System

Abstract

An object of the system according to the embodiment is to enable even a user who is not good at creating and implementing a presentation to make an effective presentation.SOLUTION: A system includes an idea input part, a text generation part, an image generation part, and a voice guidance part. The idea input unit receives an idea of a user. The text generation unit analyzes the idea received by the idea input unit and generates a text sentence. The image generation unit generates an image or a moving image based on the text generated by the text generation unit. The voice guidance unit converts the text sentence generated by the text generation unit into voice, and performs presentation.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 techniques have had the problem that it is difficult for users who are not good at creating and delivering presentations to give effective presentations.

[0005] The system according to the embodiment aims to enable even users who are not good at creating or giving presentations to give effective presentations. [Means for solving the problem]

[0006] The system according to the embodiment includes an idea input unit, a text generation unit, an image generation unit, and an audio guidance unit. The idea input unit receives a user's idea. The text generation unit analyzes the idea received by the idea input unit and generates a text sentence. The image generation unit generates an image or video based on the text sentence generated by the text generation unit. The audio guidance unit converts the text sentence generated by the text generation unit into audio and performs a presentation. [Effects of the Invention]

[0007] The system according to the embodiment allows even users who are not good at creating or giving presentations to give effective presentations. [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 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 presenter system according to an embodiment of the present invention is a system that automatically creates presentation materials and gives presentations with audio guidance simply by a user inputting their ideas. This makes it easy for people who are not good at giving presentations to give presentations.

[0029] A presenter system according to an embodiment includes an idea input unit, a text generation unit, an image generation unit, and an audio guidance unit. The idea input unit accepts a user's idea. For example, if a user inputs that they want to explain the features of a new product, the idea input unit accepts the idea. The text generation unit analyzes the idea accepted by the idea input unit and generates text. For example, the generation AI generates text that describes the product's features in detail based on the idea entered by the user. The image generation unit generates images or videos based on the text generated by the text generation unit. For example, the generation AI generates illustrations to explain the product's features or videos showing how to use the product. The audio guidance unit converts the text generated by the text generation unit into audio and performs a presentation. For example, the generation AI converts the text into audio and provides audio guidance such as, "This product was developed using the latest technology." This allows the presenter system according to an embodiment to easily give presentations to people who are not good at giving presentations.

[0030] The text generator can analyze the user's past presentation data and generate text sentences optimized for the user. For example, the text generator stores presentation materials created by the user in a database and analyzes that data. For example, it analyzes the themes and styles of past presentations and generates text sentences tailored to the user's preferences. This makes it possible to utilize the user's past data to generate more personalized text sentences.

[0031] The idea input unit can analyze the user's voice input in real time and generate text sentences in a natural conversational style. For example, when the user inputs an idea by voice, the idea input unit has the generation AI analyze the voice in real time and generate text sentences in a natural conversational style. For example, it converts what the user says directly into text, maintaining the flow of the conversation. This makes it possible to analyze the user's voice input in real time and generate text sentences in a natural conversational style.

[0032] The idea input unit supports idea input in different languages ​​and can generate text sentences in multiple languages. The idea input unit, for example, allows a user to input ideas in different languages, and the generation AI analyzes the language to generate text sentences. For example, it supports multiple languages ​​such as English, French, and Chinese. This allows idea input in different languages ​​to be supported and text sentences in multiple languages ​​to be generated.

[0033] The text generation unit can automatically search for related literature and data based on ideas entered by the user and incorporate them into the text. For example, when a user enters an idea, the text generation unit uses a generation AI to automatically search for related literature and data and incorporate that information into the text. For example, it can cite the latest research papers and statistical data. This allows related literature and data to be automatically searched for and incorporated into the text based on ideas entered by the user.

[0034] The image generation unit can analyze a user's past presentation materials and generate images and videos optimized for the user. For example, the image generation unit stores presentation materials created by the user in a database and analyzes the data. For example, it analyzes the themes and styles of past presentations and generates images and videos tailored to the user's preferences. This allows the user's past presentation materials to be analyzed and images and videos optimized for each individual user to be generated.

[0035] The image generation unit can analyze the user's voice input and generate images and videos based on the voice content in real time. For example, when a user inputs an idea by voice, the image generation unit uses a generation AI to analyze the voice in real time and generate images and videos based on the voice content. For example, it visually represents what the user is saying. This allows the user's voice input to be analyzed and images and videos based on the voice content to be generated in real time.

