System

Abstract

An object of a system according to an embodiment is to create a presentation material on the basis of information provided by a user and give advice on how to convey a presentation for each point.SOLUTION: A system includes a generation unit and an advice unit. The generation unit creates a presentation material on the basis of information provided from a user. The advice unit gives advice on how to convey the presentation for each point based on the presentation material created by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately support both the creation of presentation materials and the delivery of presentations efficiently, and there is room for improvement.

[0005] The system according to the embodiment aims to create presentation materials based on information provided by the user and provide advice on each key point of how to communicate the presentation. [Means for solving the problem]

[0006] The system according to the embodiment includes a generating unit and an advising unit. The generating unit generates presentation materials based on information provided by a user. The advising unit provides advice on each key point of how to deliver a presentation based on the presentation materials generated by the generating unit. [Effects of the Invention]

[0007] The system according to the embodiment can create presentation materials based on information provided by the user and provide advice on each key point of how to deliver the presentation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The presentation support system according to an embodiment of the present invention uses a generation AI to create presentation materials based on information provided by the user, and provides advice on each key point of how to deliver the presentation. This allows the user to obtain high-quality presentation materials and receive accurate advice on how to deliver the presentation.

[0029] A presentation support system according to an embodiment includes a generation unit and an advice unit. The generation unit creates presentation materials based on information provided by a user. For example, the generation AI analyzes data and key points provided by the user and automatically generates visually appealing and easy-to-understand slides. The generation AI also learns presentation materials from specific experts who are professional presenters and utilizes their know-how. For example, the generation AI receives prompts containing instructions on what the user wants the generation AI to do and generates presentation materials. The advice unit provides advice on each key point of how to deliver a presentation based on the presentation materials created by the generation unit. For example, it provides specific advice on tone of voice, speaking speed, and eye contact during the presentation. The advice unit also learns the presentation methods of specific experts who are professional presenters and utilizes their know-how. For example, the generation AI receives prompts containing instructions on what the user wants the generation AI to do and generates advice. This allows the presentation support system to create presentation materials based on information provided by the user and provide advice on how to deliver the presentation.

[0030] The generation unit can analyze the user's past presentation materials and generate presentation materials optimized for the user's presentation style. For example, the generation unit uses a generation AI to analyze the user's past presentation materials and automatically generate templates that match the user's presentation style. For example, it learns the user's preferred color usage, fonts, and layout, and creates new presentation materials based on that. This makes it possible to analyze the user's past presentation materials and generate materials optimized for the user's style.

[0031] The generation unit can receive real-time feedback from the user and dynamically modify the presentation materials. For example, the generation AI receives real-time feedback from the user and instantly modifies the content and design of the presentation materials. For example, if the user wants to change the order of slides, the generation AI automatically suggests the optimal order. This allows feedback to be received in real time and presentation materials to be dynamically modified.

[0032] The generation unit can automatically generate presentation materials that correspond to different cultures and languages, making it possible to support international presentations. For example, the generation unit uses a generation AI to automatically generate presentation materials that correspond to different cultures and languages. For example, slides that correspond to multiple languages, such as English, Japanese, and Chinese, can be automatically generated to support international presentations. This allows the automatic generation of presentation materials that correspond to different cultures and languages, making it possible to support international presentations.

[0033] The generation unit learns the terminology and trends specific to the user's industry and can provide presentation materials specialized for that industry. For example, the generation AI learns the terminology and trends specific to the user's industry and automatically generates presentation materials based on that. For example, a user in the medical industry is provided with slides that reflect technical terminology and the latest research results. This allows the system to learn the terminology and trends specific to the user's industry and provide presentation materials specialized for that industry.

[0034] The generation unit can analyze the user's voice input and automatically generate presentation materials from the voice input. For example, the generation unit uses a generation AI to analyze the user's voice input and automatically generate presentation materials based on the content. For example, it converts what the user says into text and reflects it in slides. This allows the user's voice input to be analyzed and presentation materials to be automatically generated from the voice.

[0035] The generation unit can analyze the user's handwritten notes, digitize the contents of the handwritten notes, and reflect them in the presentation materials. For example, the generation unit uses a generation AI to analyze the user's handwritten notes, digitize the contents, and reflect them in the presentation materials. For example, handwritten diagrams and graphs are automatically converted into slides. This allows the user's handwritten notes to be analyzed, and the handwritten contents to be digitized and reflected in the presentation materials.

