System

Abstract

An object of the system according to the embodiment is to support practice and improvement of a presentation and relieve tension.SOLUTION: A system includes a rehearsal unit, an analysis unit, a proposal unit, and an analysis unit. The rehearsal unit rehearses contents spoken by the user. The analysis unit analyzes the content spoken by the user rehearsed by the rehearsal unit. The proposal unit proposes an improvement point on the basis of the content of the speech of the user analyzed by the analysis unit. The analysis unit analyzes the user's heart rate and vocal tremor in real time.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] Previous technology had the drawback of not allowing enough time for presentation practice and improvement, and lacking support to ease nervousness.

[0005] The system according to the embodiment aims to help you practice and improve your presentation and reduce your nervousness. [Means for solving the problem]

[0006] The system according to the embodiment includes a rehearsal unit, an analysis unit, a suggestion unit, and an analysis unit. The rehearsal unit rehearses what the user will say. The analysis unit analyzes what the user will say rehearsed by the rehearsal unit. The suggestion unit suggests improvements based on what the user will say analyzed by the analysis unit. The analysis unit analyzes the user's heart rate and voice tremors in real time. [Effects of the Invention]

[0007] The system according to the embodiment can help you practice and improve your presentation and reduce your stress. [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 PresentMate AI system according to an embodiment of the present invention automatically rehearses and analyzes what a user is saying, suggests improvements, and analyzes heart rate and vocal tremors in real time, thereby helping users improve their presentation skills and become more confident in public speaking.

[0029] The PresentMate AI system according to the embodiment includes a rehearsal unit, an analysis unit, a suggestion unit, and an analysis unit. The rehearsal unit rehearses what a user will say. For example, when a user speaks a presentation, the generation AI rehearses that content. The generation AI analyzes what the user says and provides advice on how to improve their language, tone of voice, and non-verbal communication. The input to the generation AI is audio data of what the user says, and the generation AI analyzes and provides advice based on that audio data. The analysis unit analyzes what the user says rehearsed by the rehearsal unit. For example, the generation AI analyzes what the user says and suggests how to improve appropriate language, tone of voice, and non-verbal communication. The input to the generation AI is audio data of what the user says, and the generation AI analyzes and provides advice based on that audio data. The suggestion unit suggests how to improve based on what the user says analyzed by the analysis unit. For example, if a user frequently uses words like "um" and "so," the generation AI provides advice such as, "If you reduce these words, you will give a more professional impression." The input to the generation AI is the voice data of what the user is saying, and the generation AI analyzes and provides advice based on that voice data. The analysis unit analyzes the user's heart rate and voice tremors in real time. For example, if the user becomes nervous and their heart rate increases or their voice trembles, the generation AI analyzes that data and suggests breathing techniques and relaxation techniques to help them relax. The input to the generation AI is the user's heart rate data and voice data, and the generation AI analyzes and provides advice based on that data. As a result, the Presenmate AI system according to the embodiment can improve the user's presentation skills and support them in speaking in public with confidence.

[0030] The suggestion unit evaluates the structure and logical development of what the user says and can propose a more effective presentation structure. For example, the suggestion unit uses a generation AI to analyze what the user says and evaluate the structure and logical development of the talk. For example, it divides what the user says into paragraphs, checks the logical connections between each paragraph, and suggests areas for improvement. This evaluates the structure and logical development of what the user says and proposes a more effective presentation structure, improving the quality of the presentation.

[0031] The suggestion unit can learn from a user's past presentation data and provide advice optimized for each individual user. For example, the suggestion unit uses a generation AI to learn from a user's past presentation data and analyze speaking habits and patterns. For example, if a user frequently uses a particular phrase, the suggestion unit can suggest reducing the use of that phrase. This improves the quality of presentations by learning from a user's past presentation data and providing advice optimized for each individual user.

[0032] The suggestion unit can also handle presentations in different languages ​​and provide advice in multiple languages. For example, the generative AI analyzes what the user says and can handle presentations in different languages. For example, it can translate a presentation in English into Japanese and suggest appropriate wording and tone. This allows the system to handle presentations in different languages ​​and provide advice in multiple languages, thereby improving the quality of the presentation.

