System

Abstract

An object of the system according to the embodiment is to convert words and sentences into an illustration in real time.SOLUTION: A system according to an embodiment includes a text input unit, a voice input unit, a voice recognition unit, a generation unit, and a display unit. The text input unit receives a text input. The voice input unit receives a voice input. The voice recognition unit converts the voice received by the voice input unit into a sentence. The generation unit generates an illustration based on the sentence input by the text input unit and the speech recognition unit. The display unit displays the illustration generated 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 technology has the drawback of being time-consuming to convert words and sentences into illustrations, making it difficult to create illustrations in real time.

[0005] The system according to the embodiment aims to convert words and sentences into illustrations in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a text input unit, a voice input unit, a voice recognition unit, a generation unit, and a display unit. The text input unit accepts text input. The voice input unit accepts voice input. The voice recognition unit converts the voice accepted by the voice input unit into sentences. The generation unit generates illustrations based on the sentences input by the text input unit and the voice recognition unit. The display unit displays the illustrations generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can convert words and sentences into illustrations in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The diagram generation system according to the embodiment of the present invention is a system that visually organizes information expressed in words or sentences to make it easier to understand. As a result, the diagram generation system can visually organize information expressed in words or sentences to make it easier to understand.

[0029] The diagram generation system according to the embodiment includes a text input unit, a voice input unit, a voice recognition unit, a generation unit, and a display unit. The text input unit accepts text input. For example, it accepts keyboard input. The text input unit can also accept input via copy and paste. The text input unit can also accept handwritten input. The voice input unit accepts voice input. For example, it accepts microphone input. The voice input unit can also accept recorded files. The voice input unit can also accept voice input from a smartphone. The voice recognition unit converts the voice accepted by the voice input unit into text. For example, the voice recognition unit converts the voice into text using deep learning. The voice recognition unit can also convert the voice into text using an HMM (Hidden Markov Model). The voice recognition unit can also convert the voice into text using voice tone analysis. The generation unit generates a diagram based on the text input by the text input unit and the voice recognition unit. For example, the generation unit generates a flowchart. The generation unit can also generate a mind map. The generation unit can also generate graphs. The display unit displays the diagrams generated by the generation unit. For example, the display unit displays on a monitor. The display unit can also display on a projector. The display unit can also display on a smartphone. In this way, the diagram generation system can visually organize information expressed in words or sentences, making it easier to understand.

[0030] The speech recognition unit can automatically recognize a speaker's accent or dialect and convert it into standard Japanese. For example, the speech recognition unit analyzes a speaker's accent or dialect and converts it into standard Japanese. For example, it implements a function to convert Kansai dialect into standard Japanese. The speech recognition unit also builds a speech database to automatically recognize a speaker's dialect and convert it into standard Japanese. For example, it collects dialect data from each region and trains the speech recognition system. The speech recognition unit also analyzes a speaker's accent in real time and converts it into standard Japanese. For example, even if the speaker has an English accent, it converts it into standard Japanese. This makes it possible to convert a speaker's accent or dialect into standard Japanese.

[0031] The text input unit can automatically correct the grammar and style of the input content and convert it into more readable text. The text input unit, for example, analyzes the grammar of the input content and automatically corrects it. For example, it implements a function to convert incorrect grammar into correct grammar. The text input unit also analyzes the style of the input content and converts it into more readable text. For example, it adds a function to change redundant expressions into concise expressions. The text input unit also analyzes the grammar and style of the input content in real time and makes correction suggestions. For example, it detects grammatical errors while the user is entering text and presents correction suggestions. This makes it possible to automatically correct the grammar and style of the input content and convert it into more readable text.

[0032] The system can recognize handwritten input and convert the handwritten input into text. For example, the system develops a system that recognizes handwritten input and automatically converts handwritten characters into text. For example, handwritten input is entered on a tablet or smartphone and converted into text. The system also builds a machine learning model for recognizing handwritten input and improves the accuracy of handwritten character recognition. For example, the model is trained using a dataset of handwritten characters. The system also recognizes handwritten input in real time and converts it into text. For example, characters are recognized as they are being entered by hand and instantly converted into text. This makes it possible to recognize handwritten input and convert it into text.

[0033] The system can recognize user gestures and reflect the gestures in input content. For example, the system develops a system that recognizes user gestures and reflects them in input content. For example, it analyzes hand movements and finger gestures and executes specific operations. The system also uses gesture recognition technology to understand the user's intentions and reflect them in input content. For example, by making a specific shape with your fingers, a specific command is executed. The system also uses a gesture recognition system to analyze the user's movements in real time and reflect them in input content. For example, using hand movements to operate a cursor and select text. This makes it possible to recognize user gestures and reflect them in input content.

[0034] The generation unit can automatically collect background information about a text and generate a more detailed illustration. For example, the generation AI automatically collects background information about a text and generates a detailed illustration based on that information. For example, it collects information about specific technologies or concepts and reflects it in the illustration. In addition, to collect background information about a text, the generation AI searches databases and literature on the Internet to obtain related information. For example, it reflects the latest research results on a specific topic in the illustration. In addition, the generation unit collects background information about a text in real time and generates a detailed illustration. For example, it searches for related information based on keywords in the text and adds it to the illustration. This makes it possible to automatically collect background information about a text and generate a detailed illustration.

