System

Abstract

An object of a system according to an embodiment is to enable smooth and real-time communication between a person without hearing and a person with hearing.SOLUTION: A system includes a voice acquisition unit, a sign language acquisition unit, a conversion unit, and a notification unit. The voice acquisition unit acquires voice. The sign language acquisition unit acquires a sign language. The conversion unit converts the speech acquired by the speech acquisition unit into characters, and converts the sign language acquired by the sign language acquisition unit into speech and characters. The notification unit notifies the person without hearing of the characters converted by the conversion unit, and notifies the person with hearing of the voice and the characters converted by the conversion unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for people with hearing loss and people with hearing to communicate smoothly and in real time.

[0005] The system according to the embodiment aims to enable people with hearing loss and people with hearing loss to communicate smoothly in real time. [Means for solving the problem]

[0006] A system according to an embodiment includes a speech acquisition unit, a sign language acquisition unit, a conversion unit, and a notification unit. The speech acquisition unit acquires speech. The sign language acquisition unit acquires sign language. The conversion unit converts speech acquired by the speech acquisition unit into text, and converts sign language acquired by the sign language acquisition unit into speech and text. The notification unit notifies a person with hearing loss of the text converted by the conversion unit, and notifies a person with hearing of the speech and text converted by the conversion unit. [Effects of the Invention]

[0007] The system according to the embodiment can enable people with hearing loss and people with hearing loss to communicate smoothly in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A communication system according to an embodiment of the present invention is a system that enables people with hearing loss and people with hearing loss to communicate smoothly in real time, thereby enabling people with different communication styles to exchange information effectively and achieving barrier-free communication.

[0029] A communication system according to an embodiment includes a voice acquisition unit, a sign language acquisition unit, a conversion unit, and a notification unit. The voice acquisition unit acquires voice spoken by a person with hearing. For example, the voice acquisition unit collects voice using a microphone. The voice acquisition unit can also acquire voice using a built-in microphone of a smartphone or tablet. The voice acquisition unit can also acquire clear voice using a microphone with a noise-canceling function. The sign language acquisition unit acquires movements expressed in sign language by a person with hearing loss. For example, the sign language acquisition unit captures sign language movements as video using a camera. The sign language acquisition unit can also capture sign language movements in 3D using multiple cameras. The sign language acquisition unit can also detect sign language movements using a motion sensor. The conversion unit converts voice acquired by the voice acquisition unit into text and converts sign language acquired by the sign language acquisition unit into voice and text. For example, the conversion unit converts voice to text using a generation AI (e.g., text generation AI or multimodal generation AI). The conversion unit can also analyze sign language using sign language recognition technology and generate corresponding speech and text. The conversion unit can also analyze the content of the speech and sign language using natural language processing technology and perform appropriate conversion. The notification unit notifies a person with hearing loss of the text converted by the conversion unit, and notifies a person with hearing loss of the speech and text converted by the conversion unit. For example, the notification unit displays the text on a display. The notification unit can also output speech from a speaker. The notification unit can also notify a person with hearing loss using a vibration notification function. As a result, the communication system according to the embodiment enables people with different communication styles to exchange information effectively and realizes barrier-free communication.

[0030] The conversion unit automatically removes background sounds or noise from the voice data, enabling more accurate text conversion. For example, the conversion unit collects voice data and develops algorithms to filter background sounds and noise. For example, it can extract clear voice even in noisy environments, improving the accuracy of text conversion. The conversion unit also incorporates noise-canceling functionality into voice recognition technology to build a system that removes noise in real time. For example, it automatically removes environmental sounds from conference rooms and cafes. The conversion unit also applies noise reduction technology as preprocessing for the voice data, enabling the generative AI to convert voice to text more accurately. For example, it removes background music and wind noise. This removes background sounds and noise, improving the accuracy of text conversion.

[0031] The conversion unit can identify the speaker of an audio signal and display text in a different format or color for each speaker. For example, the conversion unit develops an algorithm to identify speakers from audio data and displays text in a different format for each speaker. For example, speaker A's text is displayed in blue and speaker B's text in red. The conversion unit also uses the speaker identification function to build a system that displays text in different fonts and styles for each speaker during a meeting or discussion. For example, bold or italic text may be used for each speaker. The conversion unit also uses speech recognition technology to analyze the speaker's voiceprint and display text customized for each speaker. For example, the color or format of the text may be changed depending on the speaker's position or role. This makes it easier to visually identify who is speaking by displaying text in a different format or color for each speaker.