[0036] The generation unit can analyze the user's social media accounts and reflect related topics and trends in the presentation materials. For example, the generation AI analyzes the user's social media accounts and reflects related topics and trends in the presentation materials. For example, it incorporates topics that the user is interested in into slides. This allows the user's social media accounts to be analyzed and related topics and trends to be reflected in the presentation materials.

[0037] The generation unit can analyze the user's past emails and documents and integrate related information into presentation materials. For example, the generation AI analyzes the user's past emails and documents and integrates related information into presentation materials. For example, past project reports and meeting notes are reflected in slides. This allows the user's past emails and documents to be analyzed and related information to be integrated into presentation materials.

[0038] The advice unit can analyze the user's presentation video and provide feedback on specific areas for improvement. For example, the generative AI can analyze the user's presentation video and provide feedback on specific areas for improvement. For example, it can evaluate the user's speaking speed, tone of voice, use of eye contact, etc., and suggest areas for improvement. This allows the advice unit to analyze the user's presentation video and provide feedback on specific areas for improvement.

[0039] The advice unit can monitor the user's presentation practice in real time and provide immediate advice. For example, the generative AI can monitor the user's presentation practice in real time and provide immediate advice. For example, it can analyze the user's speaking speed and tone of voice in real time and provide appropriate advice. This makes it possible to monitor the user's presentation practice in real time and provide immediate advice.

[0040] The advice unit can provide presentation advice that corresponds to different cultures and languages, making it possible to handle international presentations. For example, the generative AI provides presentation advice that corresponds to different cultures and languages. For example, it can give advice on how to give a presentation in multiple languages, such as English, Japanese, and Chinese. This allows the provision of presentation advice that corresponds to different cultures and languages, making it possible to handle international presentations.

[0041] The advice unit can learn the presentation style specific to the user's industry and provide advice specific to that industry. For example, the generative AI can learn the presentation style specific to the user's industry and provide presentation advice based on that. For example, a user in the medical industry can receive advice that reflects technical terminology and the latest research results. This allows the system to learn the presentation style specific to the user's industry and provide advice specific to that industry.

[0042] The advice unit can analyze the content of a user's presentation and suggest specific phrases and expressions. For example, the generation AI can analyze the content of a user's presentation and suggest specific phrases and expressions. For example, it can suggest effective expressions based on keywords and phrases used by the user. This makes it possible to analyze the content of a user's presentation and suggest specific phrases and expressions.

[0043] The advice unit can analyze the timing and spacing of a user's presentation and suggest the optimal timing. For example, the generative AI can analyze the timing and spacing of a user's presentation and suggest the optimal timing. For example, it can evaluate the user's speaking speed and spacing and suggest areas for improvement. This allows the timing and spacing of a user's presentation to be analyzed and the optimal timing to be suggested.

[0044] The advice unit can provide presentation advice that corresponds to different cultures and languages, making it possible to handle international presentations. For example, the generative AI provides presentation advice that corresponds to different cultures and languages. For example, it can give advice on how to give a presentation in multiple languages, such as English, Japanese, and Chinese. This allows the provision of presentation advice that corresponds to different cultures and languages, making it possible to handle international presentations.

[0045] The advice unit can learn the presentation style specific to the user's industry and provide advice specific to that industry. For example, the generative AI can learn the presentation style specific to the user's industry and provide presentation advice based on that. For example, a user in the medical industry can receive advice that reflects technical terminology and the latest research results. This allows the system to learn the presentation style specific to the user's industry and provide advice specific to that industry.

[0046] The generation unit can analyze the content of a user's presentation and suggest specific phrases and expressions. For example, the generation AI analyzes the content of a user's presentation and suggests specific phrases and expressions. For example, it suggests effective expressions based on keywords and phrases used by the user. This makes it possible to analyze the content of a user's presentation and suggest specific phrases and expressions.

[0047] The generation unit can analyze the timing and spacing of a user's presentation and suggest the optimal timing. For example, the generation AI can analyze the timing and spacing of a user's presentation and suggest the optimal timing. For example, it can evaluate the user's speaking speed and spacing and suggest areas for improvement. This allows the generation unit to analyze the timing and spacing of a user's presentation and suggest the optimal timing.