[0035] The generation unit can automatically complete ambiguous parts of a sentence and generate a complete diagram. For example, the generation AI automatically completes ambiguous parts of a sentence and generates a complete diagram. For example, it infers unclear parts in a sentence and reflects them in the diagram. In addition, to complete ambiguous parts of a sentence, the generation AI refers to past data or similar sentences and adds appropriate information. For example, it converts ambiguous procedures into specific steps. In addition, the generation unit analyzes ambiguous parts of a sentence in real time and generates a diagram by adding complementary information. For example, it defines unclear terms in a sentence and reflects them in the diagram. This makes it possible to automatically complete ambiguous parts of a sentence and generate a complete diagram.

[0036] The generation unit can automatically translate text entered in different languages ​​and generate illustrations. For example, the generation AI automatically translates text entered in different languages ​​and generates illustrations based on the content. For example, the generation unit translates text entered in English into Japanese and creates an illustration. In addition, to translate text entered in different languages, the generation AI uses a multilingual translation model. For example, a translation engine that supports multiple languages ​​is incorporated. In addition, the generation unit translates text entered in different languages ​​in real time and generates illustrations. For example, the content spoken in a foreign language during a meeting is instantly translated and reflected in the illustration. This makes it possible to automatically translate text entered in different languages ​​and generate illustrations.

[0037] The generation unit illustrates the content of the text as a 3D model, allowing for the provision of more three-dimensional visual information. For example, the generation AI analyzes the content of the text and generates an illustration as a 3D model. For example, a blueprint for a building is displayed as a 3D model. In addition, the generation unit uses 3D modeling technology to convert the content of the text into a 3D model. For example, a product prototype is generated as a 3D model. In addition, the generation AI analyzes the content of the text in real time and generates an illustration as a 3D model. For example, the content spoken during a meeting is displayed as a 3D model. This makes it possible to illustrate the content of the text as a 3D model and provide three-dimensional visual information.

[0038] The generation unit can learn the user's past selection history when selecting an illustration format and suggest the optimal format. The generation unit, for example, develops a system that learns the user's past selection history when selecting an illustration format and suggests the optimal format. For example, the generation unit makes suggestions based on formats selected by the user in the past. The generation unit also analyzes the user's selection history and suggests illustration formats based on the results. For example, it preferentially suggests formats that the user uses frequently. The generation unit also builds a system that automatically selects an illustration format that matches the user's preferences based on the past selection history. For example, it learns the user's selection patterns and suggests the optimal format. This makes it possible to learn the user's past selection history and suggest the optimal format.

[0039] The generation unit can automatically add related data and information when a diagram format is selected, thereby generating a more comprehensive diagram. The generation unit develops a system that automatically adds related data and information when a diagram format is selected, thereby generating a comprehensive diagram. For example, the generation unit obtains related information from a database and reflects it in the diagram. The generation unit also automatically collects related information when a diagram format is selected and adds it to the diagram. For example, the generation unit searches for data on the Internet and incorporates it into the diagram. The generation unit also builds a system that collects related data in real time when a diagram format is selected, thereby generating a comprehensive diagram. For example, sensor data is obtained and reflected in the diagram. This makes it possible to automatically add related data and information and generate a comprehensive diagram.

[0040] The generation unit can add animation to the diagram format to provide dynamic visual information. The generation unit, for example, develops a system that adds animation to the diagram format to provide dynamic visual information. For example, the steps of a flowchart are displayed using animation. The generation unit also adds animation to the diagram format to visually indicate changes in information. For example, the generation unit adds animation that dynamically updates data points in a graph. The generation unit also adds animation to the diagram format to allow users to intuitively understand the information. For example, the generation unit adds animation that highlights cells in a table. This makes it possible to add animation to the diagram format to provide dynamic visual information.

[0041] The generation unit can add audio explanations to the diagram format to provide information both visually and audibly. For example, the generation unit develops a system that adds audio explanations to the diagram format to provide information both visually and audibly. For example, audio explanations are added to each step of a flowchart. The generation unit also adds audio explanations to the diagram format to deepen understanding of the information. For example, audio explanations are added for data points in a graph. The generation unit also adds audio explanations to the diagram format to allow the user to understand the information from multiple perspectives. For example, audio explanations are added for each cell in a table. This makes it possible to add audio explanations to the diagram format to provide information both visually and audibly.

[0042] The generation unit can automatically summarize what participants say and reflect the summary in the diagram when generating a diagram in real time. The generation unit, for example, develops a system that automatically summarizes what participants say and reflects the summary in the diagram when generating a diagram in real time. For example, the generation unit summarizes what is said during a meeting and reflects it in a flowchart. The generation unit also analyzes what participants say in real time and reflects the summary in the diagram. For example, it summarizes ideas during a brainstorming session and adds them to a mind map. The generation unit also uses an automatic summarization system to summarize what participants say in real time and reflect the summary in the diagram. For example, it summarizes the main points of a discussion and reflects them in a graph. This makes it possible to automatically summarize what participants say and reflect the summary in the diagram when generating a diagram in real time.