[0032] The conversion unit can translate the results of speech-to-text conversion into different languages ​​in real time, enabling multilingual communication. For example, the conversion unit combines speech recognition technology and translation technology to build a system that translates the results of speech-to-text conversion into different languages ​​in real time. For example, it converts English speech into Japanese text. The conversion unit also develops a multilingual speech recognition algorithm and performs translation simultaneously with speech-to-text conversion. For example, it can instantly translate what is spoken in English during a meeting into French. The conversion unit also analyzes speech data and adds a function that allows the generation AI to translate into different languages ​​in real time. For example, it can be used as a tool to understand the local language while traveling. This enables multilingual communication by translating into different languages ​​in real time.

[0033] The conversion unit can analyze the voice data, estimate the speaker's health condition (e.g., fatigue and stress), and provide appropriate advice. For example, the conversion unit develops an algorithm that analyzes the speaker's fatigue and stress from the voice data and estimates the health condition. For example, it evaluates the degree of fatigue based on the tone and speed of the voice. The conversion unit also analyzes the speaker's voice pattern and builds a system that estimates the stress level. For example, it detects voice tremors and pitch fluctuations to identify signs of stress. The conversion unit also adds a function that provides appropriate advice based on the estimated health condition. For example, if fatigue is detected, it displays a message urging the speaker to take a break. This makes it possible to estimate the speaker's health condition and provide appropriate advice, thereby enabling health management.

[0034] The sign language acquisition unit can incorporate 3D motion capture technology using multiple cameras to capture sign language movements with higher accuracy. For example, the sign language acquisition unit may use multiple cameras to capture sign language movements in 3D and build a system that achieves highly accurate sign language recognition. For example, the sign language movements are captured from different angles and detailed motion analysis is performed. The sign language acquisition unit may also use 3D motion capture technology to develop algorithms that accurately recognize subtle sign language movements and gestures. For example, finger movements and hand positions are analyzed with high accuracy. The sign language acquisition unit may also synchronize multiple cameras to build a system that captures sign language movements in 3D in real time. For example, sign language movements are captured in three dimensions, enabling more natural speech and text conversion. This enables more accurate sign language recognition by capturing sign language movements with high accuracy.

[0035] The conversion unit can improve the generative AI's algorithm for understanding the context of sign language and converting it into natural-sounding sentences. The conversion unit, for example, analyzes the context of sign language and improves the generative AI's algorithm for converting it into natural-sounding sentences. For example, it understands the context of sign language and generates sentences with appropriate grammar and word order. The conversion unit also builds a system that infers context from sign language movements and converts it into natural-sounding sentences. For example, it analyzes the meaning of sign language and automatically generates appropriate expressions and phrases. The conversion unit also introduces natural language processing technology to understand the context of sign language and improves the generative AI's algorithm. For example, it interprets the meaning of sign language according to the context and generates natural-sounding sentences. This enables more natural communication by understanding the context of sign language and converting it into natural-sounding sentences.

[0036] The sign language acquisition unit can reflect sign language movements in real time on an animated character and display them in a visually easy-to-understand format. The sign language acquisition unit, for example, builds a system that captures sign language movements in real time and reflects them on an animated character. For example, the sign language movements are displayed as character movements. The sign language acquisition unit also uses an animated character to display the sign language movements in a visually easy-to-understand format. For example, the sign language movements are reproduced as character gestures. The sign language acquisition unit also develops an algorithm that reflects sign language movements on an animated character in real time and displays them visually. For example, the sign language movements are displayed as character movements in real time. In this way, by reflecting the sign language movements on an animated character, it is possible to display them in a visually easy-to-understand format.

[0037] The sign language acquisition unit can analyze sign language movements and add functions that can be used as a sign language learning support tool. The sign language acquisition unit, for example, analyzes sign language movements and builds a system that can be used as a sign language learning support tool. For example, it records sign language movements so that learners can review them. The sign language acquisition unit also analyzes sign language movements and adds functions that can be used as a sign language learning support tool. For example, it breaks down and displays sign language movements to make them easier for learners to understand. The sign language acquisition unit also analyzes sign language movements and develops algorithms that can be used as a sign language learning support tool. For example, it analyzes sign language movements in real time and provides feedback to learners. In this way, analyzing sign language movements and using them as a learning support tool makes it easier to learn sign language.

[0038] The conversion unit can develop algorithms that optimize the processing speed of the generation AI to minimize delays in two-way communication. For example, the conversion unit develops algorithms that optimize the processing speed of the generation AI and minimize delays in two-way communication. For example, it parallelizes the processing of speech recognition and sign language recognition to achieve real-time conversion. The conversion unit also introduces compression technology to improve the efficiency of data transfer and optimizes the processing speed of the generation AI. For example, it compresses audio data and video data to reduce communication delays. The conversion unit also makes the algorithms of the generation AI lighter and improves processing speed. For example, it omits unnecessary calculations to achieve real-time two-way communication. This minimizes delays in two-way communication, enabling smoother dialogue.