[0048] The generation unit can provide presentation advice that corresponds to different cultures and languages, making it possible to handle international presentations. For example, the generation unit provides presentation advice that corresponds to different cultures and languages. For example, it provides advice on how to give a presentation in multiple languages, such as English, Japanese, and Chinese. This allows the provision of presentation advice that corresponds to different cultures and languages, making it possible to handle international presentations.

[0049] The generation unit can learn the presentation style specific to the user's industry and provide advice specific to that industry. For example, the generation AI can learn the presentation style specific to the user's industry and provide presentation advice based on that. For example, a user in the medical industry can receive advice that reflects technical terminology and the latest research results. This allows the generation unit to learn the presentation style specific to the user's industry and provide advice specific to that industry.

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

[0051] The generation unit can analyze a user's past presentation materials and generate presentation materials optimized for the user's presentation style. For example, the generation AI can analyze a user's past presentation materials and automatically generate templates that match the user's presentation style. For example, it can learn the user's preferred colors, fonts, and layouts and create new presentation materials based on them. This allows the system to analyze a user's past presentation materials and generate materials optimized for the user's style.

[0052] The generation unit can receive real-time feedback from the user and dynamically modify the presentation materials. For example, the generation AI receives real-time feedback from the user and instantly modifies the content and design of the presentation materials. For example, if the user wants to change the order of the slides, the generation AI automatically suggests the optimal order. This allows feedback to be received in real time and presentation materials to be dynamically modified.

[0053] The generation unit can automatically generate presentation materials that correspond to different cultures and languages, making it possible to support international presentations. For example, the generation AI can automatically generate presentation materials that correspond to different cultures and languages. For example, it can automatically generate slides that correspond to multiple languages, such as English, Japanese, and Chinese, making it possible to support international presentations. This allows the automatic generation of presentation materials that correspond to different cultures and languages, making it possible to support international presentations.

[0054] The generation unit learns the terminology and trends specific to the user's industry and can provide presentation materials tailored to that industry. For example, the generation AI learns the terminology and trends specific to the user's industry and automatically generates presentation materials based on that. For example, a user in the medical industry can be provided with slides that reflect technical terminology and the latest research results. This allows the system to learn the terminology and trends specific to the user's industry and provide presentation materials tailored to that industry.

[0055] The generation unit can analyze the user's voice input and automatically generate presentation materials from the voice input. For example, the generation AI can analyze the user's voice input and automatically generate presentation materials based on that content. For example, it can convert what the user says into text and reflect that in slides. This allows the user's voice input to be analyzed and presentation materials to be automatically generated from the voice.

[0056] The generation unit can analyze the user's handwritten notes, digitize the contents of the handwritten notes, and reflect them in presentation materials. For example, the generation AI can analyze the user's handwritten notes, digitize the contents, and reflect them in presentation materials. For example, handwritten diagrams and graphs can be automatically converted into slides. This allows the user's handwritten notes to be analyzed, and the handwritten contents to be digitized and reflected in presentation materials.

[0057] The generation unit can analyze the user's social media accounts and reflect related topics and trends in the presentation materials. For example, the generation AI can analyze the user's social media accounts and reflect related topics and trends in the presentation materials. For example, it can incorporate topics that the user is interested in into the slides. This allows the generation AI to analyze the user's social media accounts and reflect related topics and trends in the presentation materials.

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

[0059] Step 1: The generator creates presentation materials based on information provided by the user. For example, the generator AI analyzes the data and key points provided by the user and automatically generates visually appealing and easy-to-understand slides. The generator AI also learns presentation materials from specific experts who are professional presenters and utilizes their know-how. For example, the generator AI receives prompts containing instructions on what the user wants the generator AI to do, and then generates presentation materials. Step 2: The advice section provides advice on each key point of how to deliver a presentation based on the presentation materials created by the generation section. For example, it provides specific advice on the tone of voice, speaking speed, and eye contact during the presentation. The advice section also learns the presentation methods of specific presentation professionals and utilizes that know-how. For example, the generation AI receives prompts containing instructions on what the user wants the generation AI to do, and then generates advice.

[0060] (Example 2) The presentation support system according to an embodiment of the present invention uses a generation AI to create presentation materials based on information provided by the user, and provides advice on each key point of how to deliver the presentation. This allows the user to obtain high-quality presentation materials and receive accurate advice on how to deliver the presentation.