[0043] The generation unit can instantly reflect participant feedback and update the diagram when generating the diagram in real time. The generation unit, for example, develops a system that instantly reflects participant feedback and updates the diagram when generating the diagram in real time. For example, when a participant proposes a new idea, it is immediately added to the diagram. The generation unit also analyzes participant feedback in real time and updates the diagram based on the results. For example, when a participant proposes a correction, it immediately corrects the diagram. The generation unit also uses a feedback system to collect participant opinions in real time and update the diagram based on the results. For example, when a participant adds a comment, it is immediately reflected in the diagram. This makes it possible to instantly reflect participant feedback and update the diagram when generating the diagram in real time.

[0044] The generation unit can synchronize between different devices when generating diagrams in real time, allowing all participants to share the same diagrams. The generation unit, for example, develops a system that synchronizes between different devices when generating diagrams in real time, allowing all participants to share the same diagrams. For example, synchronization is achieved between smartphones, tablets, and PCs. The generation unit also uses real-time communication technology to synchronize between different devices. For example, WebSocket is used to synchronize diagram data in real time. The generation unit also synchronizes between different devices when generating diagrams in real time, allowing all participants to share the same diagrams. For example, a cloud-based synchronization system is built. This makes it possible to synchronize between different devices, allowing all participants to share the same diagrams.

[0045] The generation unit can provide a function that allows participants to add notes and comments directly to diagrams when they are being generated in real time. The generation unit, for example, develops a system that provides a function that allows participants to add notes and comments directly to diagrams when they are being generated in real time. For example, it supports handwritten input on tablets and smartphones. The generation unit also provides an interface that allows participants to add notes and comments directly to diagrams. For example, it adds text boxes and drawing tools to the diagrams. The generation unit also provides a function that allows participants to add notes and comments directly to diagrams when they are being generated in real time. For example, it builds an interface like an online whiteboard. This makes it possible to provide a function that allows participants to add notes and comments directly to diagrams when they are being generated in real time.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The diagram generation system can also recognize user gestures and reflect them in the input content. For example, it can analyze hand movements and finger gestures to perform specific operations. It also uses gesture recognition technology to understand the user's intentions and reflect them in the input content. For example, making a specific shape with your fingers can execute a specific command. The gesture recognition system can also analyze the user's movements in real time and reflect them in the input content. For example, using hand movements to operate the cursor and select text. This makes it possible to recognize the user's gestures and reflect them in the input content.

[0048] The generation unit can automatically collect background information about a text and generate a more detailed illustration. For example, the generation AI automatically collects background information about a text and generates a detailed illustration based on that information. For example, it collects information about specific technologies or concepts and reflects it in the illustration. In addition, to collect background information about a text, the generation AI searches databases and literature on the Internet to obtain related information. For example, it reflects the latest research results on a specific topic in the illustration. In addition, the generation unit allows the generation AI to collect background information about a text in real time and generate a detailed illustration. For example, it searches for related information based on keywords in the text and adds it to the illustration. This makes it possible to automatically collect background information about a text and generate a detailed illustration.

[0049] The generation unit can automatically complete ambiguous parts of a sentence and generate a complete diagram. For example, the generation AI can automatically complete ambiguous parts of a sentence and generate a complete diagram. For example, it can infer unclear parts in a sentence and reflect them in the diagram. To complete ambiguous parts of a sentence, the generation AI can refer to past data or similar sentences and add appropriate information. For example, it can convert ambiguous procedures into specific steps. The generation unit can also analyze ambiguous parts of a sentence in real time and generate a diagram by adding complementary information. For example, it can define unclear terms in a sentence and reflect them in the diagram. This makes it possible to automatically complete ambiguous parts of a sentence and generate a complete diagram.

[0050] The generation unit can automatically translate text entered in different languages ​​and generate illustrations. For example, the generation AI can automatically translate text entered in different languages ​​and generate illustrations based on the content. For example, it can translate text entered in English into Japanese and create illustrations. In addition, to translate text entered in different languages, the generation AI can use a multilingual translation model. For example, it can incorporate a translation engine that supports multiple languages. In addition, the generation unit can translate text entered in different languages ​​in real time and generate illustrations. For example, it can instantly translate what is said in a foreign language during a meeting and reflect it in the illustration. This makes it possible to automatically translate text entered in different languages ​​and generate illustrations.

[0051] The generation unit illustrates the content of the text as a 3D model, allowing for the provision of more three-dimensional visual information. For example, the generation AI analyzes the content of the text and generates an illustration as a 3D model. For example, a building blueprint is displayed as a 3D model. The generation unit also uses 3D modeling technology to convert the content of the text into a 3D model. For example, a product prototype is generated as a 3D model. The generation unit also analyzes the content of the text in real time and generates an illustration as a 3D model. For example, the content spoken during a meeting is displayed as a 3D model. This makes it possible to illustrate the content of the text as a 3D model and provide three-dimensional visual information.

