System

Abstract

An object of the system according to the embodiment is to transmit a voice to a partner of an online conference even when a user speaks silently.SOLUTION: A system includes an image analysis unit, a sound generation unit, and a cooperation unit. The image analysis unit analyzes a motion of a mouth or a facial expression of the user. The sound generation unit generates a sound on the basis of a result analyzed by the image analysis unit. The cooperation unit transmits the voice generated by the voice generation unit to the other party through the online conference tool.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it can be difficult to speak during online meetings due to surrounding noise and privacy concerns.

[0005] The system according to the embodiment aims to enable a user to communicate silently to the other party in an online conference by voice. [Means for solving the problem]

[0006] The system according to the embodiment includes an image analysis unit, a voice generation unit, and a linking unit. The image analysis unit analyzes the user's mouth movements or facial expressions. The voice generation unit generates voice based on the results of the analysis by the image analysis unit. The linking unit transmits the voice generated by the voice generation unit to the other party via an online conference tool. [Effects of the Invention]

[0007] In the system according to the embodiment, even if a user speaks silently, the user can communicate the speech to the other party in the online conference by voice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A conference system according to an embodiment of the present invention analyzes a user's mouth movements and facial expressions, generates speech, and transmits it to other parties through an online conference tool. This conference system can transmit audio of what a user says silently at their desk to other parties in an online conference. For example, the conference system captures the user's mouth movements and facial expressions with a camera and analyzes them using facial recognition technology based on deep learning. Next, it generates audio based on the analyzed mouth movements and facial expressions using text-to-speech (TTS) technology. The generated audio is transmitted to other parties through the online conference tool. This allows users to participate in conferences without worrying about ambient noise and maintain privacy. This system is also available as a paid service for external use. For example, companies can adopt this system to efficiently hold meetings regardless of their employees' locations. It is also available for individual users and is expected to be used in various scenarios, such as remote work and online classes. This allows the conference system to analyze a user's mouth movements and facial expressions, generate audio, and transmit it to other parties through the online conference tool. For example, it can transmit audio of what a user says silently at their desk to other parties in an online conference. This allows users to participate in a conference without worrying about surrounding noise, and allows the conference to be held while maintaining privacy.

[0029] The conference system according to the embodiment includes an image analysis unit, a voice generation unit, and a linking unit. The image analysis unit analyzes a user's mouth movements and facial expressions. Examples of the user's mouth movements and facial expressions include, but are not limited to, opening and closing the mouth, smiling, and eyebrow movements. The image analysis unit analyzes the user's mouth movements and facial expressions using, for example, face recognition technology based on deep learning. Deep learning technologies include, for example, convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The voice generation unit generates voice based on the results of the analysis by the image analysis unit. For example, text-to-speech (TTS) technology is used for voice generation. Examples of TTS technologies include WaveNet and Tacotron. The voice generation unit can generate voice that resembles the user's voice or can generate voice using a pre-defined voice. The linking unit transmits the voice generated by the voice generation unit to the other party via an online conference tool. The linking unit can transmit the voice in real time using, for example, an API. Examples of APIs include a REST API and a WebSocket API, which allow the conferencing system according to the embodiment to analyze the mouth movements and facial expressions of a user, generate speech, and transmit the speech to the other party via an online conferencing tool.

[0030] The voice generation unit can generate voice that resembles the user's voice. The voice generation unit analyzes, for example, the tone, pitch, accent, etc. of the user's voice and generates voice based on the analysis. For example, the voice generation unit analyzes the tone of the user's voice and reflects it in the generated voice. The voice generation unit can also analyze the pitch of the user's voice and reflect it in the generated voice. The voice generation unit can also analyze the accent of the user's voice and reflect it in the generated voice. In this way, more natural communication is possible by generating voice that resembles the user's voice.

[0031] The voice generation unit can generate voice using a preset voice. The voice generation unit can generate voice using, for example, a preset male voice, a preset female voice, or a voice of a specific character. For example, the voice generation unit can generate voice using a preset male voice. The voice generation unit can also generate voice using a preset female voice. The voice generation unit can also generate voice using the voice of a specific character. In this way, by using the preset voice, voices can be generated according to the user's preferences.

[0032] The linking unit can transmit audio in real time using an API. The linking unit can transmit audio in real time using, for example, a REST API or a WebSocket API. For example, the linking unit transmits audio data using a REST API. The linking unit can also transmit audio data in real time using a WebSocket API. The linking unit can also adjust the transmission speed of audio data using an API. As a result, audio can be transmitted in real time by using the API.

[0033] The image analysis unit can use facial recognition technology using deep learning. The image analysis unit, for example, uses facial recognition technology using deep learning to analyze the user's mouth movements and facial expressions. Deep learning technologies include, for example, CNN (convolutional neural network) and RNN (recurrent neural network). For example, the image analysis unit uses CNN to analyze the user's mouth movements. The image analysis unit can also analyze the user's facial expressions using RNN. The image analysis unit can also analyze the user's overall facial movements using deep learning. As a result, the accuracy of facial recognition is improved by using deep learning.

[0034] The speech generation unit can use text-to-speech (TTS) technology. The speech generation unit generates speech using, for example, text-to-speech (TTS) technology. Examples of TTS technology include WaveNet and Tacotron. For example, the speech generation unit can generate speech from text using WaveNet. The speech generation unit can also generate speech from text using Tacotron. The speech generation unit can also generate speech that resembles the user's voice using TTS technology. This makes it possible to generate speech from text using TTS technology.

