System

Abstract

An object of the system according to the embodiment is to facilitate communication with people who have hearing or visual disabilities or people who speak different languages.SOLUTION: A system according to an embodiment includes a sign language recognition unit, a speech generation unit, a speech recognition unit, a translation unit, a text generation unit, and a character recognition unit. The sign language recognition unit recognizes sign language. The speech generation unit converts the sign language recognized by the sign language recognition unit into speech. The speech recognition unit recognizes English speech. The translation unit converts the English speech recognized by the speech recognition unit into Japanese speech. The text generation unit converts the voice into text. The character recognition unit recognizes French characters. The translation unit converts the French characters recognized by the character recognition unit into Japanese characters.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to communicate with people who are hearing or visually impaired or who speak different languages.

[0005] The system according to the embodiment aims to facilitate communication between people with hearing or visual impairments and people who speak different languages. [Means for solving the problem]

[0006] The system according to the embodiment includes a sign language recognition unit, a speech generation unit, a speech recognition unit, a translation unit, a text generation unit, and a character recognition unit. The sign language recognition unit recognizes sign language. The speech generation unit converts the sign language recognized by the sign language recognition unit into speech. The speech recognition unit recognizes English speech. The translation unit converts the English speech recognized by the speech recognition unit into Japanese speech. The text generation unit converts speech into text. The character recognition unit recognizes French characters. The translation unit converts the French characters recognized by the character recognition unit into Japanese characters. [Effects of the Invention]

[0007] The system according to the embodiment can facilitate communication between people with hearing or visual impairments and people who speak different languages. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A wearable eyeglasses device according to an embodiment of the present invention is a system that supports communication with people with hearing or visual impairments and communication between different languages. This system has the function of translating various expressions, such as converting sign language to speech, English speech to Japanese speech, speech to text, and French characters to Japanese characters. This allows the wearable eyeglasses device to facilitate communication with people with hearing or visual impairments and facilitate communication between different languages.

[0029] The eyeglass-type wearable device according to the embodiment includes a sign language recognition unit, a speech generation unit, a speech recognition unit, a translation unit, a text generation unit, and a character recognition unit. The sign language recognition unit recognizes sign language. For example, the sign language recognition unit captures sign language movements with a camera and analyzes the movements to recognize the sign language. The sign language recognition unit can also recognize sign language movements in real time. The sign language recognition unit can also use multiple cameras to recognize sign language movements with high accuracy. The speech generation unit converts the sign language recognized by the sign language recognition unit into speech. For example, the speech generation unit converts sign language into speech using a generation AI (e.g., a text generation AI or a multimodal generation AI). The speech generation unit can also generate appropriate speech depending on the content of the sign language. The speech generation unit can also generate natural speech based on the sign language movements. The speech recognition unit recognizes English speech. For example, the speech recognition unit analyzes English speech data and recognizes the content. The speech recognition unit can also recognize English speech in real time. The speech recognition unit may also use noise canceling technology to recognize English speech with high accuracy. The translation unit converts the English speech recognized by the speech recognition unit into Japanese speech. For example, the translation unit may use a generative AI to convert English speech into Japanese speech. The translation unit may also generate appropriate Japanese speech according to the content of the English speech. The translation unit may also convert English speech into natural-looking Japanese speech. The text generation unit converts speech into text. For example, the text generation unit may use a generative AI to convert speech into text. The text generation unit may also generate appropriate text according to the content of the speech. The text generation unit may also convert speech into natural-looking text. The character recognition unit recognizes French characters. For example, the character recognition unit analyzes French character data and recognizes its content. The character recognition unit may also recognize French characters in real time. The character recognition unit may also use OCR technology to recognize French characters with high accuracy. The translation unit converts the French characters recognized by the character recognition unit into Japanese characters.For example, the translation unit uses a generation AI to convert French characters into Japanese characters. The translation unit can also generate appropriate Japanese characters based on the content of the French characters. The translation unit can also convert French characters into natural-looking Japanese characters. This allows the eyeglass-type wearable device according to the embodiment to support communication with people with hearing or visual impairments and communication between different languages. For example, people who use sign language can communicate by voice, and people who speak English and people who speak Japanese can have a real-time conversation. Furthermore, converting voice to text makes it easier for people with hearing impairments to understand the content of the conversation. Furthermore, converting French characters into Japanese characters allows people to understand documents in different languages.