[0061] A presentation support system according to an embodiment includes a generation unit and an advice unit. The generation unit creates presentation materials based on information provided by a user. For example, the generation AI analyzes data and key points provided by the user and automatically generates visually appealing and easy-to-understand slides. The generation AI also learns presentation materials from specific experts who are professional presenters and utilizes their know-how. For example, the generation AI receives prompts containing instructions on what the user wants the generation AI to do and generates presentation materials. The advice unit provides advice on each key point of how to deliver a presentation based on the presentation materials created by the generation unit. For example, it provides specific advice on tone of voice, speaking speed, and eye contact during the presentation. The advice unit also learns the presentation methods of specific experts who are professional presenters and utilizes their know-how. For example, the generation AI receives prompts containing instructions on what the user wants the generation AI to do and generates advice. This allows the presentation support system to create presentation materials based on information provided by the user and provide advice on how to deliver the presentation.

[0062] The generation unit can analyze the user's past presentation materials and generate presentation materials optimized for the user's presentation style. For example, the generation unit uses a generation AI to analyze the user's past presentation materials and automatically generate templates that match the user's presentation style. For example, it learns the user's preferred color usage, fonts, and layout, and creates new presentation materials based on that. This makes it possible to analyze the user's past presentation materials and generate materials optimized for the user's style.

[0063] The generation unit can receive real-time feedback from the user and dynamically modify the presentation materials. For example, the generation AI receives real-time feedback from the user and instantly modifies the content and design of the presentation materials. For example, if the user wants to change the order of slides, the generation AI automatically suggests the optimal order. This allows feedback to be received in real time and presentation materials to be dynamically modified.

[0064] The generation unit can use the emotion estimation function to analyze the user's emotional state and propose designs and content that elicit positive emotions. For example, the generation unit can use the emotion estimation function to analyze the user's emotional state when creating presentation materials and propose designs and content that elicit positive emotions. For example, if the user is feeling stressed, the generation unit can propose colors and layouts that have a relaxing effect. This makes it possible to analyze the user's emotional state and propose designs and content that elicit positive emotions.

[0065] The generation unit can automatically generate presentation materials that correspond to different cultures and languages, making it possible to support international presentations. For example, the generation unit uses a generation AI to automatically generate presentation materials that correspond to different cultures and languages. For example, slides that correspond to multiple languages, such as English, Japanese, and Chinese, can be automatically generated to support international presentations. This allows the automatic generation of presentation materials that correspond to different cultures and languages, making it possible to support international presentations.

[0066] The generation unit learns the terminology and trends specific to the user's industry and can provide presentation materials specialized for that industry. For example, the generation AI learns the terminology and trends specific to the user's industry and automatically generates presentation materials based on that. For example, a user in the medical industry is provided with slides that reflect technical terminology and the latest research results. This allows the system to learn the terminology and trends specific to the user's industry and provide presentation materials specialized for that industry.

[0067] The generation unit can use the emotion estimation function to customize the visual elements of the presentation materials to match the user's emotions. For example, the generation unit uses the emotion estimation function to customize the visual elements of the presentation materials based on the user's emotional state. For example, if the user is relaxed, the generation unit can suggest calm colors and a simple layout. This allows the visual elements of the presentation materials to be customized to match the user's emotions.

[0068] The generation unit can analyze the user's voice input and automatically generate presentation materials from the voice input. For example, the generation unit uses a generation AI to analyze the user's voice input and automatically generate presentation materials based on the content. For example, it converts what the user says into text and reflects it in slides. This allows the user's voice input to be analyzed and presentation materials to be automatically generated from the voice.

[0069] The generation unit can analyze the user's handwritten notes, digitize the contents of the handwritten notes, and reflect them in the presentation materials. For example, the generation unit uses a generation AI to analyze the user's handwritten notes, digitize the contents, and reflect them in the presentation materials. For example, handwritten diagrams and graphs are automatically converted into slides. This allows the user's handwritten notes to be analyzed, and the handwritten contents to be digitized and reflected in the presentation materials.

[0070] The generation unit can use the emotion estimation function to analyze the user's emotional state and add keywords and phrases based on the emotional state to the presentation materials. For example, the generation unit can use the emotion estimation function to analyze the user's emotional state and add keywords and phrases based on the emotion to the presentation materials. For example, if the user has positive emotions, encouraging words and positive phrases can be added. This allows the user's emotional state to be analyzed and keywords and phrases based on the emotion to be added to the presentation materials.