[0052] The generation unit can add animation to the diagram format to provide dynamic visual information. For example, a system is developed that adds animation to the diagram format to provide dynamic visual information. For example, the steps of a flowchart are displayed using animation. The generation unit also adds animation to the diagram format to visually indicate changes in information. For example, an animation is added that dynamically updates data points in a graph. The generation unit also adds animation to the diagram format to allow users to intuitively understand the information. For example, an animation is added that highlights cells in a table. This makes it possible to add animation to the diagram format to provide dynamic visual information.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The text input unit accepts text input, such as keyboard input, input by copy and paste, or handwritten input. Step 2: The audio input unit accepts audio input. For example, it can accept microphone input, audio recording files, and audio input from a smartphone. Step 3: The speech recognition unit converts the speech received by the speech input unit into text. For example, deep learning, HMM (Hidden Markov Model), and speech tone analysis can be used to convert speech into text. Step 4: The generator generates diagrams based on the text input by the text input unit and the speech recognition unit. For example, flowcharts, mind maps, and graphs can be generated. Step 5: The display unit displays the illustration generated by the generation unit. For example, the display unit can display the illustration on a monitor, a projector, or a smartphone.

[0055] (Example 2) The diagram generation system according to the embodiment of the present invention is a system that visually organizes information expressed in words or sentences to make it easier to understand. As a result, the diagram generation system can visually organize information expressed in words or sentences to make it easier to understand.

[0056] The diagram generation system according to the embodiment includes a text input unit, a voice input unit, a voice recognition unit, a generation unit, and a display unit. The text input unit accepts text input. For example, it accepts keyboard input. The text input unit can also accept input via copy and paste. The text input unit can also accept handwritten input. The voice input unit accepts voice input. For example, it accepts microphone input. The voice input unit can also accept recorded files. The voice input unit can also accept voice input from a smartphone. The voice recognition unit converts the voice accepted by the voice input unit into text. For example, the voice recognition unit converts the voice into text using deep learning. The voice recognition unit can also convert the voice into text using an HMM (Hidden Markov Model). The voice recognition unit can also convert the voice into text using voice tone analysis. The generation unit generates a diagram based on the text input by the text input unit and the voice recognition unit. For example, the generation unit generates a flowchart. The generation unit can also generate a mind map. The generation unit can also generate graphs. The display unit displays the diagrams generated by the generation unit. For example, the display unit displays on a monitor. The display unit can also display on a projector. The display unit can also display on a smartphone. In this way, the diagram generation system can visually organize information expressed in words or sentences, making it easier to understand.

[0057] The speech recognition unit can estimate the speaker's emotions and generate sentences according to those emotions. The speech recognition unit, for example, analyzes the tone and speed of the speaker's voice to estimate the emotion. For example, if the speaker is excited, it adds exclamation marks or emphasis to the sentence. The speech recognition unit also uses facial expression analysis to estimate the speaker's emotions. For example, it uses a camera to analyze the speaker's facial expressions and generates sentences according to the emotion. The speech recognition unit also analyzes the speaker's emotions in real time and provides feedback according to the emotion. For example, if the speaker is nervous, it generates sentences that make the speaker feel relaxed. This makes it possible to generate sentences according to the speaker's emotions.

[0058] The speech recognition unit can automatically recognize a speaker's accent or dialect and convert it into standard Japanese. For example, the speech recognition unit analyzes a speaker's accent or dialect and converts it into standard Japanese. For example, it implements a function to convert Kansai dialect into standard Japanese. The speech recognition unit also builds a speech database to automatically recognize a speaker's dialect and convert it into standard Japanese. For example, it collects dialect data from each region and trains the speech recognition system. The speech recognition unit also analyzes a speaker's accent in real time and converts it into standard Japanese. For example, even if the speaker has an English accent, it converts it into standard Japanese. This makes it possible to convert a speaker's accent or dialect into standard Japanese.

[0059] The text input unit can automatically correct the grammar and style of the input content and convert it into more readable text. The text input unit, for example, analyzes the grammar of the input content and automatically corrects it. For example, it implements a function to convert incorrect grammar into correct grammar. The text input unit also analyzes the style of the input content and converts it into more readable text. For example, it adds a function to change redundant expressions into concise expressions. The text input unit also analyzes the grammar and style of the input content in real time and makes correction suggestions. For example, it detects grammatical errors while the user is entering text and presents correction suggestions. This makes it possible to automatically correct the grammar and style of the input content and convert it into more readable text.

[0060] The system can recognize handwritten input and convert the handwritten input into text. For example, the system develops a system that recognizes handwritten input and automatically converts handwritten characters into text. For example, handwritten input is entered on a tablet or smartphone and converted into text. The system also builds a machine learning model for recognizing handwritten input and improves the accuracy of handwritten character recognition. For example, the model is trained using a dataset of handwritten characters. The system also recognizes handwritten input in real time and converts it into text. For example, characters are recognized as they are being entered by hand and instantly converted into text. This makes it possible to recognize handwritten input and convert it into text.

[0061] The system can recognize user gestures and reflect the gestures in input content. For example, the system develops a system that recognizes user gestures and reflects them in input content. For example, it analyzes hand movements and finger gestures and executes specific operations. The system also uses gesture recognition technology to understand the user's intentions and reflect them in input content. For example, by making a specific shape with your fingers, a specific command is executed. The system also uses a gesture recognition system to analyze the user's movements in real time and reflect them in input content. For example, using hand movements to operate a cursor and select text. This makes it possible to recognize user gestures and reflect them in input content.