[0035] The image analysis unit can include not only the movement of the user's mouth, but also the movement of the eyes or eyebrows in its analysis. The image analysis unit, for example, analyzes the movement of the user's eyes and eyebrows. For example, the image analysis unit analyzes the movement of the user's eyes and complements the movement of the mouth taking into account the direction of the gaze. The image analysis unit can also analyze the movement of the user's eyebrows to capture changes in emotions and improve the accuracy of the analysis. The image analysis unit can also analyze the movement of the user's entire face to reproduce more natural facial expressions. In this way, by including the movement of the eyes and eyebrows in the analysis, more natural facial expressions can be reproduced.

[0036] The image analysis unit can improve the analysis accuracy by referring to the user's past facial expression data. The image analysis unit improves the analysis accuracy by, for example, referring to the user's past facial expression data. Past facial expression data includes, for example, past image data and video data. For example, the image analysis unit improves the analysis accuracy by predicting specific mouth movements based on the user's past facial expression data. The image analysis unit can also improve the analysis accuracy by referring to the user's past facial expression data and recognizing specific facial expression patterns. The image analysis unit can also use the user's past facial expression data to perform analysis taking individual features into consideration. In this way, by referring to the past facial expression data, the analysis accuracy is improved.

[0037] The image analysis unit can perform analysis taking into account changes in facial expression under different lighting conditions. The image analysis unit performs analysis taking into account changes in facial expression under different lighting conditions, for example. Different lighting conditions include bright lighting, dim lighting, natural light, and the like. For example, the image analysis unit performs analysis by emphasizing facial contours even when the lighting is dim. Furthermore, the image analysis unit can perform analysis by suppressing light reflection when the lighting is too bright. Furthermore, the image analysis unit can perform analysis by performing color correction even when the color temperature of the lighting is different. This enables accurate analysis even under different lighting conditions.

[0038] The image analysis unit can improve the analysis accuracy by taking into account the direction and angle of the user's face. The image analysis unit improves the analysis accuracy by taking into account, for example, the direction and angle of the user's face. The direction and angle of the face include, for example, front, side, and diagonal. For example, the image analysis unit performs analysis by correcting the angle even when the user's face is facing diagonally. Furthermore, the image analysis unit can also perform analysis by correcting the movement when the user's face is moving up and down. Furthermore, even when the user's face is partially hidden, the image analysis unit can perform analysis based on the unhidden part. In this way, by taking into account the direction and angle of the face, the analysis accuracy is improved.

[0039] The image analysis unit can perform analysis taking into account background information of the user. The image analysis unit performs analysis taking into account, for example, background information of the user. Background information includes, for example, background color, background objects, etc. For example, even if the user's background is moving, the image analysis unit performs analysis by removing the background. Furthermore, if the user's background is bright, the image analysis unit can also adjust the brightness of the background before performing analysis. Furthermore, if there is another person behind the user, the image analysis unit can only analyze the target user. This allows for more accurate analysis by taking background information into account.

[0040] The image analysis unit can automatically detect and analyze the user's facial feature points. The image analysis unit, for example, automatically detects and analyzes the user's facial feature points (eyes, nose, mouth, etc.). Facial feature points include, for example, the positions of the eyes, nose, and mouth. For example, the image analysis unit automatically detects and analyzes the user's facial feature points. The image analysis unit can also analyze changes in facial expression based on the user's facial feature points. The image analysis unit can also analyze mouth movement in detail based on the user's facial feature points. This automatically detecting facial feature points improves the accuracy of the analysis.

[0041] The speech generation unit can adjust the speech generation speed based on the user's speaking speed when generating speech. The speech generation unit, for example, measures the user's speaking speed and adjusts the speech generation speed based on the measured speed. The speaking speed includes, for example, the number of words per minute or the number of syllables per second. For example, if the user speaks slowly, the speech generation unit slows down the speech generation speed to generate natural speech. Furthermore, if the user speaks quickly, the speech generation unit can also speed up the speech generation speed to generate speech in real time. Furthermore, the speech generation unit can dynamically adjust the speech generation speed according to the user's speaking speed. In this way, by adjusting the speech generation speed according to the speaking speed, more natural speech can be generated.

[0042] The speech generation unit can reproduce the user's accent and intonation when generating speech. The speech generation unit, for example, analyzes the user's accent and intonation and generates speech based on the analysis. Accent and intonation include, for example, regional accents and intonation that express emotions. For example, the speech generation unit analyzes the user's accent and reflects it in the generated speech. The speech generation unit can also analyze the user's intonation and reflect it in the generated speech. The speech generation unit can also analyze the characteristics of the user's speaking style and generate natural speech. In this way, accent and intonation can be reproduced, thereby generating more natural speech.

[0043] The voice generation unit can improve the naturalness of the voice by referring to the user's past voice data when generating the voice. The voice generation unit, for example, improves the naturalness of the voice by referring to the user's past voice data. Past voice data includes, for example, past recording data and voice files. For example, the voice generation unit generates a natural voice based on the user's past voice data. The voice generation unit can also reproduce a specific speaking pattern by referring to the user's past voice data. The voice generation unit can also generate a voice that takes individual characteristics into consideration by using the user's past voice data. In this way, the naturalness of the voice is improved by referring to the past voice data.

[0044] The voice generation unit can enable voice generation in different languages ​​during voice generation. The voice generation unit enables voice generation in different languages, for example. Different languages ​​include English, Japanese, French, and the like. For example, when a user speaks in English, the voice generation unit generates voice in English. Also, when a user speaks in Japanese, the voice generation unit can generate voice in Japanese. Also, when a user uses multiple languages, the voice generation unit can provide a language switching function. This enables voice generation in different languages.