[0071] The generation unit can analyze the user's social media accounts and reflect related topics and trends in the presentation materials. For example, the generation AI analyzes the user's social media accounts and reflects related topics and trends in the presentation materials. For example, it incorporates topics that the user is interested in into slides. This allows the user's social media accounts to be analyzed and related topics and trends to be reflected in the presentation materials.

[0072] The generation unit can analyze the user's past emails and documents and integrate related information into presentation materials. For example, the generation AI analyzes the user's past emails and documents and integrates related information into presentation materials. For example, past project reports and meeting notes are reflected in slides. This allows the user's past emails and documents to be analyzed and related information to be integrated into presentation materials.

[0073] The generation unit can incorporate storytelling elements based on the user's emotions into the presentation materials using the emotion estimation function. The generation unit, for example, uses the emotion estimation function to incorporate storytelling elements based on the user's emotions into the presentation materials. For example, the generation unit adds moving episodes or successful experiences of the user to slides. This allows storytelling elements based on the user's emotions to be incorporated into the presentation materials.

[0074] The advice unit can analyze the user's presentation video and provide feedback on specific areas for improvement. For example, the generative AI can analyze the user's presentation video and provide feedback on specific areas for improvement. For example, it can evaluate the user's speaking speed, tone of voice, use of eye contact, etc., and suggest areas for improvement. This allows the advice unit to analyze the user's presentation video and provide feedback on specific areas for improvement.

[0075] The advice unit can monitor the user's presentation practice in real time and provide immediate advice. For example, the generative AI can monitor the user's presentation practice in real time and provide immediate advice. For example, it can analyze the user's speaking speed and tone of voice in real time and provide appropriate advice. This makes it possible to monitor the user's presentation practice in real time and provide immediate advice.

[0076] The advice unit can use the emotion estimation function to analyze the emotional state of the user and provide emotion-based advice for the presentation. For example, the advice unit can use the emotion estimation function to analyze the emotional state of the user and provide emotion-based advice for the presentation. For example, if the user is nervous, the advice unit can provide advice to relax. This allows the user's emotional state to be analyzed and emotion-based advice for the presentation to be provided.

[0077] The advice unit can provide presentation advice that corresponds to different cultures and languages, making it possible to handle international presentations. For example, the generative AI provides presentation advice that corresponds to different cultures and languages. For example, it can give advice on how to give a presentation in multiple languages, such as English, Japanese, and Chinese. This allows the provision of presentation advice that corresponds to different cultures and languages, making it possible to handle international presentations.

[0078] The advice unit can learn the presentation style specific to the user's industry and provide advice specific to that industry. For example, the generative AI can learn the presentation style specific to the user's industry and provide presentation advice based on that. For example, a user in the medical industry can receive advice that reflects technical terminology and the latest research results. This allows the system to learn the presentation style specific to the user's industry and provide advice specific to that industry.

[0079] The advice unit can use the emotion estimation function to simulate a presentation based on the user's emotions and suggest an optimal presentation method. The advice unit, for example, uses the emotion estimation function to simulate a presentation based on the user's emotions and suggest an optimal presentation method. For example, if the user is nervous, a simulation is performed to help the user relax. This allows the advice unit to simulate a presentation based on the user's emotions and suggest an optimal presentation method.

[0080] The advice unit can analyze the content of a user's presentation and suggest specific phrases and expressions. For example, the generation AI can analyze the content of a user's presentation and suggest specific phrases and expressions. For example, it can suggest effective expressions based on keywords and phrases used by the user. This makes it possible to analyze the content of a user's presentation and suggest specific phrases and expressions.

[0081] The advice unit can analyze the timing and spacing of a user's presentation and suggest the optimal timing. For example, the generative AI can analyze the timing and spacing of a user's presentation and suggest the optimal timing. For example, it can evaluate the user's speaking speed and spacing and suggest areas for improvement. This allows the timing and spacing of a user's presentation to be analyzed and the optimal timing to be suggested.

[0082] The advice unit can use the emotion estimation function to analyze the emotional state of the user and provide emotion-based advice for the presentation. For example, the advice unit can use the emotion estimation function to analyze the emotional state of the user and provide emotion-based advice for the presentation. For example, if the user is nervous, the advice unit can provide advice to relax. This allows the user's emotional state to be analyzed and emotion-based advice for the presentation to be provided.

[0083] The advice unit can provide presentation advice that corresponds to different cultures and languages, making it possible to handle international presentations. For example, the generative AI provides presentation advice that corresponds to different cultures and languages. For example, it can give advice on how to give a presentation in multiple languages, such as English, Japanese, and Chinese. This allows the provision of presentation advice that corresponds to different cultures and languages, making it possible to handle international presentations.