[0039] The conversion unit can add a function that automatically records the content of two-way communication and makes it possible to refer to it later. For example, the conversion unit builds a system that automatically records the content of two-way communication and makes it possible to refer to it later. For example, audio and text data can be stored in the cloud and made accessible when needed. The conversion unit also adds a function that records the content of communication in real time and makes it possible to search and play it back later. For example, the content of a meeting can be recorded and played back later for review. The conversion unit also develops a system that analyzes recorded communication data and automatically extracts important points. For example, it extracts key points and keywords from the conversation and provides a summary. In this way, the content of two-way communication can be recorded and made possible to refer to it later, so that important information cannot be missed.

[0040] The conversion unit can translate the content of two-way communication into different languages ​​in real time, enabling multilingual communication. For example, the conversion unit builds a system that translates the content of two-way communication into different languages ​​in real time. For example, content spoken in English can be instantly translated into Japanese and displayed as text. The conversion unit also develops multilingual translation algorithms and translates the content of two-way communication in real time. For example, this allows participants who speak different languages ​​during a meeting to communicate smoothly. The conversion unit also uses generative AI with built-in translation functionality to convert the content of two-way communication into different languages ​​in real time. For example, it can be used as a tool to understand local languages ​​while traveling. This enables multilingual communication by translating into different languages ​​in real time.

[0041] The conversion unit can add a function that analyzes the content of two-way communication and automatically summarizes the important points. For example, the conversion unit may analyze the content of two-way communication and build a system that automatically summarizes the important points. For example, it may extract the main points and keywords of the conversation and provide a summary. The conversion unit may also analyze the content of communication in real time and develop an algorithm that automatically summarizes the important points. For example, it may summarize the content of a meeting and make it available for later reference. The conversion unit may also use a generative AI that incorporates a summarization function to summarize the content of two-way communication in real time. For example, it may make it possible to understand long conversations in a short amount of time. This allows the important points to be grasped efficiently by summarizing the content of two-way communication.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The communication system can further include a health management unit that monitors the user's health condition. For example, the health management unit can analyze the user's stress level and fatigue level from their voice data and sign language movements, and provide appropriate advice. Specifically, it can analyze the tone and speed of the user's voice, and if stress levels are high, it can display a message encouraging the user to relax. It can also notify the user to take a break if their sign language movements become sluggish. This allows the user's health condition to be monitored in real time, and appropriate advice can be provided, enabling health management.

[0044] The communication system may further include a learning support unit that supports the user's learning. For example, the learning support unit may analyze sign language movements and provide a function that can be used as a sign language learning support tool. Specifically, the learning support unit may record sign language movements so that learners can review them. It may also be able to break down and display sign language movements to make them easier for learners to understand. Furthermore, the learning support unit may also be equipped with a function that analyzes sign language movements in real time and provides feedback to learners. This makes learning sign language easier and helps learners improve their skills.

[0045] The communication system may further include an animation display unit that reflects the user's sign language movements on an animated character in real time and displays them in a visually easy-to-understand format. For example, the animation display unit may build a system that captures sign language movements in real time and reflects them on an animated character. Specifically, the sign language movements are displayed as the character's movements, providing a visually easy-to-understand format. The animation display unit may also reproduce the sign language movements as the character's gestures, making the content of the sign language more intuitively understandable. In this way, the sign language movements can be reflected on the animated character, allowing them to be displayed in a visually easy-to-understand format.

[0046] The communication system can further include a context analysis unit that analyzes the user's sign language movements, understands the context of the sign language, and converts it into natural-sounding sentences. For example, the context analysis unit analyzes the sign language movements, understands the context of the sign language, and improves the generation AI algorithm that converts it into natural-sounding sentences. Specifically, it understands the context of the sign language and generates sentences with appropriate grammar and word order. The context analysis unit can also analyze the meaning of the sign language and automatically generate appropriate expressions and phrases. Furthermore, the context analysis unit can introduce natural language processing technology to understand the context of the sign language and improve the generation AI algorithm. This enables more natural communication by understanding the context of the sign language and converting it into natural-sounding sentences.

[0047] The communication system may further include a learning support unit that analyzes the user's sign language movements and adds a function that can be used as a sign language learning support tool. For example, the learning support unit may analyze sign language movements and build a system that can be used as a sign language learning support tool. Specifically, the learning support unit may record sign language movements so that learners can review them. It may also be possible to break down and display sign language movements to make them easier for learners to understand. Furthermore, the learning support unit may be equipped with a function that analyzes sign language movements in real time and provides feedback to learners. This makes learning sign language easier and helps learners improve their skills.