[0045] The voice generation unit can apply a filter that emphasizes the characteristics of the user's voice when generating voice. The voice generation unit generates voice by applying, for example, a filter that emphasizes the characteristics of the user's voice. Filters that emphasize voice characteristics include, for example, an echo filter and a noise reduction filter. For example, the voice generation unit applies a filter that emphasizes the characteristics of the user's voice to generate natural voice. The voice generation unit can also apply a filter that emphasizes a specific range of sound based on the characteristics of the user's voice. The voice generation unit can also analyze the characteristics of the user's voice and generate voice by applying an optimal filter. In this way, by emphasizing the characteristics of the user's voice, it is possible to generate more natural voice.

[0046] The audio generation unit can add background sounds and sound effects when generating audio to enhance the sense of realism of the audio. The audio generation unit generates audio by adding appropriate background sounds and sound effects, for example, in accordance with what the user is saying. The background sounds and sound effects include, for example, natural sounds, environmental sounds, and specific sound effects. For example, the audio generation unit adds appropriate background sounds in accordance with what the user is saying. The audio generation unit can also add sound effects in accordance with what the user is saying. The audio generation unit can also add sound effects to enhance the sense of realism in accordance with what the user is saying. In this way, the sense of realism of the audio is enhanced by adding background sounds and sound effects.

[0047] During collaboration, the collaboration unit can improve communication efficiency by adjusting the compression rate of the audio data. The collaboration unit, for example, adjusts the compression rate of the audio data to improve communication efficiency. The compression rate of the audio data includes, for example, a bit rate and a compression algorithm. For example, when the communication environment is poor, the collaboration unit increases the compression rate of the audio data to improve communication efficiency. Furthermore, when the communication environment is good, the collaboration unit can also lower the compression rate of the audio data to improve sound quality. Furthermore, the collaboration unit can dynamically adjust the compression rate of the audio data according to the communication environment. In this way, communication efficiency is improved by adjusting the compression rate of the audio data.

[0048] During collaboration, the collaboration unit can encrypt audio data to enhance security. The collaboration unit, for example, encrypts audio data to enhance security. Examples of encryption methods for audio data include AES encryption and RSA encryption. For example, the collaboration unit encrypts audio data and communicates it to enhance security. The collaboration unit can also enable selection of an encryption method for audio data. The collaboration unit can also encrypt and decrypt audio data in real time. As a result, encryption of audio data enhances communication security.

[0049] The linking unit can improve the reliability of communication by performing an error check on the audio data during linking. The linking unit, for example, performs an error check on the audio data to improve the reliability of communication. Examples of error checks on the audio data include a CRC check and a Hamming code. For example, the linking unit performs an error check on the audio data before transmitting the audio data to improve the reliability of communication. The linking unit can also perform an error check on the audio data after receiving the audio data to improve the reliability of communication. The linking unit can also perform an error check while transmitting and receiving the audio data to improve the reliability of communication in real time. In this way, by performing an error check on the audio data, the reliability of communication is improved.

[0050] The collaboration unit can ensure compatibility between different online conference tools during collaboration. The collaboration unit ensures compatibility between, for example, different online conference tools. Examples of different online conference tools include Zoom (registered trademark), Microsoft Teams (registered trademark), and Google Meet (registered trademark). For example, the collaboration unit transmits audio data between Zoom and Meet in a manner that ensures compatibility. The collaboration unit can also transmit audio data between Teams and Webex in a manner that ensures compatibility. The collaboration unit can also automatically convert the format of audio data between different online conference tools. This ensures compatibility between different online conference tools, enabling seamless communication.

[0051] The linking unit can add a function for real-time translation of voice data during linking. The linking unit adds, for example, a function for real-time translation of voice data. The real-time translation of voice data includes, for example, a translation algorithm and supported languages. For example, the linking unit translates voice spoken in English into Japanese in real time and transmits the translated voice. The linking unit can also translate voice spoken in Japanese into English in real time and transmit the translated voice. The linking unit can also provide a real-time translation function between multiple languages. This makes it possible to communicate between different languages ​​by translating voice data in real time.

[0052] The linking unit can add a function to automatically back up audio data when linked. The linking unit adds, for example, a function to automatically back up audio data. Backing up audio data includes, for example, the frequency of backups and the storage destination. For example, the linking unit automatically backs up audio data to the cloud. The linking unit can also automatically back up audio data to local storage. The linking unit can also set a backup schedule for audio data and perform backups periodically. In this way, automatic backup of audio data ensures data integrity.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The conference system can translate user comments in real time to facilitate communication between participants who speak different languages. For example, the comments of an English-speaking user can be translated into Japanese and conveyed to Japanese-speaking participants. It can also translate the comments of a Japanese-speaking user into English and convey them to English-speaking participants. Furthermore, it can translate multiple languages ​​simultaneously to support communication between multiple participants who speak different languages. This enables smooth communication between participants who speak different languages.

[0055] The conference system can automatically summarize what users say to improve the efficiency of the meeting. For example, it can summarize long statements in a short form and convey it to other participants. It can also summarize multiple statements together to make the meeting run smoothly. It can also emphasize and summarize important points to ensure that participants do not miss important information. In this way, the efficiency of the meeting is improved through the summarization of statements, and all participants can grasp the important information.

[0056] A conference system can automatically record what users say and make it available for later reference. For example, it can record what users say in text format so that participants can review it after the meeting is over. It can also record what users say in audio format so that it can be played back later. It can also summarize what users say and record it so that important points can be easily reviewed. This allows the content of the meeting to be reviewed later through the recording of what users say, ensuring that all participants understand important information.

[0057] The conference system can analyze user comments in real time and extract and display important keywords. For example, it can extract important keywords from comments and display them to other participants. It can also extract common keywords from multiple comments to support the progress of the conference. It can also analyze the frequency of keywords and display them according to their importance. This allows the conference to proceed smoothly through analysis of comments, and all participants can grasp important information.