[0033] The suggestion unit can visualize what the user says and automatically create slides and graphs. For example, the suggestion unit uses a generation AI to analyze what the user says and automatically create slides and graphs based on that content. For example, the main points of the presentation can be displayed in bullet points on the slides. This improves the quality of presentations by visualizing what the user says and automatically creating slides and graphs.

[0034] The analysis unit analyzes not only heart rate and voice tremors, but also electrodermal activity and breathing patterns to suggest more detailed relaxation techniques. For example, the generative AI analyzes a user's electrodermal activity in addition to their heart rate and voice tremors to suggest relaxation techniques. For example, if electrodermal activity is high, it may suggest deep breathing or meditation. This allows the analysis of not only heart rate and voice tremors, but also electrodermal activity and breathing patterns to suggest more detailed relaxation techniques, helping to relieve tension during presentations.

[0035] The analysis unit can learn the user's past biometric data and provide relaxation techniques optimized for each individual user. For example, the analysis unit's generative AI learns the user's past heart rate and voice tremor data and suggests relaxation techniques optimized for each individual user. For example, it re-suggests relaxation methods that have been effective in the past. In this way, by learning the user's past biometric data and providing relaxation techniques optimized for each individual user, tension during a presentation can be alleviated.

[0036] The suggestion unit can analyze the user's language and provide advice that incorporates industry-specific terminology and trending words. For example, the suggestion unit uses a generative AI to analyze what the user says and provide advice that incorporates industry-specific terminology. For example, in an IT industry presentation, the suggestion unit suggests the appropriate use of terms such as "cloud computing" and "big data." This improves the quality of presentations by analyzing the user's language and providing advice that incorporates industry-specific terminology and trending words.

[0037] The suggestion unit can learn from a user's past utterance data and provide advice on improving language that is optimized for each individual user. For example, the suggestion unit's generation AI learns from a user's past utterance data and analyzes speaking habits and patterns. For example, if a user frequently uses a particular phrase, it can suggest reducing the use of that phrase. In this way, the system learns from a user's past utterance data and provides advice on improving language that is optimized for each individual user, thereby improving the quality of presentations.

[0038] The suggestion unit can analyze the tone of the user's voice and provide more detailed advice on improving the tone, taking into account the frequency components and rhythm of the voice. For example, the suggestion unit uses a generation AI to analyze the tone of the user's voice and provide advice that takes into account the frequency components of the voice. For example, if there are a lot of low-frequency components, the suggestion unit may suggest speaking in a slightly higher voice. This improves the quality of presentations by analyzing the tone of the user's voice and providing more detailed advice on improving the tone, taking into account the frequency components and rhythm of the voice.

[0039] The suggestion unit can learn from a user's past voice data and provide voice tone improvement advice optimized for each individual user. For example, the suggestion unit uses a generation AI to learn from a user's past voice data and analyze speaking habits and patterns. For example, if a user speaks in a specific tone, the suggestion unit suggests improving that tone. This improves the quality of presentations by learning from a user's past voice data and providing voice tone improvement advice optimized for each individual user.

[0040] The suggestion unit can learn from a user's past voice data and provide voice tone improvement advice optimized for each individual user. For example, the suggestion unit uses a generation AI to learn from a user's past voice data and analyze speaking habits and patterns. For example, if a user speaks in a specific tone, the suggestion unit suggests improving that tone. This improves the quality of presentations by learning from a user's past voice data and providing voice tone improvement advice optimized for each individual user.

[0041] The suggestion unit analyzes the user's non-verbal communication and takes into account posture and eye movements to provide more detailed advice for improvement. For example, the suggestion unit uses a generation AI to analyze the user's non-verbal communication and provide advice that takes posture into account. For example, it may suggest speaking with your back straight. This improves the quality of presentations by analyzing the user's non-verbal communication and taking into account posture and eye movements to provide more detailed advice for improvement.

[0042] The suggestion unit can learn from the user's past video data and provide non-verbal communication improvement advice optimized for each individual user. For example, the suggestion unit uses a generation AI to learn from the user's past video data and analyze non-verbal communication habits and patterns. For example, if a user frequently uses a particular gesture, the suggestion unit suggests improving that gesture. This improves the quality of presentations by learning from the user's past video data and providing non-verbal communication improvement advice optimized for each individual user.