[0036] The image generation unit may provide image and video templates based on different styles or themes for the user to select from. For example, the image generation unit may provide templates according to themes such as business, education, entertainment, etc. This may provide image and video templates based on different styles or themes for the user to select from.

[0037] The image generation unit can automatically generate related 3D models and animations based on ideas input by users. For example, when a user inputs an idea, the image generation unit uses a generation AI to automatically generate related 3D models and animations based on the idea. For example, the design of a new product can be displayed as a 3D model. This allows related 3D models and animations to be automatically generated based on ideas input by users.

[0038] The voice guidance unit can analyze the user's past voice data and generate voice guidance optimized for the user. For example, the voice guidance unit stores voice data previously recorded by the user in a database and analyzes the data. For example, the voice guidance unit analyzes the tone and speaking style of past presentations and generates voice guidance tailored to the user's preferences. In this way, the user's past voice data can be analyzed and voice guidance optimized for each individual user can be generated.

[0039] The voice guidance unit can analyze the user's voice input in real time and generate voice guidance in a natural conversational format. For example, when the user inputs an idea by voice, the voice guidance unit uses the generation AI to analyze the voice in real time and generate voice guidance in a natural conversational format. For example, it converts what the user says directly into voice, maintaining the flow of the conversation. This makes it possible to analyze the user's voice input in real time and generate voice guidance in a natural conversational format.

[0040] The audio guidance unit can automatically add related music and sound effects based on the idea entered by the user. For example, when a user enters an idea, the audio guidance unit uses the generation AI to automatically add related music and sound effects based on the idea. For example, background music that matches the theme of the presentation can be selected. This allows related music and sound effects to be automatically added based on the idea entered by the user.

[0041] The customization unit can analyze the user's past customization data and provide the user with optimized customization options. For example, the customization unit stores customization data of presentations that the user has previously made in a database and analyzes the data. For example, it analyzes trends in past customization and provides customization options that match the user's preferences. This makes it possible to analyze the user's past customization data and provide optimized customization options for each individual user.

[0042] The customization unit analyzes the user's voice input in real time and can perform customization in a natural conversational style. For example, when the user inputs customization instructions by voice, the customization unit has the generation AI analyze the voice in real time and perform customization in a natural conversational style. For example, the content of the user's speech is reflected directly in the customization. This allows the user's voice input to be analyzed in real time and customization to be performed in a natural conversational style.

[0043] The customization unit can provide customization templates based on different styles or themes and allow the user to select from them. For example, the customization unit can provide templates based on themes such as business, education, entertainment, etc. This allows the user to select from customization templates based on different styles or themes.

[0044] The customization unit can automatically generate related customization options based on ideas input by the user. For example, when a user inputs an idea, the customization unit uses the generation AI to automatically generate related customization options based on the idea. For example, the customization unit selects a slide layout and colors that match the theme of the presentation. This allows related customization options to be automatically generated based on the idea input by the user.

[0045] The sharing unit can analyze the user's past sharing data and provide optimized sharing options to the user. For example, the sharing unit stores sharing data of presentations that the user has previously made in a database and analyzes the data. For example, the sharing unit analyzes past sharing methods and platforms and provides sharing options that match the user's preferences. This makes it possible to analyze the user's past sharing data and provide optimized sharing options for each individual user.

[0046] The sharing unit analyzes the user's voice input in real time and can share and distribute in a natural conversational format. For example, when the user inputs a command to share or distribute by voice, the sharing unit's generation AI analyzes the voice in real time and shares and distributes in a natural conversational format. For example, the content of the user's speech is reflected directly in the sharing. This allows the user's voice input to be analyzed in real time and shares and distributes in a natural conversational format.

[0047] The sharing section can support sharing and distribution on different platforms, allowing the user to choose. The sharing section, for example, allows the user to share a presentation on different platforms. For example, the sharing section supports multiple platforms such as social media, video conferencing tools, and websites. This allows the user to choose between sharing and distribution on different platforms.

[0048] The sharing unit can automatically generate related sharing options based on ideas input by a user. For example, when a user inputs an idea, the sharing unit's generation AI automatically generates related sharing options based on the idea. For example, it selects a sharing platform and format that matches the theme of the presentation. This makes it possible to automatically generate related sharing options based on ideas input by a user.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The presenter system can also include a gesture recognition unit that recognizes user gestures. For example, when the user waves their hand, the gesture recognition unit analyzes the gesture and recognizes it as an instruction to proceed to the next slide. Also, when the user points, the relevant part can be highlighted. This allows the user to proceed with the presentation using natural hand movements.