[0030] The sign language recognition unit can simultaneously analyze not only hand movements but also facial expressions and body movements, thereby generating more natural speech. For example, when capturing sign language movements, the sign language recognition unit simultaneously analyzes not only hand movements but also facial expressions and body movements. For example, when expressing "thank you" in sign language, the unit analyzes the smiling face and forward leaning posture of the body to generate a more natural speech of "thank you." In addition, when analyzing sign language movements, the sign language recognition unit can also analyze facial expressions and body movements in real time. In addition, when analyzing sign language movements, the sign language recognition unit can also use multiple cameras to analyze facial expressions and body movements with high accuracy. This allows sign language movements to be converted into speech more naturally.

[0031] The sign language recognition unit can learn the individual habits and styles of sign language users and generate individually optimized speech. The sign language recognition unit, for example, learns the individual habits and styles of sign language users and generates individually optimized speech. For example, the sign language recognition unit learns the speed and angle of hand movements when a specific user expresses "thank you" and generates a speech of "thank you" optimized for that user. The sign language recognition unit can also use generation AI to learn the individual habits and styles of sign language users. The sign language recognition unit can also use machine learning algorithms to learn the individual habits and styles of sign language users. This makes it possible to generate speech optimized for the individual habits and styles of the user.

[0032] The sign language recognition unit can not only convert sign language to speech, but also convert sign language to text at the same time, and provide visual feedback. For example, the sign language recognition unit can not only convert sign language to speech, but also convert sign language to text at the same time, and provide visual feedback. For example, when "hello" is expressed in sign language, the unit outputs "hello" as speech and simultaneously displays "hello" in text. The sign language recognition unit can also convert sign language to speech and text in real time. The sign language recognition unit can also use generative AI to convert sign language to speech and text with high accuracy. This allows the unit to convert sign language to speech and text at the same time, and provide visual feedback.

[0033] The sign language recognition unit can support multiple sign languages ​​and enable translation between different sign languages. The sign language recognition unit can support multiple sign languages ​​(e.g., American Sign Language, Japanese Sign Language) and enable translation between different sign languages. For example, when "thank you" is expressed in American Sign Language, it can be translated into "thank you" in Japanese Sign Language and output as voice. The sign language recognition unit can also use generative AI to support multiple sign languages. The sign language recognition unit can also use machine learning algorithms to support multiple sign languages. This enables support for multiple sign languages ​​and translation between different sign languages.

[0034] When recognizing English speech, the speech recognition unit can generate more natural Japanese speech based on the speaker's accent and dialect. For example, when recognizing English speech, the speech recognition unit takes the speaker's accent and dialect into account to generate more natural Japanese speech. For example, it recognizes "How are you?" from a speaker with a British English accent and outputs it naturally in Japanese as "How are you?". The speech recognition unit can also analyze the speaker's accent and dialect in real time when recognizing English speech. The speech recognition unit can also use generative AI to analyze the speaker's accent and dialect with high accuracy when recognizing English speech. This allows the speaker's accent and dialect to be taken into account to generate more natural Japanese speech.

[0035] The speech recognition unit can not only perform the conversion from English speech to Japanese speech, but also simultaneously perform the conversion from English speech to Japanese text, and provide visual feedback. For example, the speech recognition unit not only performs the conversion from English speech to Japanese speech, but also simultaneously performs the conversion from English speech to Japanese text, and provides visual feedback. For example, when someone says "How are you?" in English, it outputs "お元気ですか?" in Japanese speech and displays "お元気ですか?" in text at the same time. Also, the speech recognition unit can perform the conversion from English speech to Japanese speech and Japanese text in real time. In addition, the speech recognition unit can also use generative AI to perform the conversion from English speech to Japanese speech and Japanese text with high precision. Thereby, it can simultaneously perform the conversion from English speech to Japanese speech and Japanese text and provide visual feedback.

[0036] The speech recognition unit also supports voice conversion from other languages than English to Japanese, and can facilitate communication between multiple languages. For example, the speech recognition unit supports voice conversion from other languages than English (for example, Chinese, Spanish) to Japanese, and promotes communication between multiple languages. For example, when someone says "?好" in Chinese, it outputs "こんにちは" in Japanese speech. Also, the speech recognition unit can perform the voice conversion from other languages than English to Japanese in real time. In addition, the speech recognition unit can also use generative AI to perform the voice conversion from other languages than English to Japanese with high precision. Thereby, it can facilitate communication between multiple languages.