[0048] The communication system may further include an animation display unit that reflects the user's sign language movements on an animated character in real time and displays them in a visually easy-to-understand format. For example, the animation display unit may build a system that captures sign language movements in real time and reflects them on an animated character. Specifically, the sign language movements are displayed as the character's movements, providing a visually easy-to-understand format. The animation display unit may also reproduce the sign language movements as the character's gestures, making the content of the sign language more intuitively understandable. In this way, the sign language movements can be reflected on the animated character, allowing them to be displayed in a visually easy-to-understand format.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The voice capture unit captures the voice of a person with hearing. For example, the voice capture unit collects voice using a microphone. Alternatively, the voice can be captured using the built-in microphone of a smartphone or tablet. Furthermore, a microphone with a noise-canceling function can be used to capture clear voice. Step 2: The sign language acquisition unit acquires the sign language movements of the hearing-impaired person. For example, the sign language acquisition unit can capture the sign language movements as video using a camera. It can also capture the sign language movements in 3D using multiple cameras. It can also detect the sign language movements using a motion sensor. Step 3: The conversion unit converts the speech acquired by the speech acquisition unit into text, and converts the sign language acquired by the sign language acquisition unit into speech and text. For example, speech can be converted into text using generation AI (e.g., text generation AI or multimodal generation AI). Sign language can also be analyzed using sign language recognition technology to generate corresponding speech and text. Furthermore, natural language processing technology can be used to analyze the content of the speech and sign language and perform appropriate conversion. Step 4: The notification unit notifies the hearing impaired person of the characters converted by the conversion unit, and notifies the hearing impaired person of the voice and characters converted by the conversion unit. For example, the notification unit displays the text on a display. It can also output voice from a speaker. It can also notify the hearing impaired person using a vibration notification function.

[0051] (Example 2) A communication system according to an embodiment of the present invention is a system that enables people with hearing loss and people with hearing loss to communicate smoothly in real time, thereby enabling people with different communication styles to exchange information effectively and achieving barrier-free communication.

[0052] A communication system according to an embodiment includes a voice acquisition unit, a sign language acquisition unit, a conversion unit, and a notification unit. The voice acquisition unit acquires voice spoken by a person with hearing. For example, the voice acquisition unit collects voice using a microphone. The voice acquisition unit can also acquire voice using a built-in microphone of a smartphone or tablet. The voice acquisition unit can also acquire clear voice using a microphone with a noise-canceling function. The sign language acquisition unit acquires movements expressed in sign language by a person with hearing loss. For example, the sign language acquisition unit captures sign language movements as video using a camera. The sign language acquisition unit can also capture sign language movements in 3D using multiple cameras. The sign language acquisition unit can also detect sign language movements using a motion sensor. The conversion unit converts voice acquired by the voice acquisition unit into text and converts sign language acquired by the sign language acquisition unit into voice and text. For example, the conversion unit converts voice to text using a generation AI (e.g., text generation AI or multimodal generation AI). The conversion unit can also analyze sign language using sign language recognition technology and generate corresponding speech and text. The conversion unit can also analyze the content of the speech and sign language using natural language processing technology and perform appropriate conversion. The notification unit notifies a person with hearing loss of the text converted by the conversion unit, and notifies a person with hearing loss of the speech and text converted by the conversion unit. For example, the notification unit displays the text on a display. The notification unit can also output speech from a speaker. The notification unit can also notify a person with hearing loss using a vibration notification function. As a result, the communication system according to the embodiment enables people with different communication styles to exchange information effectively and realizes barrier-free communication.

[0053] The conversion unit automatically removes background sounds or noise from the voice data, enabling more accurate text conversion. For example, the conversion unit collects voice data and develops algorithms to filter background sounds and noise. For example, it can extract clear voice even in noisy environments, improving the accuracy of text conversion. The conversion unit also incorporates noise-canceling functionality into voice recognition technology to build a system that removes noise in real time. For example, it automatically removes environmental sounds from conference rooms and cafes. The conversion unit also applies noise reduction technology as preprocessing for the voice data, enabling the generative AI to convert voice to text more accurately. For example, it removes background music and wind noise. This removes background sounds and noise, improving the accuracy of text conversion.

[0054] The conversion unit can estimate the speaker's emotions and add expressions corresponding to the emotions (e.g., exclamation marks or emojis) to the text. For example, the conversion unit develops an algorithm that analyzes the speaker's emotions from audio data and calculates an emotion score. For example, it detects emotions such as joy or surprise and adds exclamation marks or emojis to the text. The conversion unit also analyzes the speaker's tone and pitch to build a system that estimates the intensity of the emotion. For example, it adds an exclamation mark to an excited voice and a period to a calm voice. The conversion unit also uses the emotion estimation function to automatically generate text expressions corresponding to the speaker's emotions. For example, it adds a tearful emoji to an emotion of sadness and a smiling emoji to an emotion of joy. This enables richer communication by adding expressions corresponding to the speaker's emotions.