[0084] The advice unit can learn the presentation style specific to the user's industry and provide advice specific to that industry. For example, the generative AI can learn the presentation style specific to the user's industry and provide presentation advice based on that. For example, a user in the medical industry can receive advice that reflects technical terminology and the latest research results. This allows the system to learn the presentation style specific to the user's industry and provide advice specific to that industry.

[0085] The advice unit can use the emotion estimation function to simulate a presentation based on the user's emotions and suggest an optimal presentation method. The advice unit, for example, uses the emotion estimation function to simulate a presentation based on the user's emotions and suggest an optimal presentation method. For example, if the user is nervous, a simulation is performed to help the user relax. This allows the advice unit to simulate a presentation based on the user's emotions and suggest an optimal presentation method.

[0086] The generation unit can analyze the content of a user's presentation and suggest specific phrases and expressions. For example, the generation AI analyzes the content of a user's presentation and suggests specific phrases and expressions. For example, it suggests effective expressions based on keywords and phrases used by the user. This makes it possible to analyze the content of a user's presentation and suggest specific phrases and expressions.

[0087] The generation unit can analyze the timing and spacing of a user's presentation and suggest the optimal timing. For example, the generation AI can analyze the timing and spacing of a user's presentation and suggest the optimal timing. For example, it can evaluate the user's speaking speed and spacing and suggest areas for improvement. This allows the generation unit to analyze the timing and spacing of a user's presentation and suggest the optimal timing.

[0088] The generation unit can use the emotion estimation function to analyze the emotional state of the user and provide emotion-based advice for the presentation. For example, the generation unit can use the emotion estimation function to analyze the emotional state of the user and provide emotion-based advice for the presentation. For example, if the user is nervous, advice to relax is provided. In this way, the user's emotional state can be analyzed and emotion-based advice for the presentation can be provided.

[0089] The generation unit can provide presentation advice that corresponds to different cultures and languages, making it possible to handle international presentations. For example, the generation unit provides presentation advice that corresponds to different cultures and languages. For example, it provides advice on how to give a presentation in multiple languages, such as English, Japanese, and Chinese. This allows the provision of presentation advice that corresponds to different cultures and languages, making it possible to handle international presentations.

[0090] The generation unit can learn the presentation style specific to the user's industry and provide advice specific to that industry. For example, the generation AI can learn the presentation style specific to the user's industry and provide presentation advice based on that. For example, a user in the medical industry can receive advice that reflects technical terminology and the latest research results. This allows the generation unit to learn the presentation style specific to the user's industry and provide advice specific to that industry.

[0091] The generation unit can use the emotion estimation function to simulate a presentation based on the user's emotions and propose an optimal presentation method. For example, the generation unit can use the emotion estimation function to simulate a presentation based on the user's emotions and propose an optimal presentation method. For example, if the user is nervous, a simulation is performed to help the user relax. This allows the generation unit to simulate a presentation based on the user's emotions and propose an optimal presentation 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 generation unit can analyze a user's past presentation materials and generate presentation materials optimized for the user's presentation style. For example, the generation AI can analyze a user's past presentation materials and automatically generate templates that match the user's presentation style. For example, it can learn the user's preferred colors, fonts, and layouts and create new presentation materials based on them. This allows the system to analyze a user's past presentation materials and generate materials optimized for the user's style.

[0094] The generation unit can receive real-time feedback from the user and dynamically modify the presentation materials. For example, the generation AI receives real-time feedback from the user and instantly modifies the content and design of the presentation materials. For example, if the user wants to change the order of the slides, the generation AI automatically suggests the optimal order. This allows feedback to be received in real time and presentation materials to be dynamically modified.

[0095] The generation unit can use the emotion estimation function to analyze the user's emotional state and propose designs and content that elicit positive emotions. For example, the emotion estimation function can be used to analyze the user's emotional state when creating presentation materials and propose designs and content that elicit positive emotions. For example, if the user is feeling stressed, the generation unit can propose colors and layouts that have a relaxing effect. This allows the generation unit to analyze the user's emotional state and propose designs and content that elicit positive emotions.