[0037] The text generation unit can generate more accurate text based on the speaker's accent and dialect when converting speech to text. For example, the text generation unit generates more accurate text by taking into account the speaker's accent and dialect when converting speech to text. For example, the Kansai dialect phrase "Nandeyanen" can be accurately converted into "Doshutedayo." The text generation unit can also analyze the speaker's accent and dialect in real time when converting speech to text. The text generation unit can also use generative AI to analyze the speaker's accent and dialect with high accuracy when converting speech to text. This allows the speaker's accent and dialect to be taken into account and more accurate text to be generated.

[0038] The text generation unit can provide more appropriate text based on contextual information and related background knowledge to understand the content of the speech. For example, the text generation unit can refer to contextual information and related background knowledge to understand the content of the speech and provide more appropriate text. For example, the text generation unit can analyze the context before and after the conversation and appropriately convert "It's nice weather today" into "The weather is nice today." The text generation unit can also refer to contextual information and related background knowledge in real time to understand the content of the speech. The text generation unit can also use generative AI to refer to contextual information and related background knowledge with high accuracy to understand the content of the speech. This allows the text generation unit to refer to contextual information and background knowledge and provide more appropriate text.

[0039] The text generation unit not only converts speech to text but also converts speech to sign language at the same time, thereby providing feedback to the hearing impaired. The text generation unit not only converts speech to text but also converts speech to sign language at the same time, thereby providing feedback to the hearing impaired. For example, when someone says "hello" in speech, it displays "hello" in text and simultaneously expresses "hello" in sign language. The text generation unit can also convert speech to text and sign language in real time. The text generation unit can also use generative AI to convert speech to text and sign language with high accuracy. This allows speech to be converted to text and sign language at the same time, thereby providing feedback to the hearing impaired.

[0040] The text generation unit can support multiple languages ​​when converting speech to text and can also enable text conversion between different languages. For example, the text generation unit can support multiple languages ​​when converting speech to text and can also enable text conversion between different languages. For example, English speech can be converted into Japanese text. The text generation unit can also support multiple languages ​​in real time when converting speech to text. The text generation unit can also use generative AI to support multiple languages ​​with high accuracy when converting speech to text. This makes it possible to support multiple languages ​​and enable text conversion between different languages.

[0041] The character recognition unit takes into account handwriting and different font styles when recognizing French characters, allowing it to generate more accurate Japanese characters. For example, the character recognition unit can accurately convert the handwritten "Bonjour" into the Japanese character "Hello." The character recognition unit can also analyze handwriting and different font styles in real time when recognizing French characters. The character recognition unit can also use generative AI to analyze handwriting and different font styles with high accuracy when recognizing French characters. This allows it to generate more accurate Japanese characters by taking into account handwriting and different font styles.

[0042] The character recognition unit can not only convert French characters to Japanese characters, but also convert the French characters to Japanese speech simultaneously, providing both visual and auditory feedback. For example, the character recognition unit can not only convert French characters to Japanese characters, but also convert the French characters to Japanese speech simultaneously, providing both visual and auditory feedback. For example, the character recognition unit can display the written word "Bonjour" as "Hello" in Japanese characters and simultaneously output "Hello" in Japanese speech. The character recognition unit can also convert French characters to Japanese characters and Japanese speech in real time. The character recognition unit can also use generative AI to convert French characters to Japanese characters and Japanese speech with high accuracy. This allows the character recognition unit to convert French characters to Japanese characters and Japanese speech simultaneously, providing both visual and auditory feedback.

[0043] The character recognition unit also supports character conversion from languages ​​other than French to Japanese, thereby facilitating multilingual document understanding. The character recognition unit also supports character conversion from languages ​​other than French (e.g., German, Italian) to Japanese, thereby facilitating multilingual document understanding. For example, it converts the German "Guten Tag" into the Japanese character "Hello." The character recognition unit can also perform real-time character conversion from languages ​​other than French to Japanese. The character recognition unit can also use generative AI to perform character conversion from languages ​​other than French to Japanese with high accuracy. This can facilitate multilingual document understanding.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The glasses-type wearable device may further include an environmental sound recognition unit. The environmental sound recognition unit recognizes surrounding environmental sounds and provides appropriate feedback to the user. For example, it may recognize the sound of a car horn and issue a warning to the user. The environmental sound recognition unit may also analyze surrounding sounds in real time and provide appropriate information to the user. Furthermore, the environmental sound recognition unit may use noise canceling technology to recognize specific sounds (e.g., ambulance sirens) with high accuracy. This allows the user to respond appropriately to surrounding environmental sounds.

