System

Abstract

An object of the system according to the embodiment is to enable a user to organize his / her opinion, construct a persuasive argument, and participate in a social discussion with confidence.SOLUTION: A system according to an embodiment includes an opinion organization unit, an argument construction unit, and a training unit. The opinion organization unit organizes opinions and thoughts of users. The argument construction unit constructs a persuasive argument on the basis of the opinions and ideas organized by the opinion organization unit. The training unit performs an online discussion or speech training in public based on the argument constructed by the argument construction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have made it difficult for users to organize their opinions and construct persuasive arguments, leading to concerns about participating in social discussions.

[0005] The system according to the embodiment aims to enable users to organize their opinions, construct persuasive arguments, and participate in social discussions with confidence. [Means for solving the problem]

[0006] The system according to the embodiment includes an opinion organization unit, an argument construction unit, and a training unit. The opinion organization unit organizes the opinions and thoughts of a user. The argument construction unit constructs persuasive arguments based on the opinions and thoughts organized by the opinion organization unit. The training unit provides training for online discussions and public speeches based on the arguments constructed by the argument construction unit. [Effects of the Invention]

[0007] Systems according to embodiments can enable users to organize their opinions, construct persuasive arguments, and participate in social discussions with confidence. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The voice booster AI according to an embodiment of the present invention is a support tool for users to express their opinions clearly and gain confidence in participating in social discussions, thereby helping users organize their opinions, construct persuasive arguments, and train for online discussions and public speaking.

[0029] A voice booster AI according to an embodiment includes an opinion organizer, an argument constructor, and a training unit. The opinion organizer organizes a user's opinions and thoughts. For example, if a user inputs, "I would like to express my opinion on environmental protection," the generation AI collects information related to that topic and organizes the user's opinions. The generation AI can summarize and categorize the user's opinions using, for example, keyword extraction technology. The argument constructor constructs persuasive arguments based on the opinions and thoughts organized by the opinion organizer. For example, the generation AI receives an instruction such as, "Please list three points about the importance of environmental protection." The generation AI generates arguments such as, "1. Environmental protection is necessary to protect the future of the Earth. 2. Environmental protection is important for maintaining ecosystem balance. 3. Environmental protection is essential for protecting people's health." The training unit trains the user for online discussions and public speeches based on the arguments constructed by the argument constructor. For example, the generation AI receives the instruction, "Please teach me how to speak at the appropriate time as the discussion progresses," and generates a training program such as, "1. Listen carefully to what other participants say, and speak when the topic changes. 2. Summarize your opinion concisely and speak. 3. Provide constructive feedback to the opinions of other participants." This enables the voice booster AI according to the embodiment to support users in clearly expressing their opinions and gaining confidence in participating in social discussions.

[0030] The opinion organization unit can analyze the user's past statement history and provide feedback to construct a consistent argument. For example, the generation AI collects the user's past statement history and evaluates the consistency and logic of the statements. For example, it analyzes past discussion logs and identifies inconsistencies and points that need to be emphasized. The opinion organization unit also provides feedback to construct a consistent argument based on the user's statement history. For example, the generation AI analyzes the user's past statements and provides advice to maintain logical consistency. This provides feedback to construct a consistent argument based on the user's past statement history.

[0031] The argument construction unit can automatically generate counterarguments to a user's opinion and train the user to think of counterarguments to those counterarguments. For example, in the argument construction unit, the generation AI automatically generates counterarguments to a user's opinion and trains the user to think of counterarguments to those counterarguments. For example, if a user states, "Environmental protection is important," the generation AI presents a counterargument such as, "It could hinder economic growth." The argument construction unit also trains the user to think of counterarguments to the counterarguments presented. For example, the generation AI prompts the user to think of a counterargument to the counterargument, "It could hinder economic growth," and helps the user construct a counterargument such as, "Environmental protection and economic growth can be achieved simultaneously." In this way, the user's argumentation skills improve as they train themselves to deal with counterarguments.