[0096] The generation unit can automatically generate presentation materials that correspond to different cultures and languages, making it possible to support international presentations. For example, the generation AI can automatically generate presentation materials that correspond to different cultures and languages. For example, it can automatically generate slides that correspond to multiple languages, such as English, Japanese, and Chinese, making it possible to support international presentations. This allows the automatic generation of presentation materials that correspond to different cultures and languages, making it possible to support international presentations.

[0097] The generation unit learns the terminology and trends specific to the user's industry and can provide presentation materials tailored to that industry. For example, the generation AI learns the terminology and trends specific to the user's industry and automatically generates presentation materials based on that. For example, a user in the medical industry can be provided with slides that reflect technical terminology and the latest research results. This allows the system to learn the terminology and trends specific to the user's industry and provide presentation materials tailored to that industry.

[0098] The generation unit can use the emotion estimation function to customize the visual elements of the presentation materials to match the user's emotions. For example, the emotion estimation function can be used to customize the visual elements of the presentation materials based on the user's emotional state. For example, if the user is relaxed, the generation unit can suggest calm colors and a simple layout. This allows the generation unit to customize the visual elements of the presentation materials to match the user's emotions.

[0099] The generation unit can analyze the user's voice input and automatically generate presentation materials from the voice input. For example, the generation AI can analyze the user's voice input and automatically generate presentation materials based on that content. For example, it can convert what the user says into text and reflect that in slides. This allows the user's voice input to be analyzed and presentation materials to be automatically generated from the voice.

[0100] The generation unit can analyze the user's handwritten notes, digitize the contents of the handwritten notes, and reflect them in presentation materials. For example, the generation AI can analyze the user's handwritten notes, digitize the contents, and reflect them in presentation materials. For example, handwritten diagrams and graphs can be automatically converted into slides. This allows the user's handwritten notes to be analyzed, and the handwritten contents to be digitized and reflected in presentation materials.

[0101] The generation unit can use the emotion estimation function to analyze the user's emotional state and add keywords and phrases based on the emotional state to the presentation materials. For example, the emotion estimation function can be used to analyze the user's emotional state and add keywords and phrases based on the emotion to the presentation materials. For example, if the user has positive emotions, encouraging words and positive phrases can be added. In this way, the user's emotional state can be analyzed and keywords and phrases based on the emotion can be added to the presentation materials.

[0102] The generation unit can analyze the user's social media accounts and reflect related topics and trends in the presentation materials. For example, the generation AI can analyze the user's social media accounts and reflect related topics and trends in the presentation materials. For example, it can incorporate topics that the user is interested in into the slides. This allows the generation AI to analyze the user's social media accounts and reflect related topics and trends in the presentation materials.

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

[0104] Step 1: The generator creates presentation materials based on information provided by the user. For example, the generator AI analyzes the data and key points provided by the user and automatically generates visually appealing and easy-to-understand slides. The generator AI also learns presentation materials from specific experts who are professional presenters and utilizes their know-how. For example, the generator AI receives prompts containing instructions on what the user wants the generator AI to do, and then generates presentation materials. Step 2: The advice section provides advice on each key point of how to deliver a presentation based on the presentation materials created by the generation section. For example, it provides specific advice on the tone of voice, speaking speed, and eye contact during the presentation. The advice section also learns the presentation methods of specific presentation professionals and utilizes that know-how. For example, the generation AI receives prompts containing instructions on what the user wants the generation AI to do, and then generates advice.

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

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

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

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

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0163] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

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

Claims

1. a generation unit that creates presentation materials based on information provided by a user; an advice unit that gives advice on how to convey the presentation for each point based on the presentation materials created by the creation unit; A system characterized by:

2. The generation unit receiving real-time feedback from the user and dynamically modifying the presentation materials; 2. The system of claim 1.

3. The generation unit Automatically generate presentation materials that correspond to different cultures and languages, enabling international presentations.

2. The system of claim 1.

4. The generation unit Analyzing the user's voice input and automatically generating the presentation materials from the voice input.

2. The system of claim 1.

5. The advice unit Analyze the user's presentation video and provide feedback on specific improvements.

2. The system of claim 1.

6. The generation unit Analyze the user's emotional state and suggest designs and content that elicit positive emotions 2. The system of claim 1.

7. The generation unit Analyzing the emotional state of the user and adding keywords and phrases based on the emotional state to the presentation materials.

2. The system of claim 1.

8. The advice unit A simulation of the presentation based on the user's emotions is performed, and the optimal presentation method is proposed.

2. The system of claim 1.

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