[0051] The presenter system may further include an eye-tracking unit that tracks the user's gaze. For example, if the user looks at a particular slide for a long time, the eye-tracking unit generates text that provides a detailed explanation of the content of that slide. Furthermore, if the user moves their gaze, the system can switch slides accordingly. This allows for dynamic control of the presentation based on the user's gaze.

[0052] The presenter system can further include a heart rate measurement unit that measures the user's heart rate. For example, if the user feels nervous, the heart rate measurement unit displays advice on how to relax. If the user's heart rate is stable, the heart rate measurement unit provides support for smoothly progressing the presentation. This makes it possible to support the presentation based on the user's physiological state.

[0053] The presenter system can also analyze feedback from users' past presentations and improve the content of the presentation based on that feedback. For example, it can generate text that reflects points for improvement pointed out in past presentations. It can also generate text that emphasizes parts of past presentations that received high marks. This allows the quality of presentations to be improved by utilizing users' past feedback.

[0054] The presenter system can also analyze the progress of the user's presentation in real time and suggest the next step based on the progress. For example, if the user is spending too much time on a particular slide, it will suggest moving on to the next slide. Also, if the user is about to exceed the presentation time, it will suggest summarizing the main points. This helps to smoothly support the progress of the user's presentation.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The idea input unit accepts the user's idea. For example, if the user inputs that he or she wants to explain the features of a new product, the idea input unit accepts the idea. Step 2: The text generation unit analyzes the ideas received by the idea input unit and generates text. For example, the generation AI generates text that details the features of a product based on the idea entered by the user. Step 3: The image generation unit generates images or videos based on the text generated by the text generation unit. For example, the generation AI generates illustrations to explain the product's features or videos showing how to use the product. Step 4: The voice guidance unit converts the text generated by the text generation unit into voice and gives a presentation. For example, the generation AI converts the text into voice and gives voice guidance in the form of "This product was developed using the latest technology."

[0057] (Example 2) The presenter system according to an embodiment of the present invention is a system that automatically creates presentation materials and gives presentations with audio guidance simply by a user inputting their ideas. This makes it easy for people who are not good at giving presentations to give presentations.

[0058] A presenter system according to an embodiment includes an idea input unit, a text generation unit, an image generation unit, and an audio guidance unit. The idea input unit accepts a user's idea. For example, if a user inputs that they want to explain the features of a new product, the idea input unit accepts the idea. The text generation unit analyzes the idea accepted by the idea input unit and generates text. For example, the generation AI generates text that describes the product's features in detail based on the idea entered by the user. The image generation unit generates images or videos based on the text generated by the text generation unit. For example, the generation AI generates illustrations to explain the product's features or videos showing how to use the product. The audio guidance unit converts the text generated by the text generation unit into audio and performs a presentation. For example, the generation AI converts the text into audio and provides audio guidance such as, "This product was developed using the latest technology." This allows the presenter system according to an embodiment to easily give presentations to people who are not good at giving presentations.

[0059] The text generator can analyze the user's past presentation data and generate text sentences optimized for the user. For example, the text generator stores presentation materials created by the user in a database and analyzes that data. For example, it analyzes the themes and styles of past presentations and generates text sentences tailored to the user's preferences. This makes it possible to utilize the user's past data to generate more personalized text sentences.

[0060] The idea input unit can analyze the user's voice input in real time and generate text sentences in a natural conversational style. For example, when the user inputs an idea by voice, the idea input unit has the generation AI analyze the voice in real time and generate text sentences in a natural conversational style. For example, it converts what the user says directly into text, maintaining the flow of the conversation. This makes it possible to analyze the user's voice input in real time and generate text sentences in a natural conversational style.

[0061] The text generation unit can use the emotion estimation function to generate text sentences in a tone and style that corresponds to the user's emotional state. For example, when the user inputs an idea, the text generation unit uses the emotion estimation function to analyze the user's emotional state. For example, if the user is excited, the text generation unit generates text sentences in an energetic tone. This allows the text sentences to be generated in a tone and style that corresponds to the user's emotional state.

[0062] The idea input unit supports idea input in different languages ​​and can generate text sentences in multiple languages. The idea input unit, for example, allows a user to input ideas in different languages, and the generation AI analyzes the language to generate text sentences. For example, it supports multiple languages ​​such as English, French, and Chinese. This allows idea input in different languages ​​to be supported and text sentences in multiple languages ​​to be generated.