[0055] The conversion unit can identify the speaker of an audio signal and display text in a different format or color for each speaker. For example, the conversion unit develops an algorithm to identify speakers from audio data and displays text in a different format for each speaker. For example, speaker A's text is displayed in blue and speaker B's text in red. The conversion unit also uses the speaker identification function to build a system that displays text in different fonts and styles for each speaker during a meeting or discussion. For example, bold or italic text may be used for each speaker. The conversion unit also uses speech recognition technology to analyze the speaker's voiceprint and display text customized for each speaker. For example, the color or format of the text may be changed depending on the speaker's position or role. This makes it easier to visually identify who is speaking by displaying text in a different format or color for each speaker.

[0056] The conversion unit can translate the results of speech-to-text conversion into different languages ​​in real time, enabling multilingual communication. For example, the conversion unit combines speech recognition technology and translation technology to build a system that translates the results of speech-to-text conversion into different languages ​​in real time. For example, it converts English speech into Japanese text. The conversion unit also develops a multilingual speech recognition algorithm and performs translation simultaneously with speech-to-text conversion. For example, it can instantly translate what is spoken in English during a meeting into French. The conversion unit also analyzes speech data and adds a function that allows the generation AI to translate into different languages ​​in real time. For example, it can be used as a tool to understand the local language while traveling. This enables multilingual communication by translating into different languages ​​in real time.

[0057] The conversion unit can analyze the voice data, estimate the speaker's health condition (e.g., fatigue and stress), and provide appropriate advice. For example, the conversion unit develops an algorithm that analyzes the speaker's fatigue and stress from the voice data and estimates the health condition. For example, it evaluates the degree of fatigue based on the tone and speed of the voice. The conversion unit also analyzes the speaker's voice pattern and builds a system that estimates the stress level. For example, it detects voice tremors and pitch fluctuations to identify signs of stress. The conversion unit also adds a function that provides appropriate advice based on the estimated health condition. For example, if fatigue is detected, it displays a message urging the speaker to take a break. This makes it possible to estimate the speaker's health condition and provide appropriate advice, thereby enabling health management.

[0058] The conversion unit can estimate the speaker's emotions and automatically generate voice feedback (e.g., words of encouragement) according to the emotions. For example, the conversion unit builds a system that analyzes the speaker's emotions from voice data and automatically generates voice feedback according to the emotions. For example, it generates words of encouragement if the speaker is sad. The conversion unit also uses the emotion estimation function to provide appropriate voice feedback according to the speaker's emotions. For example, it generates words of congratulations for emotions of joy, and words encouraging the speaker to stay calm for emotions of anger. The conversion unit also develops an algorithm that analyzes the speaker's emotions in real time and automatically generates voice feedback according to the emotions. For example, it provides voice feedback encouraging the speaker to relax if the speaker is feeling stressed. This allows for more empathetic dialogue by providing voice feedback according to the speaker's emotions.

[0059] The sign language acquisition unit can incorporate 3D motion capture technology using multiple cameras to capture sign language movements with higher accuracy. For example, the sign language acquisition unit may use multiple cameras to capture sign language movements in 3D and build a system that achieves highly accurate sign language recognition. For example, the sign language movements are captured from different angles and detailed motion analysis is performed. The sign language acquisition unit may also use 3D motion capture technology to develop algorithms that accurately recognize subtle sign language movements and gestures. For example, finger movements and hand positions are analyzed with high accuracy. The sign language acquisition unit may also synchronize multiple cameras to build a system that captures sign language movements in 3D in real time. For example, sign language movements are captured in three dimensions, enabling more natural speech and text conversion. This enables more accurate sign language recognition by capturing sign language movements with high accuracy.

[0060] The conversion unit can improve the generative AI's algorithm for understanding the context of sign language and converting it into natural-sounding sentences. The conversion unit, for example, analyzes the context of sign language and improves the generative AI's algorithm for converting it into natural-sounding sentences. For example, it understands the context of sign language and generates sentences with appropriate grammar and word order. The conversion unit also builds a system that infers context from sign language movements and converts it into natural-sounding sentences. For example, it analyzes the meaning of sign language and automatically generates appropriate expressions and phrases. The conversion unit also introduces natural language processing technology to understand the context of sign language and improves the generative AI's algorithm. For example, it interprets the meaning of sign language according to the context and generates natural-sounding sentences. This enables more natural communication by understanding the context of sign language and converting it into natural-sounding sentences.

[0061] The conversion unit can infer a speaker's emotions from sign language movements and generate voice tones and expressions corresponding to the emotions. The conversion unit, for example, builds a system that analyzes a speaker's emotions from sign language movements and generates voice tones and expressions corresponding to the emotions. For example, if the sign language movements are fast, an excited tone is generated. The conversion unit also uses an emotion estimation function to infer a speaker's emotions from sign language movements and generate an appropriate voice tone. For example, if the sign language movements are slow, a calm tone is generated. The conversion unit also analyzes sign language movements in real time and develops an algorithm that automatically generates voice expressions corresponding to the emotions. For example, if the sign language movements are forceful, emphasized voice is generated. In this way, by inferring a speaker's emotions from sign language movements and generating voice tones and expressions corresponding to the emotions, more emotional communication is possible.