[0043] The suggestion unit can apply improvements in non-verbal communication to training for interviews and negotiations, thereby improving communication skills in all business situations. For example, the suggestion unit uses a generative AI to analyze the user's non-verbal communication and apply that data to interview and negotiation training. For example, it can suggest appropriate gestures and facial expressions for an interview. This allows improvements in non-verbal communication to be applied to training for interviews and negotiations, improving communication skills in all business situations, and improving the quality of presentations.

[0044] The suggestion unit can analyze the user's nonverbal communication and provide an animation guide that suggests appropriate gestures and facial expressions. For example, the suggestion unit uses a generation AI to analyze the user's nonverbal communication and provides an animation guide that suggests appropriate gestures and facial expressions based on that data. For example, hand movements and facial expressions are shown as animations. This improves the quality of presentations by analyzing the user's nonverbal communication and providing an animation guide that suggests appropriate gestures and facial expressions.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The PresenMate AI system can also be equipped with a grammar checker that automatically converts recordings of users' presentations into text and checks for grammar and spelling errors based on the text data. For example, by converting what a user says into text in real time and pointing out grammatical and spelling errors, the accuracy of the presentation can be improved. The grammar checker can also analyze what the user says and suggest more appropriate expressions and phrases. This allows users to give more accurate and sophisticated presentations.

[0047] The PresenMate AI system can also be equipped with a reference material provider that automatically searches for and provides relevant reference materials and literature based on the content of a user's presentation. For example, presenting the latest research papers and statistical data related to what the user is talking about can increase the credibility and persuasiveness of the presentation. The reference material provider can also easily search for and instantly display information that the user wants to cite during the presentation. This allows users to make presentations based on more information.

[0048] The PresenMate AI system can also be equipped with a virtual audience unit that simulates a virtual audience to help users practice their presentations. For example, the virtual audience unit can provide an environment that is similar to a real presentation by having the virtual audience react in real time as the user gives a presentation. The virtual audience unit can also change the audience's facial expressions and attitudes depending on the content and tone of the user's speech. This allows users to practice more practically.

[0049] The PresenMate AI system can also include a visual feedback unit that provides visual feedback based on the content of a user's presentation. For example, it can automatically generate graphs and charts based on what the user is saying and display them in a visually easy-to-understand manner. The visual feedback unit can also provide real-time feedback based on the user's speaking speed and tone. This allows users to utilize visual information to give more effective presentations.

[0050] The Presenmate AI system can also be equipped with a scenario generation unit that automatically generates a presentation scenario based on the content of the user's presentation. For example, it can automatically generate the flow and structure of the presentation based on what the user will say and suggest it to the user. The scenario generation unit can also suggest the optimal scenario based on the purpose and target audience of the user's presentation. This allows users to create more effective presentation scenarios.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The rehearsal unit rehearses what the user will say. For example, when the user speaks the contents of a presentation, the generation AI rehearses that content. The input to the generation AI is the audio data of what the user will say, and the generation AI rehearses based on that audio data. Step 2: The analysis unit analyzes the user's speech, which has been rehearsed by the rehearsal unit. For example, the generation AI analyzes what the user says and suggests appropriate language, tone of voice, and areas for improvement in non-verbal communication. The input to the generation AI is audio data of what the user says, and the generation AI performs analysis based on that audio data. Step 3: The suggestion unit suggests improvements based on the user's speech analyzed by the analysis unit. For example, if the user frequently uses words like "um" and "so," the generation AI will provide advice such as "reducing these words will give a more professional impression." The input to the generation AI is the audio data of the user's speech, and the generation AI makes suggestions based on that audio data. Step 4: The analysis unit analyzes the user's heart rate and voice tremors in real time. For example, if the user becomes nervous and their heart rate increases or their voice trembles, the generation AI analyzes that data and suggests breathing techniques and relaxation techniques to help them relax. The input to the generation AI is the user's heart rate data and voice data, and the generation AI analyzes and makes suggestions based on that data.

[0053] (Example 2) The PresentMate AI system according to an embodiment of the present invention automatically rehearses and analyzes what a user is saying, suggests improvements, and analyzes heart rate and vocal tremors in real time, thereby helping users improve their presentation skills and become more confident in public speaking.