[0046] The sign language recognition unit can be equipped with a function to monitor the health condition of the user who is signing. For example, it can analyze the speed and rhythm of hand movements to estimate the user's level of fatigue. The sign language recognition unit can also detect abnormalities in the user's hand movements and notify the user of changes in their health condition in real time. Furthermore, the sign language recognition unit can use generative AI to monitor the user's health condition with high accuracy. This allows the user's health condition to be constantly monitored and appropriate measures to be taken.

[0047] The glasses-type wearable device may further include a location information acquisition unit. The location information acquisition unit acquires the user's current location and provides appropriate information. For example, if the user is in a tourist spot, information about that location may be displayed. The location information acquisition unit may also acquire the user's current location in real time and provide appropriate navigation. Furthermore, the location information acquisition unit may use GPS technology to acquire the user's current location with high accuracy. This allows the user to obtain information according to their current location.

[0048] The glasses-type wearable device may further include a weather information acquisition unit. The weather information acquisition unit acquires current weather information and provides appropriate feedback to the user. For example, if it looks like it's going to rain, it may notify the user to take an umbrella. The weather information acquisition unit may also acquire current weather information in real time and provide appropriate advice. Furthermore, the weather information acquisition unit may use a weather database to acquire weather information with high accuracy. This allows the user to take appropriate action depending on the current weather.

[0049] The glasses-type wearable device may further include a health management unit. The health management unit monitors the user's health condition and provides appropriate feedback. For example, it measures heart rate and blood pressure and notifies the user if an abnormality is detected. The health management unit may also monitor the user's health condition in real time and provide appropriate advice. Furthermore, the health management unit may use a biosensor to monitor the user's health condition with high accuracy. This allows the user to constantly monitor their own health condition and take appropriate measures.

[0050] The glasses-type wearable device can further include a learning support unit. The learning support unit supports the user's learning and provides appropriate feedback. For example, if the user is learning a foreign language, it can provide advice on the pronunciation and grammar of that language. The learning support unit can also monitor the user's learning progress in real time and propose an appropriate learning plan. Furthermore, the learning support unit can use generative AI to support the user's learning with high accuracy. This allows the user to progress with their learning effectively.

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

[0052] Step 1: The sign language recognition unit recognizes the sign language. For example, the sign language recognition unit captures sign language movements with a camera and analyzes the movements to recognize the sign language. The sign language recognition unit can also recognize sign language movements in real time. Furthermore, the sign language recognition unit can use multiple cameras to recognize sign language movements with high accuracy. Step 2: The speech generation unit converts the sign language recognized by the sign language recognition unit into speech. For example, the speech generation unit converts the sign language into speech using a generation AI (e.g., a text generation AI or a multimodal generation AI). The speech generation unit can also generate appropriate speech depending on the content of the sign language. Furthermore, the speech generation unit can also generate natural speech based on the movement of the sign language. Step 3: The speech recognition unit recognizes English speech. For example, the speech recognition unit analyzes English speech data and recognizes its content. The speech recognition unit can also recognize English speech in real time. Furthermore, the speech recognition unit can use noise canceling technology to recognize English speech with high accuracy. Step 4: The translation unit converts the English speech recognized by the speech recognition unit into Japanese speech. For example, the translation unit uses a generative AI to convert the English speech into Japanese speech. The translation unit can also generate appropriate Japanese speech depending on the content of the English speech. Furthermore, the translation unit can convert the English speech into natural Japanese speech. Step 5: The text generator converts the speech into text. For example, the text generator uses a generative AI to convert speech into text. The text generator can also generate appropriate text depending on the content of the speech. Furthermore, the text generator can convert speech into natural-sounding text. Step 6: The character recognition unit recognizes French characters. For example, the character recognition unit analyzes French character data and recognizes its content. The character recognition unit can also recognize French characters in real time. Furthermore, the character recognition unit can use OCR technology to recognize French characters with high accuracy. Step 7: The translation unit converts the French characters recognized by the character recognition unit into Japanese characters. For example, the translation unit converts the French characters into Japanese characters using a generation AI. The translation unit can also generate appropriate Japanese characters depending on the content of the French characters. Furthermore, the translation unit can convert the French characters into natural Japanese characters.