[0062] The system can use the emotion estimation function to analyze the user's emotions in real time when they are typing and provide feedback that elicits positive emotions. For example, the system uses the emotion estimation function to analyze the user's emotions in real time when they are typing and provide positive feedback. For example, if the user is feeling down, the system may display an encouraging message. The system also analyzes the user's emotions in real time and provides an interface for eliciting positive emotions. For example, the system may display an animation that makes the user smile while typing. The system also provides advice in real time to make the user's emotions more positive based on the emotion estimation data. For example, if the user is feeling stressed, the system may suggest ways to relax. This makes it possible to analyze the user's emotions in real time and provide feedback that elicits positive emotions.

[0063] The generation unit can understand the emotional nuances of a sentence and generate illustrations that correspond to the emotion. For example, the generation AI analyzes the emotional nuances of a sentence and generates illustrations that correspond to the emotion. For example, bright colors are used for positive content and subdued colors for negative content. In addition, to understand the emotional nuances of a sentence, the generation AI performs emotion analysis and generates illustrations based on the results. For example, if the emotion of joy is strong, bright colors and cheerful illustrations are used. In addition, the generation AI analyzes the emotional nuances of a sentence in real time and generates illustrations that correspond to the emotion. For example, the style of the illustration is changed to match the tone of the sentence. This makes it possible to understand the emotional nuances of a sentence and generate illustrations that correspond to the emotion.

[0064] The generation unit can automatically collect background information about a text and generate a more detailed illustration. For example, the generation AI automatically collects background information about a text and generates a detailed illustration based on that information. For example, it collects information about specific technologies or concepts and reflects it in the illustration. In addition, to collect background information about a text, the generation AI searches databases and literature on the Internet to obtain related information. For example, it reflects the latest research results on a specific topic in the illustration. In addition, the generation unit collects background information about a text in real time and generates a detailed illustration. For example, it searches for related information based on keywords in the text and adds it to the illustration. This makes it possible to automatically collect background information about a text and generate a detailed illustration.

[0065] The generation unit can automatically complete ambiguous parts of a sentence and generate a complete diagram. For example, the generation AI automatically completes ambiguous parts of a sentence and generates a complete diagram. For example, it infers unclear parts in a sentence and reflects them in the diagram. In addition, to complete ambiguous parts of a sentence, the generation AI refers to past data or similar sentences and adds appropriate information. For example, it converts ambiguous procedures into specific steps. In addition, the generation unit analyzes ambiguous parts of a sentence in real time and generates a diagram by adding complementary information. For example, it defines unclear terms in a sentence and reflects them in the diagram. This makes it possible to automatically complete ambiguous parts of a sentence and generate a complete diagram.

[0066] The generation unit can automatically translate text entered in different languages ​​and generate illustrations. For example, the generation AI automatically translates text entered in different languages ​​and generates illustrations based on the content. For example, the generation unit translates text entered in English into Japanese and creates an illustration. In addition, to translate text entered in different languages, the generation AI uses a multilingual translation model. For example, a translation engine that supports multiple languages ​​is incorporated. In addition, the generation unit translates text entered in different languages ​​in real time and generates illustrations. For example, the content spoken in a foreign language during a meeting is instantly translated and reflected in the illustration. This makes it possible to automatically translate text entered in different languages ​​and generate illustrations.

[0067] The generation unit illustrates the content of the text as a 3D model, allowing for the provision of more three-dimensional visual information. For example, the generation AI analyzes the content of the text and generates an illustration as a 3D model. For example, a blueprint for a building is displayed as a 3D model. In addition, the generation unit uses 3D modeling technology to convert the content of the text into a 3D model. For example, a product prototype is generated as a 3D model. In addition, the generation AI analyzes the content of the text in real time and generates an illustration as a 3D model. For example, the content spoken during a meeting is displayed as a 3D model. This makes it possible to illustrate the content of the text as a 3D model and provide three-dimensional visual information.

[0068] The generation unit can use the emotion estimation function to automatically select a style of illustration based on the user's emotion. The generation unit, for example, uses the emotion estimation function to automatically select a style of illustration based on the user's emotion. For example, if the user is relaxed, it generates an illustration with soft colors. The generation unit also analyzes the user's emotion in real time and selects a style of illustration based on the results. For example, if the user is excited, it uses bright colors and dynamic animations. The generation unit also develops a system that automatically adjusts the style of illustration to match the user's emotion based on the emotion estimation data. For example, it changes the design of the illustration according to changes in the user's emotion. This makes it possible to automatically select a style of illustration based on the user's emotion.

[0069] The generation unit can estimate the user's emotions when selecting an illustration format and suggest a format that corresponds to the emotion. The generation unit develops a system that estimates the user's emotions when selecting an illustration format and suggests a format that corresponds to the emotion. For example, if the user is feeling stressed, a simple format is suggested. The generation unit also analyzes the user's emotions in real time and suggests an illustration format based on the results. For example, if the user is relaxed, a detailed illustration format is suggested. The generation unit also builds a system that automatically selects an illustration format that matches the user's emotions based on the emotion estimation data. For example, the illustration format is adjusted according to changes in the user's emotions. This makes it possible to suggest an illustration format that corresponds to the user's emotions.