[0062] The sign language acquisition unit can reflect sign language movements in real time on an animated character and display them in a visually easy-to-understand format. The sign language acquisition unit, for example, builds a system that captures sign language movements in real time and reflects them on an animated character. For example, the sign language movements are displayed as character movements. The sign language acquisition unit also uses an animated character to display the sign language movements in a visually easy-to-understand format. For example, the sign language movements are reproduced as character gestures. The sign language acquisition unit also develops an algorithm that reflects sign language movements on an animated character in real time and displays them visually. For example, the sign language movements are displayed as character movements in real time. In this way, by reflecting the sign language movements on an animated character, it is possible to display them in a visually easy-to-understand format.

[0063] The sign language acquisition unit can analyze sign language movements and add functions that can be used as a sign language learning support tool. The sign language acquisition unit, for example, analyzes sign language movements and builds a system that can be used as a sign language learning support tool. For example, it records sign language movements so that learners can review them. The sign language acquisition unit also analyzes sign language movements and adds functions that can be used as a sign language learning support tool. For example, it breaks down and displays sign language movements to make them easier for learners to understand. The sign language acquisition unit also analyzes sign language movements and develops algorithms that can be used as a sign language learning support tool. For example, it analyzes sign language movements in real time and provides feedback to learners. In this way, analyzing sign language movements and using them as a learning support tool makes it easier to learn sign language.

[0064] The conversion unit can automatically generate emotional voice feedback according to the sign language movements. For example, the conversion unit builds a system that analyzes the speaker's emotions from the sign language movements and automatically generates voice feedback according to the emotions. For example, if the sign language movements are fast, an excited tone is generated. The conversion unit also uses an emotion estimation function to estimate the speaker's emotions from the sign language movements and provide appropriate voice feedback. For example, if the sign language movements are slow, a calm tone is generated. The conversion unit also analyzes the sign language movements in real time and develops an algorithm that automatically generates voice feedback according to the emotions. For example, if the sign language movements are forceful, emphasized voice is generated. This enables more emotional communication by providing emotional voice feedback according to the sign language movements.

[0065] The conversion unit can develop algorithms that optimize the processing speed of the generation AI to minimize delays in two-way communication. For example, the conversion unit develops algorithms that optimize the processing speed of the generation AI and minimize delays in two-way communication. For example, it parallelizes the processing of speech recognition and sign language recognition to achieve real-time conversion. The conversion unit also introduces compression technology to improve the efficiency of data transfer and optimizes the processing speed of the generation AI. For example, it compresses audio data and video data to reduce communication delays. The conversion unit also makes the algorithms of the generation AI lighter and improves processing speed. For example, it omits unnecessary calculations to achieve real-time two-way communication. This minimizes delays in two-way communication, enabling smoother dialogue.

[0066] The conversion unit can add a function that automatically records the content of two-way communication and makes it possible to refer to it later. For example, the conversion unit builds a system that automatically records the content of two-way communication and makes it possible to refer to it later. For example, audio and text data can be stored in the cloud and made accessible when needed. The conversion unit also adds a function that records the content of communication in real time and makes it possible to search and play it back later. For example, the content of a meeting can be recorded and played back later for review. The conversion unit also develops a system that analyzes recorded communication data and automatically extracts important points. For example, it extracts key points and keywords from the conversation and provides a summary. In this way, the content of two-way communication can be recorded and made possible to refer to it later, so that important information cannot be missed.

[0067] The conversion unit uses the emotion estimation function to display the other party's emotions in real time during two-way communication, thereby promoting more empathetic dialogue. For example, the conversion unit builds a system that uses the emotion estimation function to display the other party's emotions in real time during two-way communication. For example, the conversion unit displays the other party's emotion score on a screen to improve the quality of the dialogue. The conversion unit also uses the emotion estimation function to analyze the other party's emotions in real time and promote empathetic dialogue. For example, if the other party is sad, words of encouragement are displayed. The conversion unit also develops a system that provides feedback in real time based on the emotion estimation data during two-way communication. For example, an appropriate response is suggested according to the other party's emotions. This enables more empathetic dialogue by displaying the other party's emotions in real time.

[0068] The conversion unit can translate the content of two-way communication into different languages ​​in real time, enabling multilingual communication. For example, the conversion unit builds a system that translates the content of two-way communication into different languages ​​in real time. For example, content spoken in English can be instantly translated into Japanese and displayed as text. The conversion unit also develops multilingual translation algorithms and translates the content of two-way communication in real time. For example, this allows participants who speak different languages ​​during a meeting to communicate smoothly. The conversion unit also uses generative AI with built-in translation functionality to convert the content of two-way communication into different languages ​​in real time. For example, it can be used as a tool to understand local languages ​​while traveling. This enables multilingual communication by translating into different languages ​​in real time.