[0053] (Example 2) A glasses-type wearable device according to an embodiment of the present invention is a system that supports communication with people with hearing or visual impairments and communication between different languages. This system has the function of translating various expressions, such as converting sign language to speech, converting English speech to Japanese speech, converting speech to text, and converting French characters to Japanese characters. This allows the glasses-type wearable device to facilitate communication with people with hearing or visual impairments and to facilitate communication between different languages.

[0054] The eyeglass-type wearable device according to the embodiment includes a sign language recognition unit, a speech generation unit, a speech recognition unit, a translation unit, a text generation unit, and a character recognition unit. The sign language recognition unit recognizes sign language. For example, the sign language recognition unit captures sign language movements with a camera and analyzes the movements to recognize the sign language. The sign language recognition unit can also recognize sign language movements in real time. The sign language recognition unit can also use multiple cameras to recognize sign language movements with high accuracy. The speech generation unit converts the sign language recognized by the sign language recognition unit into speech. For example, the speech generation unit converts sign language into speech using a generation AI (e.g., a text generation AI or a multimodal generation AI). The speech generation unit can also generate appropriate speech depending on the content of the sign language. The speech generation unit can also generate natural speech based on the sign language movements. The speech recognition unit recognizes English speech. For example, the speech recognition unit analyzes English speech data and recognizes the content. The speech recognition unit can also recognize English speech in real time. The speech recognition unit may also use noise canceling technology to recognize English speech with high accuracy. The translation unit converts the English speech recognized by the speech recognition unit into Japanese speech. For example, the translation unit may use a generative AI to convert English speech into Japanese speech. The translation unit may also generate appropriate Japanese speech according to the content of the English speech. The translation unit may also convert English speech into natural-looking Japanese speech. The text generation unit converts speech into text. For example, the text generation unit may use a generative AI to convert speech into text. The text generation unit may also generate appropriate text according to the content of the speech. The text generation unit may also convert speech into natural-looking text. The character recognition unit recognizes French characters. For example, the character recognition unit analyzes French character data and recognizes its content. The character recognition unit may also recognize French characters in real time. The character recognition unit may also use OCR technology to recognize French characters with high accuracy. The translation unit converts the French characters recognized by the character recognition unit into Japanese characters.For example, the translation unit uses a generation AI to convert French characters into Japanese characters. The translation unit can also generate appropriate Japanese characters based on the content of the French characters. The translation unit can also convert French characters into natural-looking Japanese characters. This allows the eyeglass-type wearable device according to the embodiment to support communication with people with hearing or visual impairments and communication between different languages. For example, people who use sign language can communicate by voice, and people who speak English and people who speak Japanese can have a real-time conversation. Furthermore, converting voice to text makes it easier for people with hearing impairments to understand the content of the conversation. Furthermore, converting French characters into Japanese characters allows people to understand documents in different languages.

[0055] The sign language recognition unit can simultaneously analyze not only hand movements but also facial expressions and body movements, thereby generating more natural speech. For example, when capturing sign language movements, the sign language recognition unit simultaneously analyzes not only hand movements but also facial expressions and body movements. For example, when expressing "thank you" in sign language, the unit analyzes the smiling face and forward leaning posture of the body to generate a more natural speech of "thank you." In addition, when analyzing sign language movements, the sign language recognition unit can also analyze facial expressions and body movements in real time. In addition, when analyzing sign language movements, the sign language recognition unit can also use multiple cameras to analyze facial expressions and body movements with high accuracy. This allows sign language movements to be converted into speech more naturally.

[0056] The sign language recognition unit can learn the individual habits and styles of sign language users and generate individually optimized speech. The sign language recognition unit, for example, learns the individual habits and styles of sign language users and generates individually optimized speech. For example, the sign language recognition unit learns the speed and angle of hand movements when a specific user expresses "thank you" and generates a speech of "thank you" optimized for that user. The sign language recognition unit can also use generation AI to learn the individual habits and styles of sign language users. The sign language recognition unit can also use machine learning algorithms to learn the individual habits and styles of sign language users. This makes it possible to generate speech optimized for the individual habits and styles of the user.