[0032] The training unit can analyze the timing of a user's speech in real time and suggest the optimal timing to speak. For example, the generation AI in the training unit analyzes the timing of a user's speech in real time and suggests the optimal timing to speak. For example, it may advise the user to speak just before another participant finishes speaking. The training unit also provides training to improve the user's timing to speak. For example, the generation AI may advise the user to "listen carefully to what other participants say, and speak when the topic changes." This provides training to the user to speak at the optimal timing.

[0033] The training department can evaluate the content of what the user says in real time and provide immediate feedback on areas for improvement. For example, the generation AI in the training department evaluates the content of what the user says in real time and provides immediate feedback on areas for improvement. For example, it evaluates the logic and clarity of the statement and presents specific areas for improvement. The training department also provides feedback on areas for improvement based on the content of what the user says. For example, the generation AI may give advice to the user such as, "The content of your statement is vague, so please explain it with a specific example." This provides real-time feedback on the content of what the user says.

[0034] The training department can provide training programs that are compatible with different discussion formats. For example, the generation AI provides training programs that are compatible with different discussion formats (e.g., panel discussions, debates). For example, the generation AI provides a training program that is compatible with panel discussions, simulating dialogue with multiple panelists and practicing the timing and content of speech. The training department also provides training programs that are compatible with debate-style discussions. For example, the generation AI teaches the user how to speak at the appropriate time as the debate progresses. This allows the user to receive training that is compatible with different discussion formats.

[0035] The training department can provide a function that records what the user says and plays it back later for self-evaluation. For example, the training department provides a function that allows the generation AI to record what the user says and play it back later for self-evaluation. For example, after a discussion is over, the user's own comments can be played back to check for areas for improvement. The training department also provides feedback for the user to use in self-evaluation. For example, the generation AI can give the user advice such as "The content of your comment is vague, so please explain it with specific examples." This allows the user to play back their own comments and perform self-evaluation.

[0036] The training unit can analyze the user's speech in real time and provide immediate feedback on areas for improvement. For example, the generation AI in the training unit analyzes the user's speech in real time and provides immediate feedback on areas for improvement. For example, it evaluates the structure and logic of the speech and suggests specific areas for improvement. The training unit also provides feedback on areas for improvement based on the content of the user's speech. For example, the generation AI may advise the user, "The content of your speech is vague, so please explain it with specific examples." This provides real-time feedback on the user's speech.

[0037] The training unit can visualize the content of the user's speech and generate slides and graphs that are visually easy to understand. In the training unit, for example, the generation AI visualizes the content of the user's speech and generates slides and graphs that are visually easy to understand. For example, it automatically generates slides that show the main points of the speech. The training unit also generates graphs that are visually easy to understand based on the content of the user's speech. For example, the generation AI creates bar graphs and pie charts based on the speech data to convey information visually. This makes the content of the user's speech easier to understand visually.

[0038] The training unit can provide training programs that are compatible with different speech formats. For example, the training unit provides training programs that the generation AI is compatible with different speech formats (e.g., presentations, impromptu speeches). For example, the generation AI provides a training program that is compatible with presentations, practices the presentation using slides, and adjusts the timing and content of the presentation. The training unit also provides a training program in the form of an impromptu speech. For example, the generation AI teaches the user how to speak at the appropriate time as the impromptu speech progresses. This allows the user to receive training that is compatible with different speech formats.

[0039] The training unit can provide a function that allows the user to record a speech and play it back later for self-evaluation. For example, the training unit provides a function that allows the generation AI to record a speech and play it back later for self-evaluation. For example, after a speech is finished, the user can play back what they said and check for areas for improvement. The training unit also provides feedback for the user to use in self-evaluation. For example, the generation AI can give the user advice such as, "The content of your speech is vague, so please explain it with specific examples." This allows the user to play back their own speech and perform self-evaluation.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] Voice Booster AI is a support tool for organizing users' opinions and constructing persuasive arguments, but it can also add a function to visually express users' opinions. For example, the opinion organization module can display and visually organize users' opinions in mind map format. The argument construction module can also convert users' opinions into graphs and charts to make them more visually persuasive. Furthermore, the training module can provide training for users to use visual materials in presentations. This allows users to communicate effectively by incorporating visual elements.

