priority of generation based on the input time of the analyzed features at the time of generation. The generation unit generates remarks and presentation materials preferentially based on, for example, recently analyzed features. The generation unit can also generate remarks and presentation materials preferentially based on features frequently referred to by the user. The generation unit can also generate remarks and presentation materials at a 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. A part or all of the above-mentioned 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 input time data of the analyzed features to the generation AI and cause the generation AI to execute the determination of the priority of generation.

[0087] The generation unit can adjust the order of generation based on the relevance of the analyzed features at the time of generation. For example, the generation unit preferentially generates features in which the user is interested. The generation unit can also preferentially generate features related to the user's work. The generation unit can also preferentially generate features related to the user's project. This makes it possible to adjust the order of generation based on the relevance of the analyzed features. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using 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 to the generation AI and cause the generation AI to adjust the order of generation.

[0088] The providing unit can estimate the user's emotion and adjust the display method of the remarks and presentation materials to be provided based on the estimated user's emotion. For example, when the user is nervous, the providing unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the providing unit can also provide a display method including detailed information. Furthermore, when 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 emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to the generation AI and cause the generation AI to adjust the display method.

[0089] The providing unit can select the optimal display method by referring to the user's past usage history at the time of providing. For example, the providing unit preferentially provides the display method used by the user 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 according to a specific situation. This makes it possible to select the optimal display method based on the user's past usage history. A part or all of the above-mentioned 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 past usage history data to the generating AI and cause the generating AI to select the optimal display method.

[0090] The providing unit can estimate the user's emotion and adjust the operation procedure of the remarks and presentation materials to be provided based on the estimated user's emotion. For example, when the user is nervous, the providing unit can provide a simple and intuitive operation procedure. Furthermore, when the user is relaxed, the providing unit can also provide a detailed operation procedure. Furthermore, when the user is in a hurry, the providing unit can also provide a procedure that allows the user to operate quickly. This allows the operation procedure to be adjusted according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to the generation AI and cause the generation AI to adjust the operation procedure.

[0091] The providing unit can select the optimal display method in consideration of the user's device information at the time of providing. For example, when the user is using a smartphone, the providing unit provides a display method that matches the screen size. In addition, when the user is using a tablet, the providing unit can also provide a display method optimized for a large screen. In addition, when 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. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's device information data to the generating AI and cause the generating 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, the reception unit preferentially suggests 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 in a specific time period from the user's past input history. This makes it possible to select the optimal input method based on the user's past input history. A part or all of the above-mentioned processing in the reception unit may be performed, for example, using AI or may be performed without using AI. For example, the reception unit can input the user's past input history data to the generation AI and cause the generation AI to select the optimal input method.

[0094] The analysis unit can adjust the level of detail of the analysis based on the importance of the person during the analysis. For example, in the case of an influential person, a detailed analysis is performed. In addition, the analysis unit can also perform a concise analysis in the case of a general person. Furthermore, in the case of a person famous in a specific field, the analysis unit can also perform an analysis specialized in that field. This makes it possible to adjust the level of detail of the analysis based on the importance of the person. A part 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 to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0095] The generation unit can adjust the level of detail of the generation based on the importance of the analyzed features at the time of generation. For example, detailed remarks and presentation materials are generated based on important features. The generation unit can also generate concise remarks and presentation materials based on general features. Furthermore, the generation unit can generate remarks and presentation materials specialized in a specific field based on features important in that field. This makes it possible to adjust the level of detail of the generation based on the importance of the analyzed features. A part or all of the above-mentioned 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 importance data of the analyzed features to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0096] The providing unit can select the optimal display method by referring to the user's past usage history at the time of providing. For example, the display method used by the user in the past is preferentially provided. 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 according to a specific situation. This makes it possible to select the optimal display method based on the user's past usage history. A part or all of the above-mentioned 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 past usage history data to the generating AI and cause the generating AI to select the optimal display method.

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

[0098] The reception unit can estimate the user's emotion and adjust the timing of information input of the person to be imitated based on the estimated user's emotion. For example, if the user is nervous, the reception unit adjusts to wait for input until the user is relaxed. The reception unit can also adjust to prompt the user to input immediately if the user is excited. Furthermore, if the user is tired, the reception unit can also adjust to suggest a break and resume input later. This makes it possible to adjust the timing of input according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's facial expression data to the generation AI and cause the generation AI to execute the estimation of the user's emotion.

[0099] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit provides a detailed analysis result. Furthermore, when the user is nervous, the analysis unit can also provide a concise and to the point analysis result. Furthermore, when the user is excited, the analysis unit can also provide an analysis result with a visually stimulating effect. This makes it possible to adjust the expression method of the analysis according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's facial expression data to the generation AI and cause the generation AI to adjust the expression method of the analysis.

[0100] The generation unit can estimate the user's emotion and adjust the expression method of the generated remarks and presentation materials based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates remarks and presentation materials including detailed explanations. In addition, when the user is nervous, the generation unit can also generate remarks and presentation materials that are concise and to the point. Furthermore, when the user is excited, the generation unit can also generate remarks and presentation materials with visually stimulating effects. This makes it possible to adjust the expression method of the remarks and presentation materials according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's facial expression data to the generation AI and cause the generation AI to adjust the expression method of the remarks and presentation materials.