[0069] The conversion unit can add a function that analyzes the content of two-way communication and automatically summarizes the important points. For example, the conversion unit may analyze the content of two-way communication and build a system that automatically summarizes the important points. For example, it may extract the main points and keywords of the conversation and provide a summary. The conversion unit may also analyze the content of communication in real time and develop an algorithm that automatically summarizes the important points. For example, it may summarize the content of a meeting and make it available for later reference. The conversion unit may also use a generative AI that incorporates a summarization function to summarize the content of two-way communication in real time. For example, it may make it possible to understand long conversations in a short amount of time. This allows the important points to be grasped efficiently by summarizing the content of two-way communication.

[0070] The conversion unit can use the emotion estimation function to provide emotional feedback at appropriate times during two-way communication. For example, the conversion unit uses the emotion estimation function to build a system that provides emotional feedback at appropriate times during two-way communication. For example, if the other party is happy, it displays words of congratulations. The conversion unit also provides feedback in real time based on the emotion estimation data during two-way communication. For example, if the other party is sad, it displays words of encouragement. The conversion unit also uses the emotion estimation function to develop an algorithm that provides emotional feedback at appropriate times during two-way communication. For example, it suggests an appropriate response according to the other party's emotions. This allows for more empathetic dialogue by providing emotional feedback at appropriate times.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The communication system can further include a health management unit that monitors the user's health condition. For example, the health management unit can analyze the user's stress level and fatigue level from their voice data and sign language movements, and provide appropriate advice. Specifically, it can analyze the tone and speed of the user's voice, and if stress levels are high, it can display a message encouraging the user to relax. It can also notify the user to take a break if their sign language movements become sluggish. This allows the user's health condition to be monitored in real time, and appropriate advice can be provided, enabling health management.

[0073] The communication system may further include an emotion feedback unit that estimates the user's emotion and provides appropriate feedback based on the estimated emotion. For example, the emotion feedback unit may analyze the user's emotion from voice data or sign language movements and provide feedback according to the emotion. Specifically, if the user is happy, a congratulatory message may be displayed, and if the user is sad, words of encouragement may be displayed. The emotion feedback unit may also generate voice tones and expressions according to the user's emotion, promoting more empathetic dialogue. This allows for richer communication by providing feedback according to the user's emotion.

[0074] The communication system may further include a learning support unit that supports the user's learning. For example, the learning support unit may analyze sign language movements and provide a function that can be used as a sign language learning support tool. Specifically, the learning support unit may record sign language movements so that learners can review them. It may also be able to break down and display sign language movements to make them easier for learners to understand. Furthermore, the learning support unit may also be equipped with a function that analyzes sign language movements in real time and provides feedback to learners. This makes learning sign language easier and helps learners improve their skills.

[0075] The communication system may further include an audio feedback unit that estimates the user's emotions and provides appropriate audio feedback based on the estimated emotions. For example, the audio feedback unit analyzes the user's emotions from their voice data and sign language movements and provides audio feedback according to the emotions. Specifically, if the user is excited, the audio feedback unit generates audio encouraging the user to calm down, and if the user is sad, the audio feedback unit generates words of encouragement. The audio feedback unit may also generate audio tones and expressions according to the user's emotions, promoting more empathetic dialogue. This allows for richer communication by providing audio feedback according to the user's emotions.

[0076] The communication system may further include an advice unit that estimates the user's emotions and provides appropriate advice based on the estimated emotions. For example, the advice unit may analyze the user's emotions from their voice data or sign language movements and provide advice according to the emotions. Specifically, if the user feels stressed, the advice unit may display advice encouraging relaxation, and if the user feels tired, the advice unit may display a message encouraging them to take a break. The advice unit may also suggest appropriate actions according to the user's emotions, and may support health management and stress reduction. In this way, by providing advice according to the user's emotions, it is possible to support a healthier lifestyle.

[0077] The communication system may further include an animation display unit that reflects the user's sign language movements on an animated character in real time and displays them in a visually easy-to-understand format. For example, the animation display unit may build a system that captures sign language movements in real time and reflects them on an animated character. Specifically, the sign language movements are displayed as the character's movements, providing a visually easy-to-understand format. The animation display unit may also reproduce the sign language movements as the character's gestures, making the content of the sign language more intuitively understandable. In this way, the sign language movements can be reflected on the animated character, allowing them to be displayed in a visually easy-to-understand format.