[0042] Voice Booster AI automatically generates counterarguments to a user's opinion and trains the user to think of counterarguments. It can also provide training to help users understand opinions from different perspectives. For example, the argument construction unit automatically generates supporting arguments as well as counterarguments to the user's opinion, helping the user understand the argument from multiple perspectives. The training unit also provides training to help users understand opinions from different perspectives and provide appropriate counterarguments or support. This allows users to understand arguments from multiple perspectives and engage in deeper discussions.

[0043] Voice Booster AI analyzes the timing of a user's speech in real time and suggests optimal timing. It can also evaluate the content and structure of a user's speech in real time and provide immediate feedback on areas for improvement. For example, the training department evaluates the logic and clarity of a user's speech and suggests specific areas for improvement. It also evaluates the structure of a user's speech and suggests more effective structures. This allows users to improve the content and structure of their speech in real time and communicate more effectively.

[0044] Voice Booster AI evaluates the content of a user's speech in real time and provides immediate feedback on areas for improvement. It can also evaluate the pace and rhythm of a user's speech in real time and suggest optimal pace and rhythm. For example, if a user's speech pace is too fast, the training department will advise them to speak more slowly. Also, if the rhythm of their speech is monotonous, it will advise them to add variety. This allows users to adjust the pace and rhythm of their speech and communicate more effectively.

[0045] Voice Booster AI not only provides training programs that correspond to different discussion formats, but can also provide training programs that correspond to different cultures and languages. For example, the training department provides training programs that teach communication styles and manners in different cultures. It also provides training programs that correspond to discussions in different languages, helping users improve their multilingual communication skills. This allows users to communicate effectively in different cultures and languages.

[0046] Voice Booster AI provides a function to record a user's speech and play it back later for self-evaluation, but it can also provide a function to convert the user's speech into text and perform text-based self-evaluation. For example, the training department automatically converts the user's speech into text and provides feedback for text-based self-evaluation. The user can also edit the text and check for areas for improvement. This allows the user to check the content of their speech in text and perform self-evaluation.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The opinion organizer organizes the user's opinions and thoughts. For example, if a user inputs, "I would like to express my opinion on environmental protection," the generation AI collects information related to that topic and organizes the user's opinions. The generation AI can use keyword extraction technology to summarize and categorize the user's opinions. Step 2: The argument construction unit constructs persuasive arguments based on the opinions and ideas organized by the opinion organization unit. For example, the generation AI receives the instruction, "Please list three points about the importance of environmental protection," and generates arguments such as, "1. Environmental protection is necessary to protect the future of the Earth. 2. Environmental protection is important for maintaining the balance of the ecosystem. 3. Environmental protection is essential for protecting people's health." Step 3: The training unit trains participants for online discussions and public speeches based on the arguments constructed by the argument construction unit. For example, the generation AI receives the instruction, "Please teach me how to speak at the appropriate time as the discussion progresses," and generates a training program such as, "1. Listen carefully to what other participants say, and speak when the topic changes. 2. Summarize your opinion concisely. 3. Provide constructive feedback on the opinions of other participants."

[0049] (Example 2) The voice booster AI according to an embodiment of the present invention is a support tool for users to express their opinions clearly and gain confidence in participating in social discussions, thereby helping users organize their opinions, construct persuasive arguments, and train for online discussions and public speaking.