[0101] The providing unit can estimate the user's emotion and adjust the display method of the remarks and presentation materials to be provided based on the estimated user's emotion. For example, when the user is nervous, the providing unit can provide a display method that is simple and highly visible. Furthermore, when the user is relaxed, the providing unit can also provide a display method that includes detailed information. Furthermore, when 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 emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to the generation AI and cause the generation AI to adjust the display method.

[0102] The providing unit can estimate the user's emotion and adjust the operation procedure of the remarks and presentation materials to be provided based on the estimated user's emotion. For example, when the user is nervous, the providing unit can provide a simple and intuitive operation procedure. Furthermore, when the user is relaxed, the providing unit can also provide a detailed operation procedure. Furthermore, when the user is in a hurry, the providing unit can also provide a procedure that allows the user to operate quickly. This makes it possible to adjust the operation procedure according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to the generation AI and cause the generation AI to adjust the operation procedure.

[0103] The process flow of the second embodiment will be briefly described below.

[0104] Step 1: The reception unit inputs information about the person to be imitated. The information about the person to be imitated includes the way of speaking, presentation style, words used, etc. The reception unit provides an interface for the user to input information about the influential person. Step 2: The analysis unit uses the 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 generation AI to generate speech and presentation materials based on the features extracted by the analyzer. The generator generates a speech manuscript 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 and allows the user to 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

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

[0108] Each of the multiple elements including the above-mentioned 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 on a person he or she wants 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 the 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 remarks 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 remarks and presentation materials to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[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 the voice according to instructions from the processor 46.

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

[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 performed in a secure state.

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

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

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

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

[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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

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

[0124] Each of the multiple elements including the above-mentioned 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 on a person he or she wants 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 the 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 remarks 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 remarks and presentation materials to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes 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 type 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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[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 the voice according to instructions from the processor 46.

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

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

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

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

[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 voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] Each of the multiple elements including the above-mentioned 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 on a person he or she wants 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 the 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 remarks 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 remarks and presentation materials to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[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, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[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 the voice according to instructions from the processor 46.

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

[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 performed in a secure state.

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

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

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

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

[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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] Each of the multiple elements including the above-mentioned 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 on a person he or she wants 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 the 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 remarks 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 remarks and presentation materials to the user. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0159] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

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

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

[0162] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

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

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

[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 Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

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

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

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

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

[0172] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

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

[0174] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

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

[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 a statement or a presentation material 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 is Analyze the speaking and presentation styles of influential people and the words they use 2. The system of claim 1, (Appendix 3) The generation unit is Generate a speech transcript based on the analyzed features 2. The system of claim 1, (Appendix 4) The generation unit is Generate presentation slides based on the analyzed features 2. The system of claim 1, (Appendix 5) The providing unit is Providing the generated speech or presentation slides to the user 2. The system of claim 1, (Appendix 6) The reception unit is Using a method for estimating a user's emotions, the timing of inputting information of a person to be imitated is adjusted based on the estimated user's emotions. 2. The system of claim 1, (Appendix 7) The reception unit is Analyze the user's past input history and select the optimal input method 2. The system of claim 1, (Appendix 8) The reception unit is When entering information about the person you want to imitate, it will be filtered based on the user's current areas of interest. 2. The system of claim 1, (Appendix 9) The reception unit is Using a method for estimating user emotions, the priority of information to be input is determined based on the estimated user emotions. 2. The system of claim 1, (Appendix 10) The reception unit is 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 more relevant. 2. The system of claim 1, (Appendix 11) The reception unit is When you enter 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 is 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 is During analysis, adjust the analysis detail based on the importance of the person 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, different analysis algorithms are applied depending on the person category. 2. The system of claim 1, (Appendix 15) The analysis unit is Using a method for estimating user emotion, the length of the analysis is adjusted based on the estimated user emotion. 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, prioritize analysis based on when a person's information was entered 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, adjust the order of analysis based on person relevance 2. The system of claim 1, (Appendix 18) The generation unit is 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 is 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 is 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 is Using a method for estimating 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 is 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 is At generation time, adjust the generation order based on the analyzed feature relevance 2. The system of claim 1, (Appendix 24) The providing unit is Using a method for estimating user emotions, the system adjusts the way statements and presentation materials are provided based on the estimated user emotions. 2. The system of claim 1, (Appendix 25) The providing unit is 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 is Using a method for estimating a user's emotions, the system adjusts the remarks provided and the operation procedures for presentation materials based on the estimated user's emotions. 2. The system of claim 1, (Appendix 27) The providing unit is 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 a statement or a presentation material 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:

2. The analysis unit is Analyze the speaking and presentation styles of influential people and the words they use 2. The system of claim 1 .

3. The generation unit is Generate 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 is Providing the generated speech or presentation slides to the user 4. The system of claim 3.

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

2. The system of claim 1 .

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

7. The reception unit is When entering information about the person you want to imitate, it will be filtered based on the user's current areas of interest.

2. The system of claim 1 .

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