[0063] The text generation unit can automatically search for related literature and data based on ideas entered by the user and incorporate them into the text. For example, when a user enters an idea, the text generation unit uses a generation AI to automatically search for related literature and data and incorporate that information into the text. For example, it can cite the latest research papers and statistical data. This allows related literature and data to be automatically searched for and incorporated into the text based on ideas entered by the user.

[0064] The text generation unit can use the emotion estimation function to analyze the emotional response to an idea input by a user and generate text that elicits positive emotions. For example, when a user inputs an idea, the text generation unit uses the emotion estimation function to analyze the emotional response to the idea. For example, ideas that the user feels positive about are preferentially reflected in the text. This makes it possible to analyze the emotional response to an idea input by a user and generate text that elicits positive emotions.

[0065] The image generation unit can analyze a user's past presentation materials and generate images and videos optimized for the user. For example, the image generation unit stores presentation materials created by the user in a database and analyzes the data. For example, it analyzes the themes and styles of past presentations and generates images and videos tailored to the user's preferences. This allows the user's past presentation materials to be analyzed and images and videos optimized for each individual user to be generated.

[0066] The image generation unit can analyze the user's voice input and generate images and videos based on the voice content in real time. For example, when a user inputs an idea by voice, the image generation unit uses a generation AI to analyze the voice in real time and generate images and videos based on the voice content. For example, it visually represents what the user is saying. This allows the user's voice input to be analyzed and images and videos based on the voice content to be generated in real time.

[0067] The image generation unit can use the emotion estimation function to generate images and videos containing visual elements corresponding to the user's emotional state. For example, when the user inputs an idea, the image generation unit uses the emotion estimation function to analyze the user's emotional state. For example, if the user is excited, the image generation unit generates images and videos containing energetic visual elements. This makes it possible to generate images and videos containing visual elements corresponding to the user's emotional state.

[0068] The image generation unit may provide image and video templates based on different styles or themes for the user to select from. For example, the image generation unit may provide templates according to themes such as business, education, entertainment, etc. This may provide image and video templates based on different styles or themes for the user to select from.

[0069] The image generation unit can automatically generate related 3D models and animations based on ideas input by users. For example, when a user inputs an idea, the image generation unit uses a generation AI to automatically generate related 3D models and animations based on the idea. For example, the design of a new product can be displayed as a 3D model. This allows related 3D models and animations to be automatically generated based on ideas input by users.

[0070] The image generation unit can use the emotion estimation function to generate images and videos that include visual elements that the user most emotionally empathizes with. For example, the image generation unit uses the emotion estimation function to generate images and videos that include visual elements that the user most emotionally empathizes with. For example, it creates visuals that move the user. This makes it possible to generate images and videos that include visual elements that the user most emotionally empathizes with.

[0071] The voice guidance unit can analyze the user's past voice data and generate voice guidance optimized for the user. For example, the voice guidance unit stores voice data previously recorded by the user in a database and analyzes the data. For example, the voice guidance unit analyzes the tone and speaking style of past presentations and generates voice guidance tailored to the user's preferences. In this way, the user's past voice data can be analyzed and voice guidance optimized for each individual user can be generated.

[0072] The voice guidance unit can analyze the user's voice input in real time and generate voice guidance in a natural conversational format. For example, when the user inputs an idea by voice, the voice guidance unit uses the generation AI to analyze the voice in real time and generate voice guidance in a natural conversational format. For example, it converts what the user says directly into voice, maintaining the flow of the conversation. This makes it possible to analyze the user's voice input in real time and generate voice guidance in a natural conversational format.

[0073] The voice guidance unit can use the emotion estimation function to generate voice guidance in a tone and style that corresponds to the user's emotional state. For example, when the user inputs an idea, the voice guidance unit uses the emotion estimation function to analyze the user's emotional state. For example, if the user is excited, the voice guidance unit generates voice guidance in an energetic tone. This allows the voice guidance to be generated in a tone and style that corresponds to the user's emotional state.

[0074] The audio guidance unit can automatically add related music and sound effects based on the idea entered by the user. For example, when a user enters an idea, the audio guidance unit uses the generation AI to automatically add related music and sound effects based on the idea. For example, background music that matches the theme of the presentation can be selected. This allows related music and sound effects to be automatically added based on the idea entered by the user.

[0075] The voice guidance unit can use the emotion estimation function to generate voice guidance that the user most emotionally empathizes with. The voice guidance unit, for example, uses the emotion estimation function to generate voice guidance that the user most emotionally empathizes with. For example, the voice guidance unit creates voice guidance in a tone that moves the user. This allows the generation of voice guidance that the user most emotionally empathizes with.