[0050] A voice booster AI according to an embodiment includes an opinion organizer, an argument constructor, and a training unit. The opinion organizer organizes a user's opinions and thoughts. For example, if a user inputs, "I would like to express my opinion on environmental protection," the generation AI collects information related to that topic and organizes the user's opinions. The generation AI can summarize and categorize the user's opinions using, for example, keyword extraction technology. The argument constructor constructs persuasive arguments based on the opinions and thoughts organized by the opinion organizer. For example, the generation AI receives an instruction such as, "Please list three points about the importance of environmental protection." The generation AI generates arguments such as, "1. Environmental protection is necessary to protect the future of the Earth. 2. Environmental protection is important for maintaining ecosystem balance. 3. Environmental protection is essential for protecting people's health." The training unit trains the user for online discussions and public speeches based on the arguments constructed by the argument constructor. For example, the generation AI receives the instruction, "Please teach me how to speak at the appropriate time as the discussion progresses," and generates a training program such as, "1. Listen carefully to what other participants say, and speak when the topic changes. 2. Summarize your opinion concisely and speak. 3. Provide constructive feedback to the opinions of other participants." This enables the voice booster AI according to the embodiment to support users in clearly expressing their opinions and gaining confidence in participating in social discussions.

[0051] The opinion organization unit can analyze the user's past statement history and provide feedback to construct a consistent argument. For example, the generation AI collects the user's past statement history and evaluates the consistency and logic of the statements. For example, it analyzes past discussion logs and identifies inconsistencies and points that need to be emphasized. The opinion organization unit also provides feedback to construct a consistent argument based on the user's statement history. For example, the generation AI analyzes the user's past statements and provides advice to maintain logical consistency. This provides feedback to construct a consistent argument based on the user's past statement history.

[0052] The argument construction unit can automatically generate counterarguments to a user's opinion and train the user to think of counterarguments to those counterarguments. For example, in the argument construction unit, the generation AI automatically generates counterarguments to a user's opinion and trains the user to think of counterarguments to those counterarguments. For example, if a user states, "Environmental protection is important," the generation AI presents a counterargument such as, "It could hinder economic growth." The argument construction unit also trains the user to think of counterarguments to the counterarguments presented. For example, the generation AI prompts the user to think of a counterargument to the counterargument, "It could hinder economic growth," and helps the user construct a counterargument such as, "Environmental protection and economic growth can be achieved simultaneously." In this way, the user's argumentation skills improve as they train themselves to deal with counterarguments.

[0053] The argument construction unit uses the emotion estimation function to adjust arguments according to the user's emotional state and propose more persuasive expressions. The argument construction unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and adjust the arguments. For example, if the user is nervous, the generation AI proposes expressions in a relaxed tone. The argument construction unit also adjusts the content and expression method of the arguments according to the user's emotional state. For example, if the user is feeling angry, the generation AI proposes expressions in a calm tone and advises the user to avoid emotional expressions. This allows the arguments to be adjusted according to the user's emotional state, enabling more persuasive expressions.

[0054] The training unit can analyze the timing of a user's speech in real time and suggest the optimal timing to speak. For example, the generation AI in the training unit analyzes the timing of a user's speech in real time and suggests the optimal timing to speak. For example, it may advise the user to speak just before another participant finishes speaking. The training unit also provides training to improve the user's timing to speak. For example, the generation AI may advise the user to "listen carefully to what other participants say, and speak when the topic changes." This provides training to the user to speak at the optimal timing.

[0055] The training department can evaluate the content of what the user says in real time and provide immediate feedback on areas for improvement. For example, the generation AI in the training department evaluates the content of what the user says in real time and provides immediate feedback on areas for improvement. For example, it evaluates the logic and clarity of the statement and presents specific areas for improvement. The training department also provides feedback on areas for improvement based on the content of what the user says. For example, the generation AI may give advice to the user such as, "The content of your statement is vague, so please explain it with a specific example." This provides real-time feedback on the content of what the user says.