[0054] The PresentMate AI system according to the embodiment includes a rehearsal unit, an analysis unit, a suggestion unit, and an analysis unit. The rehearsal unit rehearses what a user will say. For example, when a user speaks a presentation, the generation AI rehearses that content. The generation AI analyzes what the user says and provides advice on how to improve their language, tone of voice, and non-verbal communication. The input to the generation AI is audio data of what the user says, and the generation AI analyzes and provides advice based on that audio data. The analysis unit analyzes what the user says rehearsed by the rehearsal unit. For example, the generation AI analyzes what the user says and suggests how to improve appropriate language, tone of voice, and non-verbal communication. The input to the generation AI is audio data of what the user says, and the generation AI analyzes and provides advice based on that audio data. The suggestion unit suggests how to improve based on what the user says analyzed by the analysis unit. For example, if a user frequently uses words like "um" and "so," the generation AI provides advice such as, "If you reduce these words, you will give a more professional impression." The input to the generation AI is the voice data of what the user is saying, and the generation AI analyzes and provides advice based on that voice data. The analysis unit analyzes the user's heart rate and voice tremors in real time. For example, if the user becomes nervous and their heart rate increases or their voice trembles, the generation AI analyzes that data and suggests breathing techniques and relaxation techniques to help them relax. The input to the generation AI is the user's heart rate data and voice data, and the generation AI analyzes and provides advice based on that data. As a result, the Presenmate AI system according to the embodiment can improve the user's presentation skills and support them in speaking in public with confidence.

[0055] The suggestion unit evaluates the structure and logical development of what the user says and can propose a more effective presentation structure. For example, the suggestion unit uses a generation AI to analyze what the user says and evaluate the structure and logical development of the talk. For example, it divides what the user says into paragraphs, checks the logical connections between each paragraph, and suggests areas for improvement. This evaluates the structure and logical development of what the user says and proposes a more effective presentation structure, improving the quality of the presentation.

[0056] The suggestion unit can learn from a user's past presentation data and provide advice optimized for each individual user. For example, the suggestion unit uses a generation AI to learn from a user's past presentation data and analyze speaking habits and patterns. For example, if a user frequently uses a particular phrase, the suggestion unit can suggest reducing the use of that phrase. This improves the quality of presentations by learning from a user's past presentation data and providing advice optimized for each individual user.

[0057] The suggestion unit can analyze the user's emotional state using the emotion estimation function and provide advice according to the emotion. For example, the suggestion unit uses the generation AI to analyze what the user says and evaluates the user's emotional state using the emotion estimation function. For example, if the user is nervous, the suggestion unit provides advice to relax. In this way, the quality of the presentation is improved by analyzing the user's emotional state using the emotion estimation function and providing advice according to the emotion.

[0058] The suggestion unit can also handle presentations in different languages ​​and provide advice in multiple languages. For example, the generative AI analyzes what the user says and can handle presentations in different languages. For example, it can translate a presentation in English into Japanese and suggest appropriate wording and tone. This allows the system to handle presentations in different languages ​​and provide advice in multiple languages, thereby improving the quality of the presentation.

[0059] The suggestion unit can visualize what the user says and automatically create slides and graphs. For example, the suggestion unit uses a generation AI to analyze what the user says and automatically create slides and graphs based on that content. For example, the main points of the presentation can be displayed in bullet points on the slides. This improves the quality of presentations by visualizing what the user says and automatically creating slides and graphs.

[0060] The analysis unit analyzes not only heart rate and voice tremors, but also electrodermal activity and breathing patterns to suggest more detailed relaxation techniques. For example, the generative AI analyzes a user's electrodermal activity in addition to their heart rate and voice tremors to suggest relaxation techniques. For example, if electrodermal activity is high, it may suggest deep breathing or meditation. This allows the analysis of not only heart rate and voice tremors, but also electrodermal activity and breathing patterns to suggest more detailed relaxation techniques, helping to relieve tension during presentations.

[0061] The analysis unit can learn the user's past biometric data and provide relaxation techniques optimized for each individual user. For example, the analysis unit's generative AI learns the user's past heart rate and voice tremor data and suggests relaxation techniques optimized for each individual user. For example, it re-suggests relaxation methods that have been effective in the past. In this way, by learning the user's past biometric data and providing relaxation techniques optimized for each individual user, tension during a presentation can be alleviated.