[0070] The generation unit can learn the user's past selection history when selecting an illustration format and suggest the optimal format. The generation unit, for example, develops a system that learns the user's past selection history when selecting an illustration format and suggests the optimal format. For example, the generation unit makes suggestions based on formats selected by the user in the past. The generation unit also analyzes the user's selection history and suggests illustration formats based on the results. For example, it preferentially suggests formats that the user uses frequently. The generation unit also builds a system that automatically selects an illustration format that matches the user's preferences based on the past selection history. For example, it learns the user's selection patterns and suggests the optimal format. This makes it possible to learn the user's past selection history and suggest the optimal format.

[0071] The generation unit can automatically add related data and information when a diagram format is selected, thereby generating a more comprehensive diagram. The generation unit develops a system that automatically adds related data and information when a diagram format is selected, thereby generating a comprehensive diagram. For example, the generation unit obtains related information from a database and reflects it in the diagram. The generation unit also automatically collects related information when a diagram format is selected and adds it to the diagram. For example, the generation unit searches for data on the Internet and incorporates it into the diagram. The generation unit also builds a system that collects related data in real time when a diagram format is selected, thereby generating a comprehensive diagram. For example, sensor data is obtained and reflected in the diagram. This makes it possible to automatically add related data and information and generate a comprehensive diagram.

[0072] The generation unit can add animation to the diagram format to provide dynamic visual information. The generation unit, for example, develops a system that adds animation to the diagram format to provide dynamic visual information. For example, the steps of a flowchart are displayed using animation. The generation unit also adds animation to the diagram format to visually indicate changes in information. For example, the generation unit adds animation that dynamically updates data points in a graph. The generation unit also adds animation to the diagram format to allow users to intuitively understand the information. For example, the generation unit adds animation that highlights cells in a table. This makes it possible to add animation to the diagram format to provide dynamic visual information.

[0073] The generation unit can add audio explanations to the diagram format to provide information both visually and audibly. For example, the generation unit develops a system that adds audio explanations to the diagram format to provide information both visually and audibly. For example, audio explanations are added to each step of a flowchart. The generation unit also adds audio explanations to the diagram format to deepen understanding of the information. For example, audio explanations are added for data points in a graph. The generation unit also adds audio explanations to the diagram format to allow the user to understand the information from multiple perspectives. For example, audio explanations are added for each cell in a table. This makes it possible to add audio explanations to the diagram format to provide information both visually and audibly.

[0074] The generation unit can use the emotion estimation function to customize the illustration format in real time according to the user's emotions. The generation unit, for example, develops a system that uses the emotion estimation function to customize the illustration format in real time according to the user's emotions. For example, if the user is relaxed, it generates an illustration with soft colors. The generation unit also analyzes the user's emotions in real time and customizes the illustration format based on the results. For example, if the user is excited, it uses bright colors and dynamic animations. The generation unit also builds a system that automatically adjusts the illustration format to match the user's emotions based on the emotion estimation data. For example, it changes the design of the illustration according to changes in the user's emotions. This makes it possible to customize the illustration format in real time according to the user's emotions.

[0075] The generation unit can estimate the emotions of participants when generating illustrations in real time and display illustrations according to their emotions. The generation unit, for example, develops a system that estimates the emotions of participants when generating illustrations in real time and displays illustrations according to their emotions. For example, if a participant is nervous, it uses colors that have a relaxing effect. The generation unit also analyzes the emotions of participants in real time and displays illustrations based on the results. For example, if a participant is excited, it uses vivid colors and dynamic animations. The generation unit also builds a system that generates illustrations in real time that match the emotions of participants based on emotion estimation data. For example, it changes the design of the illustration according to changes in the emotions of participants. This makes it possible to estimate the emotions of participants when generating illustrations in real time and display illustrations according to their emotions.

[0076] The generation unit can automatically summarize what participants say and reflect the summary in the diagram when generating a diagram in real time. The generation unit, for example, develops a system that automatically summarizes what participants say and reflects the summary in the diagram when generating a diagram in real time. For example, the generation unit summarizes what is said during a meeting and reflects it in a flowchart. The generation unit also analyzes what participants say in real time and reflects the summary in the diagram. For example, it summarizes ideas during a brainstorming session and adds them to a mind map. The generation unit also uses an automatic summarization system to summarize what participants say in real time and reflect the summary in the diagram. For example, it summarizes the main points of a discussion and reflects them in a graph. This makes it possible to automatically summarize what participants say and reflect the summary in the diagram when generating a diagram in real time.

[0077] The generation unit can instantly reflect participant feedback and update the diagram when generating the diagram in real time. The generation unit, for example, develops a system that instantly reflects participant feedback and updates the diagram when generating the diagram in real time. For example, when a participant proposes a new idea, it is immediately added to the diagram. The generation unit also analyzes participant feedback in real time and updates the diagram based on the results. For example, when a participant proposes a correction, it immediately corrects the diagram. The generation unit also uses a feedback system to collect participant opinions in real time and update the diagram based on the results. For example, when a participant adds a comment, it is immediately reflected in the diagram. This makes it possible to instantly reflect participant feedback and update the diagram when generating the diagram in real time.