[0056] The training unit can use the emotion estimation function to train the system to adjust the tone and expression of speech according to the user's emotional state. For example, the training unit uses the emotion estimation function to analyze the user's emotional state in real time and train the system to adjust the tone and expression of speech. For example, if the user is nervous, the system may suggest speaking in a relaxed tone. The training unit also trains the system to adjust the tone and expression of speech according to the user's emotional state. For example, if the user is feeling angry, the generation AI may suggest speaking in a calm tone and advise the user to avoid emotional expressions. In this way, the tone and expression of speech are adjusted according to the user's emotional state.

[0057] The training department can provide training programs that are compatible with different discussion formats. For example, the generation AI provides training programs that are compatible with different discussion formats (e.g., panel discussions, debates). For example, the generation AI provides a training program that is compatible with panel discussions, simulating dialogue with multiple panelists and practicing the timing and content of speech. The training department also provides training programs that are compatible with debate-style discussions. For example, the generation AI teaches the user how to speak at the appropriate time as the debate progresses. This allows the user to receive training that is compatible with different discussion formats.

[0058] The training department can provide a function that records what the user says and plays it back later for self-evaluation. For example, the training department provides a function that allows the generation AI to record what the user says and play it back later for self-evaluation. For example, after a discussion is over, the user's own comments can be played back to check for areas for improvement. The training department also provides feedback for the user to use in self-evaluation. For example, the generation AI can give the user advice such as "The content of your comment is vague, so please explain it with specific examples." This allows the user to play back their own comments and perform self-evaluation.

[0059] The training unit can use the emotion estimation function to identify an environment in which the user can speak most relaxedly and recommend training in that environment. The training unit, for example, uses the emotion estimation function to identify an environment in which the user can speak most relaxedly. For example, it analyzes the user's heart rate and facial expressions to evaluate the level of relaxation. The training unit also recommends training in an environment in which the user can speak relaxedly. For example, the generation AI gives the user advice such as "practice speaking in a quiet place." This allows the user to train in an environment in which they can speak relaxedly.

[0060] The training unit can analyze the user's speech in real time and provide immediate feedback on areas for improvement. For example, the generation AI in the training unit analyzes the user's speech in real time and provides immediate feedback on areas for improvement. For example, it evaluates the structure and logic of the speech and suggests specific areas for improvement. The training unit also provides feedback on areas for improvement based on the content of the user's speech. For example, the generation AI may advise the user, "The content of your speech is vague, so please explain it with specific examples." This provides real-time feedback on the user's speech.

[0061] The training unit can visualize the content of the user's speech and generate slides and graphs that are visually easy to understand. In the training unit, for example, the generation AI visualizes the content of the user's speech and generates slides and graphs that are visually easy to understand. For example, it automatically generates slides that show the main points of the speech. The training unit also generates graphs that are visually easy to understand based on the content of the user's speech. For example, the generation AI creates bar graphs and pie charts based on the speech data to convey information visually. This makes the content of the user's speech easier to understand visually.

[0062] The training unit can use the emotion estimation function to train the system to adjust the tone and pace of speech according to the user's emotional state. For example, the training unit uses the emotion estimation function to analyze the user's emotional state in real time and train the system to adjust the tone and pace of speech. For example, if the user is nervous, the training unit suggests speaking at a slower pace. The training unit also trains the system to adjust the tone and pace of speech according to the user's emotional state. For example, if the user is feeling angry, the generation AI suggests speaking in a calm tone and advises the user to avoid emotional expressions. This allows the tone and pace of speech to be adjusted according to the user's emotional state.

[0063] The training unit can provide training programs that are compatible with different speech formats. For example, the training unit provides training programs that the generation AI is compatible with different speech formats (e.g., presentations, impromptu speeches). For example, the generation AI provides a training program that is compatible with presentations, practices the presentation using slides, and adjusts the timing and content of the presentation. The training unit also provides a training program in the form of an impromptu speech. For example, the generation AI teaches the user how to speak at the appropriate time as the impromptu speech progresses. This allows the user to receive training that is compatible with different speech formats.