[0058] The conference system can automatically categorize user comments and organize them by agenda. For example, comments can be categorized by agenda and displayed to other participants. Multiple comments can also be grouped and organized into related agenda items to facilitate smooth progress in the meeting. Furthermore, important agenda items can be highlighted to ensure participants do not miss important information. This allows the meeting to proceed smoothly through the categorization of comments, and all participants can grasp important information.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The image analysis unit analyzes the user's mouth movements and facial expressions. Examples of the user's mouth movements and facial expressions include, but are not limited to, opening and closing the mouth, smiling, and eyebrow movements. The image analysis unit analyzes the user's mouth movements and facial expressions using, for example, face recognition technology using deep learning. Deep learning technology includes, for example, CNN (convolutional neural network) and RNN (recurrent neural network). Step 2: The voice generator generates voice based on the results of the image analysis. For example, text-to-speech (TTS) technology is used for voice generation. Examples of TTS technologies include WaveNet and Tacotron. The voice generator can generate a voice that resembles the user's voice or can generate a voice using a pre-defined voice. Step 3: The collaboration unit transmits the audio generated by the audio generation unit to the other party via an online conference tool. The collaboration unit can transmit the audio in real time, for example, using an API. Examples of APIs include a REST API and a WebSocket API.

[0061] (Example 2) A conference system according to an embodiment of the present invention analyzes a user's mouth movements and facial expressions, generates speech, and transmits it to other parties through an online conference tool. This conference system can transmit audio of what a user says silently at their desk to other parties in an online conference. For example, the conference system captures the user's mouth movements and facial expressions with a camera and analyzes them using facial recognition technology based on deep learning. Next, it generates audio based on the analyzed mouth movements and facial expressions using text-to-speech (TTS) technology. The generated audio is transmitted to other parties through the online conference tool. This allows users to participate in conferences without worrying about ambient noise and maintain privacy. This system is also available as a paid service for external use. For example, companies can adopt this system to efficiently hold meetings regardless of their employees' locations. It is also available for individual users and is expected to be used in various scenarios, such as remote work and online classes. This allows the conference system to analyze a user's mouth movements and facial expressions, generate audio, and transmit it to other parties through the online conference tool. For example, it can transmit audio of what a user says silently at their desk to other parties in an online conference. This allows users to participate in a conference without worrying about surrounding noise, and allows the conference to be held while maintaining privacy.

[0062] The conference system according to the embodiment includes an image analysis unit, a voice generation unit, and a linking unit. The image analysis unit analyzes a user's mouth movements and facial expressions. Examples of the user's mouth movements and facial expressions include, but are not limited to, opening and closing the mouth, smiling, and eyebrow movements. The image analysis unit analyzes the user's mouth movements and facial expressions using, for example, face recognition technology based on deep learning. Deep learning technologies include, for example, convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The voice generation unit generates voice based on the results of the analysis by the image analysis unit. For example, text-to-speech (TTS) technology is used for voice generation. Examples of TTS technologies include WaveNet and Tacotron. The voice generation unit can generate voice that resembles the user's voice or can generate voice using a pre-defined voice. The linking unit transmits the voice generated by the voice generation unit to the other party via an online conference tool. The linking unit can transmit the voice in real time using, for example, an API. Examples of APIs include a REST API and a WebSocket API, which allow the conferencing system according to the embodiment to analyze the mouth movements and facial expressions of a user, generate speech, and transmit the speech to the other party via an online conferencing tool.

[0063] The voice generation unit can generate voice that resembles the user's voice. The voice generation unit analyzes, for example, the tone, pitch, accent, etc. of the user's voice and generates voice based on the analysis. For example, the voice generation unit analyzes the tone of the user's voice and reflects it in the generated voice. The voice generation unit can also analyze the pitch of the user's voice and reflect it in the generated voice. The voice generation unit can also analyze the accent of the user's voice and reflect it in the generated voice. In this way, more natural communication is possible by generating voice that resembles the user's voice.

[0064] The voice generation unit can generate voice using a preset voice. The voice generation unit can generate voice using, for example, a preset male voice, a preset female voice, or a voice of a specific character. For example, the voice generation unit can generate voice using a preset male voice. The voice generation unit can also generate voice using a preset female voice. The voice generation unit can also generate voice using the voice of a specific character. In this way, by using the preset voice, voices can be generated according to the user's preferences.

[0065] The linking unit can transmit audio in real time using an API. The linking unit can transmit audio in real time using, for example, a REST API or a WebSocket API. For example, the linking unit transmits audio data using a REST API. The linking unit can also transmit audio data in real time using a WebSocket API. The linking unit can also adjust the transmission speed of audio data using an API. As a result, audio can be transmitted in real time by using the API.

[0066] The image analysis unit can use facial recognition technology using deep learning. The image analysis unit, for example, uses facial recognition technology using deep learning to analyze the user's mouth movements and facial expressions. Deep learning technologies include, for example, CNN (convolutional neural network) and RNN (recurrent neural network). For example, the image analysis unit uses CNN to analyze the user's mouth movements. The image analysis unit can also analyze the user's facial expressions using RNN. The image analysis unit can also analyze the user's overall facial movements using deep learning. As a result, the accuracy of facial recognition is improved by using deep learning.

[0067] The speech generation unit can use text-to-speech (TTS) technology. The speech generation unit generates speech using, for example, text-to-speech (TTS) technology. Examples of TTS technology include WaveNet and Tacotron. For example, the speech generation unit can generate speech from text using WaveNet. The speech generation unit can also generate speech from text using Tacotron. The speech generation unit can also generate speech that resembles the user's voice using TTS technology. This makes it possible to generate speech from text using TTS technology.