[0078] The communication system can further include a context analysis unit that analyzes the user's sign language movements, understands the context of the sign language, and converts it into natural-sounding sentences. For example, the context analysis unit analyzes the sign language movements, understands the context of the sign language, and improves the generation AI algorithm that converts it into natural-sounding sentences. Specifically, it understands the context of the sign language and generates sentences with appropriate grammar and word order. The context analysis unit can also analyze the meaning of the sign language and automatically generate appropriate expressions and phrases. Furthermore, the context analysis unit can introduce natural language processing technology to understand the context of the sign language and improve the generation AI algorithm. This enables more natural communication by understanding the context of the sign language and converting it into natural-sounding sentences.

[0079] The communication system may further include a learning support unit that analyzes the user's sign language movements and adds a function that can be used as a sign language learning support tool. For example, the learning support unit may analyze sign language movements and build a system that can be used as a sign language learning support tool. Specifically, the learning support unit may record sign language movements so that learners can review them. It may also be possible to break down and display sign language movements to make them easier for learners to understand. Furthermore, the learning support unit may be equipped with a function that analyzes sign language movements in real time and provides feedback to learners. This makes learning sign language easier and helps learners improve their skills.

[0080] The communication system may further include an emotion feedback unit that estimates the user's emotion and provides appropriate feedback based on the estimated emotion. For example, the emotion feedback unit may analyze the user's emotion from voice data or sign language movements and provide feedback according to the emotion. Specifically, if the user is happy, a congratulatory message may be displayed, and if the user is sad, words of encouragement may be displayed. The emotion feedback unit may also generate voice tones and expressions according to the user's emotion, promoting more empathetic dialogue. This allows for richer communication by providing feedback according to the user's emotion.

[0081] The communication system may further include an animation display unit that reflects the user's sign language movements on an animated character in real time and displays them in a visually easy-to-understand format. For example, the animation display unit may build a system that captures sign language movements in real time and reflects them on an animated character. Specifically, the sign language movements are displayed as the character's movements, providing a visually easy-to-understand format. The animation display unit may also reproduce the sign language movements as the character's gestures, making the content of the sign language more intuitively understandable. In this way, the sign language movements can be reflected on the animated character, allowing them to be displayed in a visually easy-to-understand format.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The voice capture unit captures the voice of a person with hearing. For example, the voice capture unit collects voice using a microphone. Alternatively, the voice can be captured using the built-in microphone of a smartphone or tablet. Furthermore, a microphone with a noise-canceling function can be used to capture clear voice. Step 2: The sign language acquisition unit acquires the sign language movements of the hearing-impaired person. For example, the sign language acquisition unit can capture the sign language movements as video using a camera. It can also capture the sign language movements in 3D using multiple cameras. It can also detect the sign language movements using a motion sensor. Step 3: The conversion unit converts the speech acquired by the speech acquisition unit into text, and converts the sign language acquired by the sign language acquisition unit into speech and text. For example, speech can be converted into text using generation AI (e.g., text generation AI or multimodal generation AI). Sign language can also be analyzed using sign language recognition technology to generate corresponding speech and text. Furthermore, natural language processing technology can be used to analyze the content of the speech and sign language and perform appropriate conversion. Step 4: The notification unit notifies the hearing impaired person of the characters converted by the conversion unit, and notifies the hearing impaired person of the voice and characters converted by the conversion unit. For example, the notification unit displays the text on a display. It can also output voice from a speaker. It can also notify the hearing impaired person using a vibration notification function.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a voice acquisition unit that acquires voice; a sign language acquisition unit for acquiring sign language; a conversion unit that converts the speech acquired by the speech acquisition unit into text and converts the sign language acquired by the sign language acquisition unit into speech and text; a notification unit that notifies a person who has no hearing of the characters converted by the conversion unit and notifies a person who has hearing of the voice and characters converted by the conversion unit. A system characterized by:

2. The conversion unit Automatically remove background sounds or noise from audio data for more accurate transcription 2. The system of claim 1.

3. The conversion unit Estimate the speaker's emotion and add expressions (e.g., exclamation marks or emojis) to the text according to the emotion.

2. The system of claim 1.

4. The conversion unit Identifying the speaker of the audio and displaying text in a different format or color for each speaker 2. The system of claim 1.

5. The conversion unit The results of the speech-to-text conversion are translated into different languages ​​in real time, enabling multilingual communication.

2. The system of claim 1.

6. The conversion unit Analyzes voice data, estimates the speaker's health condition (e.g., fatigue or stress), and provides appropriate advice 2. The system of claim 1.

7. The conversion unit Estimate the speaker's emotions and automatically generate voice feedback (e.g., encouraging words) according to the emotions.

2. The system of claim 1.

8. The sign language acquisition unit In order to capture the sign language movements with greater precision, 3D motion capture technology using multiple cameras will be introduced.

2. The system of claim 1.

Images