[0064] The training unit can provide a function that allows the user to record a speech and play it back later for self-evaluation. For example, the training unit provides a function that allows the generation AI to record a speech and play it back later for self-evaluation. For example, after a speech is finished, the user can play back what they said and check for areas for improvement. The training unit also provides feedback for the user to use in self-evaluation. For example, the generation AI can give the user advice such as, "The content of your speech is vague, so please explain it with specific examples." This allows the user to play back their own speech and perform self-evaluation.

[0065] The training unit can use the emotion estimation function to identify speech patterns that give the user the most confidence and provide advice based on those patterns. The training unit, for example, uses the emotion estimation function to identify speech patterns that give the user the most confidence. For example, it analyzes past speech history and extracts patterns of successful speeches. The training unit also provides advice based on speech patterns that give the user confidence. For example, the generation AI advises the user, "Based on patterns of successful speeches in the past, give a speech with a similar structure." This allows advice based on speech patterns that give the user confidence.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] Voice Booster AI is a support tool for organizing users' opinions and constructing persuasive arguments, but it can also add a function to visually express users' opinions. For example, the opinion organization module can display and visually organize users' opinions in mind map format. The argument construction module can also convert users' opinions into graphs and charts to make them more visually persuasive. Furthermore, the training module can provide training for users to use visual materials in presentations. This allows users to communicate effectively by incorporating visual elements.

[0068] Voice Booster AI analyzes the user's past speech history and provides feedback to help them construct consistent arguments. It can also analyze the user's speech tone and style to provide personalized advice. For example, the opinion organizer analyzes the user's speech tone (e.g., aggressive, calm, emotional) and recommends speaking in an appropriate tone. The argument constructor analyzes the user's speech style (e.g., logical, emotional, fact-oriented) and helps them construct arguments in the most appropriate style. This allows users to communicate effectively according to their speech style.

[0069] Voice Booster AI automatically generates counterarguments to a user's opinion and trains the user to think of counterarguments. It can also provide training to help users understand opinions from different perspectives. For example, the argument construction unit automatically generates supporting arguments as well as counterarguments to the user's opinion, helping the user understand the argument from multiple perspectives. The training unit also provides training to help users understand opinions from different perspectives and provide appropriate counterarguments or support. This allows users to understand arguments from multiple perspectives and engage in deeper discussions.

[0070] Voice Booster AI uses its emotion estimation function to adjust arguments according to the user's emotional state, and can also provide feedback according to the user's emotional state. For example, the argument construction module provides advice on how to relax if the user is tense, or advice on how to stay calm if the user is angry. Furthermore, the training module provides training programs according to the user's emotional state and helps improve emotion control skills. This allows users to communicate effectively while controlling their emotions.

[0071] Voice Booster AI analyzes the timing of a user's speech in real time and suggests optimal timing. It can also evaluate the content and structure of a user's speech in real time and provide immediate feedback on areas for improvement. For example, the training department evaluates the logic and clarity of a user's speech and suggests specific areas for improvement. It also evaluates the structure of a user's speech and suggests more effective structures. This allows users to improve the content and structure of their speech in real time and communicate more effectively.

[0072] Voice Booster AI evaluates the content of a user's speech in real time and provides immediate feedback on areas for improvement. It can also evaluate the pace and rhythm of a user's speech in real time and suggest optimal pace and rhythm. For example, if a user's speech pace is too fast, the training department will advise them to speak more slowly. Also, if the rhythm of their speech is monotonous, it will advise them to add variety. This allows users to adjust the pace and rhythm of their speech and communicate more effectively.