[0068] The image analysis unit can estimate the user's emotions and adjust the analysis accuracy based on the estimated user's emotions. The image analysis unit estimates the user's emotions using, for example, the user's facial expression analysis, voice analysis, biometrics, etc. For example, the image analysis unit analyzes the user's facial expression to estimate the emotions. The image analysis unit can also analyze the user's voice to estimate the emotions. The image analysis unit can also analyze the user's biometric data to estimate the emotions. The image analysis unit adjusts the analysis accuracy based on the estimated user's emotions. For example, if the user is nervous, the analysis accuracy can be increased to more accurately capture mouth movements. If the user is relaxed, the analysis accuracy can be kept normal to capture natural movements. If the user is in a hurry, the analysis speed can be prioritized and real-time analysis can be performed. This allows for more accurate analysis by adjusting the analysis accuracy according to the user's emotions.

[0069] The image analysis unit can include not only the movement of the user's mouth, but also the movement of the eyes or eyebrows in its analysis. The image analysis unit, for example, analyzes the movement of the user's eyes and eyebrows. For example, the image analysis unit analyzes the movement of the user's eyes and complements the movement of the mouth taking into account the direction of the gaze. The image analysis unit can also analyze the movement of the user's eyebrows to capture changes in emotions and improve the accuracy of the analysis. The image analysis unit can also analyze the movement of the user's entire face to reproduce more natural facial expressions. In this way, by including the movement of the eyes and eyebrows in the analysis, more natural facial expressions can be reproduced.

[0070] The image analysis unit can improve the analysis accuracy by referring to the user's past facial expression data. The image analysis unit improves the analysis accuracy by, for example, referring to the user's past facial expression data. Past facial expression data includes, for example, past image data and video data. For example, the image analysis unit improves the analysis accuracy by predicting specific mouth movements based on the user's past facial expression data. The image analysis unit can also improve the analysis accuracy by referring to the user's past facial expression data and recognizing specific facial expression patterns. The image analysis unit can also use the user's past facial expression data to perform analysis taking individual features into consideration. In this way, by referring to the past facial expression data, the analysis accuracy is improved.

[0071] The image analysis unit can perform analysis taking into account changes in facial expression under different lighting conditions. The image analysis unit performs analysis taking into account changes in facial expression under different lighting conditions, for example. Different lighting conditions include bright lighting, dim lighting, natural light, and the like. For example, the image analysis unit performs analysis by emphasizing facial contours even when the lighting is dim. Furthermore, the image analysis unit can perform analysis by suppressing light reflection when the lighting is too bright. Furthermore, the image analysis unit can perform analysis by performing color correction even when the color temperature of the lighting is different. This enables accurate analysis even under different lighting conditions.

[0072] The image analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. The image analysis unit estimates the user's emotions using, for example, the user's facial expression analysis, voice analysis, biometrics, etc. For example, the image analysis unit analyzes the user's facial expression to estimate the emotion. The image analysis unit can also analyze the user's voice to estimate the emotion. The image analysis unit can also analyze the user's biometric data to estimate the emotion. The image analysis unit adjusts the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that focuses on the main points can be provided. This allows the display method of the analysis results to be adjusted according to the user's emotions, enabling more appropriate display.

[0073] The image analysis unit can improve the analysis accuracy by taking into account the direction and angle of the user's face. The image analysis unit improves the analysis accuracy by taking into account, for example, the direction and angle of the user's face. The direction and angle of the face include, for example, front, side, and diagonal. For example, the image analysis unit performs analysis by correcting the angle even when the user's face is facing diagonally. Furthermore, the image analysis unit can also perform analysis by correcting the movement when the user's face is moving up and down. Furthermore, even when the user's face is partially hidden, the image analysis unit can perform analysis based on the unhidden part. In this way, by taking into account the direction and angle of the face, the analysis accuracy is improved.

[0074] The image analysis unit can perform analysis taking into account background information of the user. The image analysis unit performs analysis taking into account, for example, background information of the user. Background information includes, for example, background color, background objects, etc. For example, even if the user's background is moving, the image analysis unit performs analysis by removing the background. Furthermore, if the user's background is bright, the image analysis unit can also adjust the brightness of the background before performing analysis. Furthermore, if there is another person behind the user, the image analysis unit can only analyze the target user. This allows for more accurate analysis by taking background information into account.

[0075] The image analysis unit can automatically detect and analyze the user's facial feature points. The image analysis unit, for example, automatically detects and analyzes the user's facial feature points (eyes, nose, mouth, etc.). Facial feature points include, for example, the positions of the eyes, nose, and mouth. For example, the image analysis unit automatically detects and analyzes the user's facial feature points. The image analysis unit can also analyze changes in facial expression based on the user's facial feature points. The image analysis unit can also analyze mouth movement in detail based on the user's facial feature points. This automatically detecting facial feature points improves the accuracy of the analysis.

[0076] The voice generation unit can estimate the user's emotion and adjust the tone and pitch of the voice based on the estimated user's emotion. The voice generation unit estimates the user's emotion using, for example, the user's facial expression analysis, voice analysis, biometrics, etc. For example, the voice generation unit analyzes the user's facial expression to estimate the emotion. The voice generation unit can also analyze the user's voice to estimate the emotion. The voice generation unit can also analyze the user's biometric data to estimate the emotion. The voice generation unit adjusts the tone and pitch of the voice based on the estimated user's emotion. For example, if the user is nervous, the voice generation unit can generate a calm tone. If the user is relaxed, the voice generation unit can generate a bright tone. If the user is in a hurry, the voice generation unit can generate a quick and concise tone. In this way, by adjusting the tone and pitch of the voice according to the user's emotion, a more natural voice can be generated.