[0062] The analysis unit can use the emotion estimation function to analyze the user's emotional state and suggest relaxation techniques according to the emotion. For example, the generation AI analyzes the user's heart rate and voice tremors, and then uses the emotion estimation function to evaluate the user's emotional state. For example, if the user is nervous, the analysis unit provides advice on how to relax. In this way, the emotion estimation function can be used to analyze the user's emotional state and suggest relaxation techniques according to the emotion, thereby reducing tension during a presentation.

[0063] The suggestion unit can analyze the user's language and provide advice that incorporates industry-specific terminology and trending words. For example, the suggestion unit uses a generative AI to analyze what the user says and provide advice that incorporates industry-specific terminology. For example, in an IT industry presentation, the suggestion unit suggests the appropriate use of terms such as "cloud computing" and "big data." This improves the quality of presentations by analyzing the user's language and providing advice that incorporates industry-specific terminology and trending words.

[0064] The suggestion unit can learn from a user's past utterance data and provide advice on improving language that is optimized for each individual user. For example, the suggestion unit's generation AI learns from a user's past utterance data and analyzes speaking habits and patterns. For example, if a user frequently uses a particular phrase, it can suggest reducing the use of that phrase. In this way, the system learns from a user's past utterance data and provides advice on improving language that is optimized for each individual user, thereby improving the quality of presentations.

[0065] The suggestion unit can analyze the user's emotional state using the emotion estimation function and provide advice on improving language usage according to the emotion. For example, the suggestion unit uses the generation AI to analyze what the user says and evaluates the user's emotional state using the emotion estimation function. For example, if the user is nervous, the suggestion unit provides advice on how to relax. In this way, the quality of the presentation is improved by analyzing the user's emotional state using the emotion estimation function and providing advice on improving language usage according to the emotion.

[0066] The suggestion unit can analyze the tone of the user's voice and provide more detailed advice on improving the tone, taking into account the frequency components and rhythm of the voice. For example, the suggestion unit uses a generation AI to analyze the tone of the user's voice and provide advice that takes into account the frequency components of the voice. For example, if there are a lot of low-frequency components, the suggestion unit may suggest speaking in a slightly higher voice. This improves the quality of presentations by analyzing the tone of the user's voice and providing more detailed advice on improving the tone, taking into account the frequency components and rhythm of the voice.

[0067] The suggestion unit can learn from a user's past voice data and provide voice tone improvement advice optimized for each individual user. For example, the suggestion unit uses a generation AI to learn from a user's past voice data and analyze speaking habits and patterns. For example, if a user speaks in a specific tone, the suggestion unit suggests improving that tone. This improves the quality of presentations by learning from a user's past voice data and providing voice tone improvement advice optimized for each individual user.

[0068] The suggestion unit can learn from a user's past voice data and provide voice tone improvement advice optimized for each individual user. For example, the suggestion unit uses a generation AI to learn from a user's past voice data and analyze speaking habits and patterns. For example, if a user speaks in a specific tone, the suggestion unit suggests improving that tone. This improves the quality of presentations by learning from a user's past voice data and providing voice tone improvement advice optimized for each individual user.

[0069] The suggestion unit can analyze the user's emotional state using the emotion estimation function and provide advice on improving the tone of voice according to the emotion. For example, the suggestion unit uses the generation AI to analyze the user's tone of voice and evaluate the user's emotional state using the emotion estimation function. For example, if the user is nervous, the suggestion unit provides advice on relaxing. In this way, the quality of the presentation is improved by analyzing the user's emotional state using the emotion estimation function and providing advice on improving the tone of voice according to the emotion.

[0070] The suggestion unit analyzes the user's non-verbal communication and takes into account posture and eye movements to provide more detailed advice for improvement. For example, the suggestion unit uses a generation AI to analyze the user's non-verbal communication and provide advice that takes posture into account. For example, it may suggest speaking with your back straight. This improves the quality of presentations by analyzing the user's non-verbal communication and taking into account posture and eye movements to provide more detailed advice for improvement.