[0076] The customization unit can analyze the user's past customization data and provide the user with optimized customization options. For example, the customization unit stores customization data of presentations that the user has previously made in a database and analyzes the data. For example, it analyzes trends in past customization and provides customization options that match the user's preferences. This makes it possible to analyze the user's past customization data and provide optimized customization options for each individual user.

[0077] The customization unit analyzes the user's voice input in real time and can perform customization in a natural conversational style. For example, when the user inputs customization instructions by voice, the customization unit has the generation AI analyze the voice in real time and perform customization in a natural conversational style. For example, the content of the user's speech is reflected directly in the customization. This allows the user's voice input to be analyzed in real time and customization to be performed in a natural conversational style.

[0078] The customization unit can use the emotion estimation function to provide customization options according to the emotional state of the user. For example, when the user performs customization, the customization unit uses the emotion estimation function to analyze the emotional state of the user. For example, if the user is excited, the customization unit provides an energetic customization option. This makes it possible to provide customization options according to the emotional state of the user.

[0079] The customization unit can provide customization templates based on different styles or themes and allow the user to select from them. For example, the customization unit can provide templates based on themes such as business, education, entertainment, etc. This allows the user to select from customization templates based on different styles or themes.

[0080] The customization unit can automatically generate related customization options based on ideas input by the user. For example, when a user inputs an idea, the customization unit uses the generation AI to automatically generate related customization options based on the idea. For example, the customization unit selects a slide layout and colors that match the theme of the presentation. This allows related customization options to be automatically generated based on the idea input by the user.

[0081] The customization unit can use the emotion estimation function to provide customization options that the user most emotionally empathizes with. For example, the customization unit can use the emotion estimation function to provide customization options that the user most emotionally empathizes with. For example, the customization unit can create visuals that move the user. This can provide customization options that the user most emotionally empathizes with.

[0082] The sharing unit can analyze the user's past sharing data and provide optimized sharing options to the user. For example, the sharing unit stores sharing data of presentations that the user has previously made in a database and analyzes the data. For example, the sharing unit analyzes past sharing methods and platforms and provides sharing options that match the user's preferences. This makes it possible to analyze the user's past sharing data and provide optimized sharing options for each individual user.

[0083] The sharing unit analyzes the user's voice input in real time and can share and distribute in a natural conversational format. For example, when the user inputs a command to share or distribute by voice, the sharing unit's generation AI analyzes the voice in real time and shares and distributes in a natural conversational format. For example, the content of the user's speech is reflected directly in the sharing. This allows the user's voice input to be analyzed in real time and shares and distributes in a natural conversational format.

[0084] The sharing unit can use the emotion estimation function to provide sharing options according to the user's emotional state. For example, when the user shares or distributes, the sharing unit uses the emotion estimation function to analyze the user's emotional state. For example, if the user is excited, the sharing unit provides energetic sharing options. This makes it possible to provide sharing options according to the user's emotional state.

[0085] The sharing section can support sharing and distribution on different platforms, allowing the user to choose. The sharing section, for example, allows the user to share a presentation on different platforms. For example, the sharing section supports multiple platforms such as social media, video conferencing tools, and websites. This allows the user to choose between sharing and distribution on different platforms.

[0086] The sharing unit can automatically generate related sharing options based on ideas input by a user. For example, when a user inputs an idea, the sharing unit's generation AI automatically generates related sharing options based on the idea. For example, it selects a sharing platform and format that matches the theme of the presentation. This makes it possible to automatically generate related sharing options based on ideas input by a user.

[0087] The sharing unit can use the emotion estimation function to provide sharing options that the user most emotionally empathizes with. For example, the sharing unit can use the emotion estimation function to provide sharing options that the user most emotionally empathizes with. For example, the sharing unit can provide a sharing method that moves the user. This can provide sharing options that the user most emotionally empathizes with.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The presenter system can also include a gesture recognition unit that recognizes user gestures. For example, when the user waves their hand, the gesture recognition unit analyzes the gesture and recognizes it as an instruction to proceed to the next slide. Also, when the user points, the relevant part can be highlighted. This allows the user to proceed with the presentation using natural hand movements.

[0090] The presenter system may further include an eye-tracking unit that tracks the user's gaze. For example, if the user looks at a particular slide for a long time, the eye-tracking unit generates text that provides a detailed explanation of the content of that slide. Furthermore, if the user moves their gaze, the system can switch slides accordingly. This allows for dynamic control of the presentation based on the user's gaze.