[0077] The speech generation unit can adjust the speech generation speed based on the user's speaking speed when generating speech. The speech generation unit, for example, measures the user's speaking speed and adjusts the speech generation speed based on the measured speed. The speaking speed includes, for example, the number of words per minute or the number of syllables per second. For example, if the user speaks slowly, the speech generation unit slows down the speech generation speed to generate natural speech. Furthermore, if the user speaks quickly, the speech generation unit can also speed up the speech generation speed to generate speech in real time. Furthermore, the speech generation unit can dynamically adjust the speech generation speed according to the user's speaking speed. In this way, by adjusting the speech generation speed according to the speaking speed, more natural speech can be generated.

[0078] The speech generation unit can reproduce the user's accent and intonation when generating speech. The speech generation unit, for example, analyzes the user's accent and intonation and generates speech based on the analysis. Accent and intonation include, for example, regional accents and intonation that express emotions. For example, the speech generation unit analyzes the user's accent and reflects it in the generated speech. The speech generation unit can also analyze the user's intonation and reflect it in the generated speech. The speech generation unit can also analyze the characteristics of the user's speaking style and generate natural speech. In this way, accent and intonation can be reproduced, thereby generating more natural speech.

[0079] The voice generation unit can improve the naturalness of the voice by referring to the user's past voice data when generating the voice. The voice generation unit, for example, improves the naturalness of the voice by referring to the user's past voice data. Past voice data includes, for example, past recording data and voice files. For example, the voice generation unit generates a natural voice based on the user's past voice data. The voice generation unit can also reproduce a specific speaking pattern by referring to the user's past voice data. The voice generation unit can also generate a voice that takes individual characteristics into consideration by using the user's past voice data. In this way, the naturalness of the voice is improved by referring to the past voice data.

[0080] The voice generation unit can estimate the user's emotion and adjust the emotional expression of the voice based on the estimated user's emotion. The voice generation unit estimates the user's emotion using, for example, the user's facial expression analysis, voice analysis, biometrics, etc. For example, the voice generation unit analyzes the user's facial expression to estimate the emotion. The voice generation unit can also analyze the user's voice to estimate the emotion. The voice generation unit can also analyze the user's biometric data to estimate the emotion. The voice generation unit adjusts the emotional expression of the voice based on the estimated user's emotion. For example, if the user is nervous, the voice generation unit can generate a voice with a calm emotional expression. If the user is relaxed, the voice generation unit can generate a voice with a cheerful emotional expression. If the user is in a hurry, the voice generation unit can generate a voice with a quick and concise emotional expression. In this way, by adjusting the emotional expression of the voice according to the user's emotion, a more natural voice can be generated.

[0081] The voice generation unit can enable voice generation in different languages ​​during voice generation. The voice generation unit enables voice generation in different languages, for example. Different languages ​​include English, Japanese, French, and the like. For example, when a user speaks in English, the voice generation unit generates voice in English. Also, when a user speaks in Japanese, the voice generation unit can generate voice in Japanese. Also, when a user uses multiple languages, the voice generation unit can provide a language switching function. This enables voice generation in different languages.

[0082] The voice generation unit can apply a filter that emphasizes the characteristics of the user's voice when generating voice. The voice generation unit generates voice by applying, for example, a filter that emphasizes the characteristics of the user's voice. Filters that emphasize voice characteristics include, for example, an echo filter and a noise reduction filter. For example, the voice generation unit applies a filter that emphasizes the characteristics of the user's voice to generate natural voice. The voice generation unit can also apply a filter that emphasizes a specific range of sound based on the characteristics of the user's voice. The voice generation unit can also analyze the characteristics of the user's voice and generate voice by applying an optimal filter. In this way, by emphasizing the characteristics of the user's voice, it is possible to generate more natural voice.

[0083] The audio generation unit can add background sounds and sound effects when generating audio to enhance the sense of realism of the audio. The audio generation unit generates audio by adding appropriate background sounds and sound effects, for example, in accordance with what the user is saying. The background sounds and sound effects include, for example, natural sounds, environmental sounds, and specific sound effects. For example, the audio generation unit adds appropriate background sounds in accordance with what the user is saying. The audio generation unit can also add sound effects in accordance with what the user is saying. The audio generation unit can also add sound effects to enhance the sense of realism in accordance with what the user is saying. In this way, the sense of realism of the audio is enhanced by adding background sounds and sound effects.

[0084] The collaboration unit can estimate the user's emotions and adjust the voice transmission method based on the estimated user's emotions. The collaboration unit estimates the user's emotions using, for example, the user's facial expression analysis, voice analysis, biometrics, etc. For example, the collaboration unit analyzes the user's facial expression to estimate the emotion. The collaboration unit can also analyze the user's voice to estimate the emotion. The collaboration unit can also analyze the user's biometric data to estimate the emotion. The collaboration unit adjusts the voice transmission method based on the estimated user's emotions. For example, if the user is nervous, a calm voice transmission method can be provided. If the user is relaxed, a cheerful voice transmission method can be provided. If the user is in a hurry, a quick and concise voice transmission method can be provided. As a result, by adjusting the voice transmission method according to the user's emotions, more appropriate voice transmission is possible.

[0085] During collaboration, the collaboration unit can improve communication efficiency by adjusting the compression rate of the audio data. The collaboration unit, for example, adjusts the compression rate of the audio data to improve communication efficiency. The compression rate of the audio data includes, for example, a bit rate and a compression algorithm. For example, when the communication environment is poor, the collaboration unit increases the compression rate of the audio data to improve communication efficiency. Furthermore, when the communication environment is good, the collaboration unit can also lower the compression rate of the audio data to improve sound quality. Furthermore, the collaboration unit can dynamically adjust the compression rate of the audio data according to the communication environment. In this way, communication efficiency is improved by adjusting the compression rate of the audio data.