[0078] The generation unit can synchronize between different devices when generating diagrams in real time, allowing all participants to share the same diagrams. The generation unit, for example, develops a system that synchronizes between different devices when generating diagrams in real time, allowing all participants to share the same diagrams. For example, synchronization is achieved between smartphones, tablets, and PCs. The generation unit also uses real-time communication technology to synchronize between different devices. For example, WebSocket is used to synchronize diagram data in real time. The generation unit also synchronizes between different devices when generating diagrams in real time, allowing all participants to share the same diagrams. For example, a cloud-based synchronization system is built. This makes it possible to synchronize between different devices, allowing all participants to share the same diagrams.

[0079] The generation unit can provide a function that allows participants to add notes and comments directly to diagrams when they are being generated in real time. The generation unit, for example, develops a system that provides a function that allows participants to add notes and comments directly to diagrams when they are being generated in real time. For example, it supports handwritten input on tablets and smartphones. The generation unit also provides an interface that allows participants to add notes and comments directly to diagrams. For example, it adds text boxes and drawing tools to the diagrams. The generation unit also provides a function that allows participants to add notes and comments directly to diagrams when they are being generated in real time. For example, it builds an interface like an online whiteboard. This makes it possible to provide a function that allows participants to add notes and comments directly to diagrams when they are being generated in real time.

[0080] The generation unit uses the emotion estimation function to analyze the emotions of participants when generating diagrams in real time, and can make suggestions to support the progress of the discussion. The generation unit, for example, develops a system that uses the emotion estimation function to analyze the emotions of participants when generating diagrams in real time, and makes suggestions to support the progress of the discussion. For example, if a participant is nervous, the generation unit makes a suggestion to relax. The generation unit also analyzes the emotions of participants in real time, and makes a suggestion to support the progress of the discussion based on the results. For example, if a participant is excited, the generation unit makes a suggestion to calm down. The generation unit also builds a system that makes suggestions in real time to support the progress of the discussion that are tailored to the emotions of participants, based on the emotion estimation data. For example, the direction of the discussion is adjusted according to changes in the emotions of participants. This makes it possible to analyze the emotions of participants when generating diagrams in real time, and make suggestions to support the progress of the discussion.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The diagram generation system can also recognize user gestures and reflect them in the input content. For example, it can analyze hand movements and finger gestures to perform specific operations. It also uses gesture recognition technology to understand the user's intentions and reflect them in the input content. For example, making a specific shape with your fingers can execute a specific command. The gesture recognition system can also analyze the user's movements in real time and reflect them in the input content. For example, using hand movements to operate the cursor and select text. This makes it possible to recognize the user's gestures and reflect them in the input content.

[0083] The speech recognition unit can estimate the speaker's emotions and generate sentences that correspond to those emotions. For example, it analyzes the tone and speed of the speaker's voice to estimate emotions. For example, if the speaker is excited, it adds exclamation marks or emphasis to the sentence. The speech recognition unit also uses facial expression analysis to estimate the speaker's emotions. For example, it uses a camera to analyze the speaker's facial expressions and generates sentences that correspond to those emotions. The speech recognition unit also analyzes the speaker's emotions in real time and provides feedback that corresponds to those emotions. For example, if the speaker is nervous, it generates sentences that make the speaker feel relaxed. This makes it possible to generate sentences that correspond to the speaker's emotions.

[0084] The generation unit can understand the emotional nuances of a sentence and generate illustrations that correspond to the emotion. For example, the generation AI analyzes the emotional nuances of a sentence and generates illustrations that correspond to the emotion. For example, bright colors are used for positive content and subdued colors for negative content. In addition, to understand the emotional nuances of a sentence, the generation AI performs emotion analysis and generates illustrations based on the results. For example, if the emotion of joy is strong, bright colors and cheerful illustrations are used. In addition, the generation AI analyzes the emotional nuances of a sentence in real time and generates illustrations that correspond to the emotion. For example, the style of the illustration is changed to match the tone of the sentence. This makes it possible to understand the emotional nuances of a sentence and generate illustrations that correspond to the emotion.

[0085] The generation unit can estimate the user's emotions when selecting an illustration format and suggest a format that corresponds to the emotion. For example, we develop a system that estimates the user's emotions when selecting an illustration format and suggests a format that corresponds to the emotion. For example, if the user is feeling stressed, a simple format is suggested. The generation unit also analyzes the user's emotions in real time and suggests an illustration format based on the results. For example, if the user is relaxed, a detailed illustration format is suggested. The generation unit also builds a system that automatically selects an illustration format that matches the user's emotions based on the emotion estimation data. For example, the illustration format is adjusted according to changes in the user's emotions. This makes it possible to suggest an illustration format that corresponds to the user's emotions.

[0086] The generation unit can use the emotion estimation function to analyze the emotions of participants when generating diagrams in real time and make suggestions to support the progress of the discussion. For example, a system is developed that uses the emotion estimation function to analyze the emotions of participants when generating diagrams in real time and make suggestions to support the progress of the discussion. For example, if a participant is nervous, the system makes a suggestion to relax. The generation unit also analyzes the emotions of participants in real time and makes suggestions to support the progress of the discussion based on the results. For example, if a participant is excited, the system makes a suggestion to calm down. The generation unit also builds a system that uses the emotion estimation data to make suggestions in real time to support the progress of the discussion that are tailored to the emotions of participants. For example, the system adjusts the direction of the discussion according to changes in the emotions of participants. This makes it possible to analyze the emotions of participants when generating diagrams in real time and make suggestions to support the progress of the discussion.