[0071] The suggestion unit can learn from the user's past video data and provide non-verbal communication improvement advice optimized for each individual user. For example, the suggestion unit uses a generation AI to learn from the user's past video data and analyze non-verbal communication habits and patterns. For example, if a user frequently uses a particular gesture, the suggestion unit suggests improving that gesture. This improves the quality of presentations by learning from the user's past video data and providing non-verbal communication improvement advice optimized for each individual user.

[0072] The suggestion unit can analyze the user's emotional state using the emotion estimation function and provide advice to improve nonverbal communication according to the emotion. For example, the suggestion unit uses the generation AI to analyze the user's nonverbal communication and evaluates the user's emotional state using the emotion estimation function. For example, if the user is nervous, the suggestion unit provides advice to relax. In this way, the quality of the presentation is improved by analyzing the user's emotional state using the emotion estimation function and providing advice to improve nonverbal communication according to the emotion.

[0073] The suggestion unit can apply improvements in non-verbal communication to training for interviews and negotiations, thereby improving communication skills in all business situations. For example, the suggestion unit uses a generative AI to analyze the user's non-verbal communication and apply that data to interview and negotiation training. For example, it can suggest appropriate gestures and facial expressions for an interview. This allows improvements in non-verbal communication to be applied to training for interviews and negotiations, improving communication skills in all business situations, and improving the quality of presentations.

[0074] The suggestion unit can analyze the user's nonverbal communication and provide an animation guide that suggests appropriate gestures and facial expressions. For example, the suggestion unit uses a generation AI to analyze the user's nonverbal communication and provides an animation guide that suggests appropriate gestures and facial expressions based on that data. For example, hand movements and facial expressions are shown as animations. This improves the quality of presentations by analyzing the user's nonverbal communication and providing an animation guide that suggests appropriate gestures and facial expressions.

[0075] The suggestion unit can monitor the user's emotional state in real time using the emotion estimation function and provide advice to improve nonverbal communication according to the emotion. For example, the suggestion unit uses the generation AI to analyze the user's nonverbal communication and monitors emotions in real time using the emotion estimation function. For example, if the user is nervous, the suggestion unit provides advice to relax. In this way, the quality of the presentation is improved by monitoring the user's emotional state in real time using the emotion estimation function and providing advice to improve nonverbal communication according to the emotion.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The PresenMate AI system can also be equipped with a grammar checker that automatically converts recordings of users' presentations into text and checks for grammar and spelling errors based on the text data. For example, by converting what a user says into text in real time and pointing out grammatical and spelling errors, the accuracy of the presentation can be improved. The grammar checker can also analyze what the user says and suggest more appropriate expressions and phrases. This allows users to give more accurate and sophisticated presentations.

[0078] The PresenMate AI system can also be equipped with a reference material provider that automatically searches for and provides relevant reference materials and literature based on the content of a user's presentation. For example, presenting the latest research papers and statistical data related to what the user is talking about can increase the credibility and persuasiveness of the presentation. The reference material provider can also easily search for and instantly display information that the user wants to cite during the presentation. This allows users to make presentations based on more information.

[0079] The PresenMate AI system can also be equipped with a virtual audience unit that simulates a virtual audience to help users practice their presentations. For example, the virtual audience unit can provide an environment that is similar to a real presentation by having the virtual audience react in real time as the user gives a presentation. The virtual audience unit can also change the audience's facial expressions and attitudes depending on the content and tone of the user's speech. This allows users to practice more practically.

[0080] The PresenMate AI system can also include a visual feedback unit that provides visual feedback based on the content of a user's presentation. For example, it can automatically generate graphs and charts based on what the user is saying and display them in a visually easy-to-understand manner. The visual feedback unit can also provide real-time feedback based on the user's speaking speed and tone. This allows users to utilize visual information to give more effective presentations.

[0081] The Presenmate AI system can also be equipped with a scenario generation unit that automatically generates a presentation scenario based on the content of the user's presentation. For example, it can automatically generate the flow and structure of the presentation based on what the user will say and suggest it to the user. The scenario generation unit can also suggest the optimal scenario based on the purpose and target audience of the user's presentation. This allows users to create more effective presentation scenarios.

[0082] The PresenMate AI system can also include a relaxation music provider that analyzes the user's emotional state and provides music or environmental sounds that correspond to the emotion. For example, if the user is nervous, it can play music that has a relaxing effect. The relaxation music provider can also provide environmental sounds such as natural sounds or white noise depending on the user's emotional state. This allows the user to practice their presentation in a relaxed state.