[0086] During collaboration, the collaboration unit can encrypt audio data to enhance security. The collaboration unit, for example, encrypts audio data to enhance security. Examples of encryption methods for audio data include AES encryption and RSA encryption. For example, the collaboration unit encrypts audio data and communicates it to enhance security. The collaboration unit can also enable selection of an encryption method for audio data. The collaboration unit can also encrypt and decrypt audio data in real time. As a result, encryption of audio data enhances communication security.

[0087] The linking unit can improve the reliability of communication by performing an error check on the audio data during linking. The linking unit, for example, performs an error check on the audio data to improve the reliability of communication. Examples of error checks on the audio data include a CRC check and a Hamming code. For example, the linking unit performs an error check on the audio data before transmitting the audio data to improve the reliability of communication. The linking unit can also perform an error check on the audio data after receiving the audio data to improve the reliability of communication. The linking unit can also perform an error check while transmitting and receiving the audio data to improve the reliability of communication in real time. In this way, by performing an error check on the audio data, the reliability of communication is improved.

[0088] The linking unit can estimate the user's emotions and determine the priority of voice transmission based on the estimated user's emotions. The linking unit estimates the user's emotions using, for example, facial expression analysis, voice analysis, biometrics, etc. For example, the linking unit analyzes the user's facial expression to estimate the emotion. The linking unit can also analyze the user's voice to estimate the emotion. The linking unit can also analyze the user's biometric data to estimate the emotion. The linking unit determines the priority of voice transmission based on the estimated user's emotions. For example, if the user is nervous, important information can be transmitted with priority. Also, if the user is relaxed, detailed information can be transmitted with priority. Also, if the user is in a hurry, information that summarizes the main points can be transmitted with priority. In this way, by determining the priority of voice transmission according to the user's emotions, important information can be transmitted with priority.

[0089] The collaboration unit can ensure compatibility between different online conference tools during collaboration. The collaboration unit ensures compatibility between, for example, different online conference tools. Examples of different online conference tools include Zoom (registered trademark), Microsoft Teams (registered trademark), and Google Meet (registered trademark). For example, the collaboration unit transmits audio data between Zoom and Meet in a manner that ensures compatibility. The collaboration unit can also transmit audio data between Teams and Webex in a manner that ensures compatibility. The collaboration unit can also automatically convert the format of audio data between different online conference tools. This ensures compatibility between different online conference tools, enabling seamless communication.

[0090] The linking unit can add a function for real-time translation of voice data during linking. The linking unit adds, for example, a function for real-time translation of voice data. The real-time translation of voice data includes, for example, a translation algorithm and supported languages. For example, the linking unit translates voice spoken in English into Japanese in real time and transmits the translated voice. The linking unit can also translate voice spoken in Japanese into English in real time and transmit the translated voice. The linking unit can also provide a real-time translation function between multiple languages. This makes it possible to communicate between different languages ​​by translating voice data in real time.

[0091] The linking unit can add a function to automatically back up audio data when linked. The linking unit adds, for example, a function to automatically back up audio data. Backing up audio data includes, for example, the frequency of backups and the storage destination. For example, the linking unit automatically backs up audio data to the cloud. The linking unit can also automatically back up audio data to local storage. The linking unit can also set a backup schedule for audio data and perform backups periodically. In this way, automatic backup of audio data ensures data integrity. === Hard Collateral 1-1 === Each of the multiple elements including the image analysis unit, the voice generation unit, and the linking unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the image analysis unit captures the user's mouth movements and facial expressions using the camera 42 of the smart device 14, and the captured images are analyzed by the specific processing unit 290 of the data processing device 12 using deep learning technology. The voice generation unit generates voice based on the analysis results of the specific processing unit 290 of the data processing device 12, for example. The linking unit transmits the generated voice to the online conference tool via the communication I / F 44 of the smart device 14, for example. === Hard Collateral 1-2 === Each of the multiple elements including the image analysis unit, the voice generation unit, and the linking unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the image analysis unit captures the user's mouth movements and facial expressions using the camera 42 of the smart glasses 214, and the captured images are analyzed by the specific processing unit 290 of the data processing device 12 using deep learning technology. The voice generation unit generates voice based on the analysis results of the specific processing unit 290 of the data processing device 12, for example. The linking unit transmits the generated voice to the online conference tool via the communication I / F 44 of the smart glasses 214, for example. === Hard Collateral 1-3 === Each of the multiple elements including the image analysis unit, the voice generation unit, and the linking unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the image analysis unit captures the user's mouth movements and facial expressions using the camera 42 of the headset terminal 314, and the captured images are analyzed by the specific processing unit 290 of the data processing device 12 using deep learning technology. The voice generation unit generates voice based on the analysis results of the specific processing unit 290 of the data processing device 12, for example. The linking unit transmits the generated voice to the online conference tool via the communication I / F 44 of the headset terminal 314, for example. === Hard Collateral 1-4 === Each of the multiple elements including the image analysis unit, the voice generation unit, and the linking unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the image analysis unit uses the camera 42 of the robot 414 to capture images of the user's mouth movements and facial expressions, which are then analyzed by the specific processing unit 290 of the data processing device 12 using deep learning technology. The voice generation unit generates voice based on the analysis results of the specific processing unit 290 of the data processing device 12, for example. The linking unit transmits the generated voice to the online conference tool via the communication I / F 44 of the robot 414, for example.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The conference system can estimate the user's emotions and adjust the progress of the conference based on the estimated emotions. For example, if the user is nervous, the system can slow down the progress of the conference to allow the user to relax. If the user is excited, the system can smooth the progress of the conference and actively incorporate the user's opinions. Furthermore, if the user is tired, the system can shorten the progress of the conference and efficiently move forward with the agenda. This makes it possible to proceed with the conference according to the user's emotions, resulting in more effective communication.