[0087] The generation unit can automatically collect background information about a text and generate a more detailed illustration. For example, the generation AI automatically collects background information about a text and generates a detailed illustration based on that information. For example, it collects information about specific technologies or concepts and reflects it in the illustration. In addition, to collect background information about a text, the generation AI searches databases and literature on the Internet to obtain related information. For example, it reflects the latest research results on a specific topic in the illustration. In addition, the generation unit allows the generation AI to collect background information about a text in real time and generate a detailed illustration. For example, it searches for related information based on keywords in the text and adds it to the illustration. This makes it possible to automatically collect background information about a text and generate a detailed illustration.

[0088] The generation unit can automatically complete ambiguous parts of a sentence and generate a complete diagram. For example, the generation AI can automatically complete ambiguous parts of a sentence and generate a complete diagram. For example, it can infer unclear parts in a sentence and reflect them in the diagram. To complete ambiguous parts of a sentence, the generation AI can refer to past data or similar sentences and add appropriate information. For example, it can convert ambiguous procedures into specific steps. The generation unit can also analyze ambiguous parts of a sentence in real time and generate a diagram by adding complementary information. For example, it can define unclear terms in a sentence and reflect them in the diagram. This makes it possible to automatically complete ambiguous parts of a sentence and generate a complete diagram.

[0089] The generation unit can automatically translate text entered in different languages ​​and generate illustrations. For example, the generation AI can automatically translate text entered in different languages ​​and generate illustrations based on the content. For example, it can translate text entered in English into Japanese and create illustrations. In addition, to translate text entered in different languages, the generation AI can use a multilingual translation model. For example, it can incorporate a translation engine that supports multiple languages. In addition, the generation unit can translate text entered in different languages ​​in real time and generate illustrations. For example, it can instantly translate what is said in a foreign language during a meeting and reflect it in the illustration. This makes it possible to automatically translate text entered in different languages ​​and generate illustrations.

[0090] The generation unit illustrates the content of the text as a 3D model, allowing for the provision of more three-dimensional visual information. For example, the generation AI analyzes the content of the text and generates an illustration as a 3D model. For example, a building blueprint is displayed as a 3D model. The generation unit also uses 3D modeling technology to convert the content of the text into a 3D model. For example, a product prototype is generated as a 3D model. The generation unit also analyzes the content of the text in real time and generates an illustration as a 3D model. For example, the content spoken during a meeting is displayed as a 3D model. This makes it possible to illustrate the content of the text as a 3D model and provide three-dimensional visual information.

[0091] The generation unit can add animation to the diagram format to provide dynamic visual information. For example, a system is developed that adds animation to the diagram format to provide dynamic visual information. For example, the steps of a flowchart are displayed using animation. The generation unit also adds animation to the diagram format to visually indicate changes in information. For example, an animation is added that dynamically updates data points in a graph. The generation unit also adds animation to the diagram format to allow users to intuitively understand the information. For example, an animation is added that highlights cells in a table. This makes it possible to add animation to the diagram format to provide dynamic visual information.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The text input unit accepts text input, such as keyboard input, input by copy and paste, or handwritten input. Step 2: The audio input unit accepts audio input. For example, it can accept microphone input, audio recording files, and audio input from a smartphone. Step 3: The speech recognition unit converts the speech received by the speech input unit into text. For example, deep learning, HMM (Hidden Markov Model), and speech tone analysis can be used to convert speech into text. Step 4: The generator generates diagrams based on the text input by the text input unit and the speech recognition unit. For example, flowcharts, mind maps, and graphs can be generated. Step 5: The display unit displays the illustration generated by the generation unit. For example, the display unit can display the illustration on a monitor, a projector, or a smartphone.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] 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.

[0109] 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.

[0110] 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 AI 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] 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.

[0124] 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.

[0125] 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 AI 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] 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.

[0140] 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.

[0141] 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 AI 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 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 text input unit that accepts text input; a voice input unit that accepts voice input; a speech recognition unit that converts the speech received by the speech input unit into text; a generation unit that generates an illustration based on a sentence input by the text input unit and the voice recognition unit; a display unit that displays the illustration generated by the generation unit; A system characterized by:

2. The voice recognition unit Estimate the speaker's emotions and create sentences based on those emotions 2. The system of claim 1.

3. The voice recognition unit Automatically recognizes the speaker's accent or dialect and converts it into standard Japanese 2. The system of claim 1.

4. The text input unit Automatically corrects grammar and style of input to make it easier to read 2. The system of claim 1.

5. The system comprises: Recognize handwritten input and convert the handwritten input into text 2. The system of claim 1.

6. The system comprises: Recognizing a user's gesture and reflecting the gesture in the input content 2. The system of claim 1.

7. The system comprises: Analyzes user emotions in real time as they type and provides feedback that elicits positive emotions 2. The system of claim 1.

8. The generation unit Understand the emotional nuances of the text and generate illustrations that correspond to the emotions 2. The system of claim 1.

Images