[0091] The presenter system can further include a heart rate measurement unit that measures the user's heart rate. For example, if the user feels nervous, the heart rate measurement unit displays advice on how to relax. If the user's heart rate is stable, the heart rate measurement unit provides support for smoothly progressing the presentation. This makes it possible to support the presentation based on the user's physiological state.

[0092] The presenter system can also estimate the user's emotions and adjust the content of the presentation based on the estimated emotions. For example, if the user is nervous, it can generate text containing humor to help them relax. If the user is excited, it can generate text to make the most of their energy and create an energetic presentation. This allows the system to deliver a presentation that is tailored to the user's emotional state.

[0093] The presenter system can also analyze the user's tone of voice and adjust the content of the presentation based on the tone of voice. For example, if the user's voice is low and calm, the system generates text in a formal tone. On the other hand, if the user's voice is high and energetic, the system generates text in a casual tone. This allows the presentation to be tailored to the user's tone of voice.

[0094] The presenter system may further include an expression analysis unit that analyzes the user's facial expression. For example, the expression analysis unit generates text in a positive tone when the user is smiling, or generates text in a formal tone when the user is serious. This allows the content of the presentation to be adjusted based on the user's facial expression.

[0095] The presenter system can also analyze feedback from users' past presentations and improve the content of the presentation based on that feedback. For example, it can generate text that reflects points for improvement pointed out in past presentations. It can also generate text that emphasizes parts of past presentations that received high marks. This allows the quality of presentations to be improved by utilizing users' past feedback.

[0096] The presenter system can also estimate the user's emotions and adjust the speed of the presentation based on the estimated emotions. For example, if the user is nervous, the presentation can be slowed down. On the other hand, if the user is relaxed, the presentation can be made to proceed smoothly. In this way, the speed of the presentation can be adjusted according to the user's emotional state.

[0097] The presenter system can also analyze the strength of the user's voice and adjust the content of the presentation based on the strength of the voice. For example, if the user's voice is strong, the system generates text in a strong tone. If the user's voice is weak, the system generates text in a gentle tone. This allows the presentation to be tailored to the strength of the user's voice.

[0098] The presenter system can also analyze the progress of the user's presentation in real time and suggest the next step based on the progress. For example, if the user is spending too much time on a particular slide, it will suggest moving on to the next slide. Also, if the user is about to exceed the presentation time, it will suggest summarizing the main points. This helps to smoothly support the progress of the user's presentation.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The idea input unit accepts the user's idea. For example, if the user inputs that he or she wants to explain the features of a new product, the idea input unit accepts the idea. Step 2: The text generation unit analyzes the ideas received by the idea input unit and generates text. For example, the generation AI generates text that details the features of a product based on the idea entered by the user. Step 3: The image generation unit generates images or videos based on the text generated by the text generation unit. For example, the generation AI generates illustrations to explain the product's features or videos showing how to use the product. Step 4: The voice guidance unit converts the text generated by the text generation unit into voice and gives a presentation. For example, the generation AI converts the text into voice and gives voice guidance in the form of "This product was developed using the latest technology."

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

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

Claims

1. an idea input unit that accepts ideas from users; a text generation unit that analyzes the idea received by the idea input unit and generates a text sentence; an image generation unit that generates an image or a video based on the text sentence generated by the text generation unit; a voice guidance unit that converts the text sentence generated by the text generation unit into voice and makes a presentation. A system characterized by:

2. The idea input unit Analyzing the user's voice input in real time and generating the text sentence in a natural conversational format 2. The system of claim 1.

3. The text generation unit Based on the idea input by the user, related literature and data are automatically searched for and incorporated into the text.

2. The system of claim 1.

4. The image generation unit Analyzing the user's past presentation materials and generating the images and videos optimized for the user 2. The system of claim 1.

5. The voice guidance unit Analyzing past voice data of the user and generating voice guidance optimized for the user 2. The system of claim 1.

6. The text generation unit Using emotion estimation functionality to generate the text passage in a tone or style that corresponds to the user's emotional state.

2. The system of claim 1.

7. The image generation unit Using an emotion estimation function, the image or the video is generated including visual elements corresponding to the emotional state of the user.

2. The system of claim 1.

8. The voice guidance unit Using an emotion estimation function, generate audio guidance that the user most emotionally empathizes with.

2. The system of claim 1.

Images