[0094] The conference system can translate user comments in real time to facilitate communication between participants who speak different languages. For example, the comments of an English-speaking user can be translated into Japanese and conveyed to Japanese-speaking participants. It can also translate the comments of a Japanese-speaking user into English and convey them to English-speaking participants. Furthermore, it can translate multiple languages ​​simultaneously to support communication between multiple participants who speak different languages. This enables smooth communication between participants who speak different languages.

[0095] The conferencing system can estimate a user's emotions and automatically adjust the meeting agenda based on the estimated emotions. For example, if the user is excited, the system will delve deeper into the agenda and actively incorporate the user's opinions. If the user is tired, the system will simplify the agenda and proceed efficiently. Furthermore, if the user is relaxed, the system can flexibly change the agenda and adjust it to suit the user's interests and concerns. This makes it possible to adjust the agenda based on the user's emotions, resulting in more effective meetings.

[0096] The conference system can automatically summarize what users say to improve the efficiency of the meeting. For example, it can summarize long statements in a short form and convey it to other participants. It can also summarize multiple statements together to make the meeting run smoothly. It can also emphasize and summarize important points to ensure that participants do not miss important information. In this way, the efficiency of the meeting is improved through the summarization of statements, and all participants can grasp the important information.

[0097] The conference system can estimate the user's emotions and adjust the atmosphere of the conference based on the estimated emotions. For example, if the user is nervous, the system creates a relaxed atmosphere, allowing the user to speak with confidence. If the user is excited, the system creates a lively atmosphere, allowing the user's opinions to be actively incorporated. Furthermore, if the user is tired, the system creates a calm atmosphere, allowing the conference to proceed efficiently. In this way, the atmosphere of the conference can be adjusted according to the user's emotions, resulting in more effective communication.

[0098] A conference system can automatically record what users say and make it available for later reference. For example, it can record what users say in text format so that participants can review it after the meeting is over. It can also record what users say in audio format so that it can be played back later. It can also summarize what users say and record it so that important points can be easily reviewed. This allows the content of the meeting to be reviewed later through the recording of what users say, ensuring that all participants understand important information.

[0099] The conference system can estimate the user's emotions and provide feedback on the conference based on the estimated emotions. For example, if the user is nervous, the system can provide advice to relax. If the user is excited, the system can also provide advice to calm down. Furthermore, if the user is tired, the system can also encourage the user to take a break. This provides feedback according to the user's emotions, resulting in more effective conferences.

[0100] The conference system can analyze user comments in real time and extract and display important keywords. For example, it can extract important keywords from comments and display them to other participants. It can also extract common keywords from multiple comments to support the progress of the conference. It can also analyze the frequency of keywords and display them according to their importance. This allows the conference to proceed smoothly through analysis of comments, and all participants can grasp important information.

[0101] The conferencing system can estimate the user's emotions and adjust the timing of the end of the conference based on the estimated emotions. For example, if the user is tired, the system can adjust the conference to end earlier. If the user is excited, the system can extend the conference to fully incorporate the user's opinions. Furthermore, if the user is relaxed, the system can flexibly adjust the progress of the conference and change the content to suit the user's interests and concerns. This allows the end of the conference to be adjusted according to the user's emotions, resulting in more effective conferences.

[0102] The conference system can automatically categorize user comments and organize them by agenda. For example, comments can be categorized by agenda and displayed to other participants. Multiple comments can also be grouped and organized into related agenda items to facilitate smooth progress in the meeting. Furthermore, important agenda items can be highlighted to ensure participants do not miss important information. This allows the meeting to proceed smoothly through the categorization of comments, and all participants can grasp important information.

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

[0104] Step 1: The image analysis unit analyzes the user's mouth movements and facial expressions. Examples of the user's mouth movements and facial expressions include, but are not limited to, opening and closing the mouth, smiling, and eyebrow movements. The image analysis unit analyzes the user's mouth movements and facial expressions using, for example, face recognition technology using deep learning. Deep learning technology includes, for example, CNN (convolutional neural network) and RNN (recurrent neural network). Step 2: The voice generator generates voice based on the results of the image analysis. For example, text-to-speech (TTS) technology is used for voice generation. Examples of TTS technologies include WaveNet and Tacotron. The voice generator can generate a voice that resembles the user's voice or can generate a voice using a pre-defined voice. Step 3: The collaboration unit transmits the audio generated by the audio generation unit to the other party via an online conference tool. The collaboration unit can transmit the audio in real time, for example, using an API. Examples of APIs include a REST API and a WebSocket API.

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

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

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

[0108] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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, in order to avoid confusion and to 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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 image analysis unit that analyzes the user's mouth movements or facial expressions; a sound generating unit that generates sound based on the result of the analysis by the image analyzing unit; a linking unit that transmits the voice generated by the voice generating unit to a partner through an online conference tool; A system characterized by:

2. The voice generation unit Generate a voice that resembles the user's voice 2. The system of claim 1.

3. The voice generation unit Generate speech using pre-defined voices 2. The system of claim 1.

4. The linking unit is Use APIs to deliver audio in real time 2. The system of claim 1.

5. The image analysis unit Use deep learning facial recognition technology 2. The system of claim 1.

6. The voice generation unit Use Text-to-Speech (TTS) technology 2. The system of claim 1.

7. The image analysis unit Estimate the user's emotions and adjust the analysis accuracy based on the estimated user emotions.

2. The system of claim 1.

8. The image analysis unit Include not only the user's mouth movements but also their eye or eyebrow movements in the analysis 2. The system of claim 1.

9. The image analysis unit Improve analysis accuracy by referencing the user's past facial expression data 2. The system of claim 1.

Images