[0083] The PresenMate AI system can also include an emotional feedback unit that analyzes the user's emotional state and provides feedback according to their emotions. For example, if the user is speaking with confidence, positive feedback can be provided to further boost the user's confidence. The emotional feedback unit can also provide advice on how to relax if the user is nervous. This allows the user to receive feedback according to their emotions and improve the quality of their presentation.

[0084] The PresenMate AI system can also be equipped with an emotional training unit that analyzes the user's emotional state and provides a training program tailored to that emotion. For example, if the user tends to get nervous, it can provide a training program to help them relax. The emotional training unit can also suggest ways to practice presentations based on the user's emotional state. This allows the user to receive training tailored to their emotions and improve the quality of their presentations.

[0085] The PresenMate AI system can also be equipped with an emotion advice unit that analyzes the user's emotional state and provides advice according to the emotion. For example, if the user is nervous, the emotion advice unit can provide advice to relax and help the user speak with confidence. If the user is speaking with confidence, the emotion advice unit can also provide advice to maintain that confidence. This allows the user to receive advice according to their emotions and improve the quality of their presentation.

[0086] The Presenmate AI system can also be equipped with an emotional relaxation module that analyzes the user's emotional state and provides relaxation techniques according to the emotion. For example, if the user is nervous, it can suggest relaxation techniques such as deep breathing or meditation. The emotional relaxation module can also provide relaxation settings and music according to the user's emotional state. This allows the user to practice their presentation in a relaxed state.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The rehearsal unit rehearses what the user will say. For example, when the user speaks the contents of a presentation, the generation AI rehearses that content. The input to the generation AI is the audio data of what the user will say, and the generation AI rehearses based on that audio data. Step 2: The analysis unit analyzes the user's speech, which has been rehearsed by the rehearsal unit. For example, the generation AI analyzes what the user says and suggests appropriate language, tone of voice, and areas for improvement in non-verbal communication. The input to the generation AI is audio data of what the user says, and the generation AI performs analysis based on that audio data. Step 3: The suggestion unit suggests improvements based on the user's speech analyzed by the analysis unit. For example, if the user frequently uses words like "um" and "so," the generation AI will provide advice such as "reducing these words will give a more professional impression." The input to the generation AI is the audio data of the user's speech, and the generation AI makes suggestions based on that audio data. Step 4: The analysis unit analyzes the user's heart rate and voice tremors in real time. For example, if the user becomes nervous and their heart rate increases or their voice trembles, the generation AI analyzes that data and suggests breathing techniques and relaxation techniques to help them relax. The input to the generation AI is the user's heart rate data and voice data, and the generation AI analyzes and makes suggestions based on that data.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

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

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

[0133] In the 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.

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

[0135] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0137] The data processing system 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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."

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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]

[0156] 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 rehearsal unit for rehearsing what the user is going to say; an analysis unit that analyzes the content of the speech rehearsed by the rehearsal unit; a suggestion unit that suggests improvements based on the content of the user's speech analyzed by the analysis unit; an analysis unit that analyzes the user's heart rate and voice tremor in real time; A system characterized by:

2. The proposal unit Learns from the user's past presentation data and provides personalized advice 2. The system of claim 1.

3. The proposal unit Accommodates presentations in different languages ​​and provides advice in multiple languages 2. The system of claim 1.

4. The analysis unit It analyzes not only the heart rate and voice tremor, but also electrodermal activity and breathing patterns to suggest more detailed relaxation techniques.

2. The system of claim 1.

5. The proposal unit Analyze the user's language and provide advice incorporating industry-specific terminology and trending words 2. The system of claim 1.

6. The proposal unit Analyze the tone of the user's voice, taking into account the frequency components and rhythm of the voice, and provide more detailed advice on improving the tone.

2. The system of claim 1.

7. The proposal unit Analyze the user's non-verbal communication, take into account posture and eye movements, and provide more detailed advice on how to improve.

2. The system of claim 1.

8. The proposal unit Analyzing the user's emotional state using an emotion estimation function and providing advice according to the emotion 2. The system of claim 1.

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