[0073] Voice Booster AI uses its emotion estimation function to train itself to adjust the tone and expression of speech according to the user's emotional state, but it can also train itself to adjust the content and structure of speech according to the user's emotional state. For example, if the user is nervous, the training module will suggest concise and clear content. On the other hand, if the user is angry, it will suggest calm and logical content. This allows users to adjust the content and structure of speech according to their emotional state and communicate more effectively.

[0074] Voice Booster AI not only provides training programs that correspond to different discussion formats, but can also provide training programs that correspond to different cultures and languages. For example, the training department provides training programs that teach communication styles and manners in different cultures. It also provides training programs that correspond to discussions in different languages, helping users improve their multilingual communication skills. This allows users to communicate effectively in different cultures and languages.

[0075] Voice Booster AI provides a function to record a user's speech and play it back later for self-evaluation, but it can also provide a function to convert the user's speech into text and perform text-based self-evaluation. For example, the training department automatically converts the user's speech into text and provides feedback for text-based self-evaluation. The user can also edit the text and check for areas for improvement. This allows the user to check the content of their speech in text and perform self-evaluation.

[0076] Voice Booster AI uses its emotion estimation function to identify the environment in which the user can speak most relaxed and recommend training in that environment. It can also identify the environment in which the user can concentrate best and recommend training in that environment. For example, the training module evaluates the user's level of concentration and identifies the environment in which the user can concentrate best. It also recommends training in an environment where the user can concentrate, providing support for effective training. This allows the user to train in an environment where they can concentrate and improve their effective communication skills.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The opinion organizer organizes the user's opinions and thoughts. For example, if a user inputs, "I would like to express my opinion on environmental protection," the generation AI collects information related to that topic and organizes the user's opinions. The generation AI can use keyword extraction technology to summarize and categorize the user's opinions. Step 2: The argument construction unit constructs persuasive arguments based on the opinions and ideas organized by the opinion organization unit. For example, the generation AI receives the instruction, "Please list three points about the importance of environmental protection," and generates arguments such as, "1. Environmental protection is necessary to protect the future of the Earth. 2. Environmental protection is important for maintaining the balance of the ecosystem. 3. Environmental protection is essential for protecting people's health." Step 3: The training unit trains participants for online discussions and public speeches based on the arguments constructed by the argument construction unit. For example, the generation AI receives the instruction, "Please teach me how to speak at the appropriate time as the discussion progresses," and generates a training program such as, "1. Listen carefully to what other participants say, and speak when the topic changes. 2. Summarize your opinion concisely. 3. Provide constructive feedback on the opinions of other participants."

[0079] 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.

[0080] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0081] 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.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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).

[0088] 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.

[0089] 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.

[0090] 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.

[0091] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0092] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0093] 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.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] 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.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 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.

[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 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.

[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. 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.

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 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.

[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 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.

[0111] 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.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 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.

[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 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).

[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] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0133] 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."

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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]

[0146] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an opinion organizing unit that organizes user opinions and thoughts; an argument construction unit that constructs persuasive arguments based on the opinions and ideas organized by the opinion organization unit; a training unit that performs training for online discussions and public speeches based on the arguments constructed by the argument construction unit. A system characterized by:

2. The opinion organizing section Analyze the user's past utterances and provide feedback to help them build coherent arguments 2. The system of claim 1.

3. The argument constructor: Automatically generate counterarguments to user opinions, and train users to think of counterarguments to those opinions.

2. The system of claim 1.

4. The training section Analyzes the timing of user speech in real time and suggests optimal timing for speech 2. The system of claim 1.

5. The training section Offer training programs that accommodate different discussion formats 2. The system of claim 1.

6. The training section Analyzes user speech in real time and provides immediate feedback on improvements 2. The system of claim 1.

7. The argument constructor: Using emotion estimation, the system adjusts arguments according to the user's emotional state and suggests more persuasive expressions.

2. The system of claim 1.

8. The training section Using emotion estimation, we identify the environment in which the user feels most comfortable speaking and recommend training in that environment.

2. The system of claim 1.

Images