[0057] The sign language recognition unit can use the emotion estimation function to estimate the emotion of the user using sign language and generate speech with a tone and intonation that corresponds to that emotion. The sign language recognition unit can, for example, use the emotion estimation function to estimate the emotion of the user using sign language and generate speech with a tone and intonation that corresponds to that emotion. For example, when expressing "thank you" in sign language, if the feeling of gratitude is strong, speech is generated with a brighter tone. The sign language recognition unit can also use the emotion estimation function to estimate the emotion of the user using sign language in real time. The sign language recognition unit can also use the emotion estimation function to use a generation AI to estimate the emotion of the user using sign language with high accuracy. This makes it possible to generate speech with a tone and intonation that corresponds to the user's emotion.

[0058] The sign language recognition unit can not only convert sign language to speech, but also convert sign language to text at the same time, and provide visual feedback. For example, the sign language recognition unit can not only convert sign language to speech, but also convert sign language to text at the same time, and provide visual feedback. For example, when "hello" is expressed in sign language, the unit outputs "hello" as speech and simultaneously displays "hello" in text. The sign language recognition unit can also convert sign language to speech and text in real time. The sign language recognition unit can also use generative AI to convert sign language to speech and text with high accuracy. This allows the unit to convert sign language to speech and text at the same time, and provide visual feedback.

[0059] The sign language recognition unit can support multiple sign languages ​​and enable translation between different sign languages. The sign language recognition unit can support multiple sign languages ​​(e.g., American Sign Language, Japanese Sign Language) and enable translation between different sign languages. For example, when "thank you" is expressed in American Sign Language, it can be translated into "thank you" in Japanese Sign Language and output as voice. The sign language recognition unit can also use generative AI to support multiple sign languages. The sign language recognition unit can also use machine learning algorithms to support multiple sign languages. This enables support for multiple sign languages ​​and translation between different sign languages.

[0060] The sign language recognition unit can use the emotion estimation function to display the emotion of a user using sign language in real time, thereby improving the quality of communication. The sign language recognition unit can use, for example, the emotion estimation function to display the emotion of a user using sign language in real time, thereby improving the quality of communication. For example, when "thank you" is expressed in sign language, if the feeling of gratitude is strong, the recognition unit can display "thanks." The sign language recognition unit can also use a generative AI to display the emotion of a user using sign language in real time using the emotion estimation function. The sign language recognition unit can also use a machine learning algorithm to display the emotion of a user using sign language with high accuracy using the emotion estimation function. This allows the user's emotion to be displayed in real time, improving the quality of communication.

[0061] When recognizing English speech, the speech recognition unit can generate more natural Japanese speech based on the speaker's accent and dialect. For example, when recognizing English speech, the speech recognition unit takes the speaker's accent and dialect into account to generate more natural Japanese speech. For example, it recognizes "How are you?" from a speaker with a British English accent and outputs it naturally in Japanese as "How are you?". The speech recognition unit can also analyze the speaker's accent and dialect in real time when recognizing English speech. The speech recognition unit can also use generative AI to analyze the speaker's accent and dialect with high accuracy when recognizing English speech. This allows the speaker's accent and dialect to be taken into account to generate more natural Japanese speech.

[0062] The speech recognition unit can use the emotion estimation function to estimate the emotion of an English speaker and generate Japanese speech with a tone and intonation that corresponds to that emotion. For example, the speech recognition unit can use the emotion estimation function to estimate the emotion of an English speaker and generate Japanese speech with a tone and intonation that corresponds to that emotion. For example, if the English speaker says "Thank you!" with a strong sense of gratitude, the speech recognition unit can output "Arigatou!" in Japanese with a brighter tone. The speech recognition unit can also use the emotion estimation function to estimate the emotion of an English speaker in real time. The speech recognition unit can also use the emotion estimation function to use a generation AI to estimate the emotion of an English speaker with high accuracy. This makes it possible to generate Japanese speech with a tone and intonation that corresponds to the emotion of the English speaker.

[0063] The speech recognition unit can not only convert English speech to Japanese speech, but also convert English speech to Japanese text at the same time, and provide visual feedback. For example, when someone says "How are you?" in English, the speech recognition unit can output "How are you?" in Japanese as speech and simultaneously display "How are you?" in text. The speech recognition unit can also convert English speech to Japanese speech and Japanese text in real time. The speech recognition unit can also use generative AI to convert English speech to Japanese speech and Japanese text with high accuracy. This allows the speech recognition unit to convert English speech to Japanese speech and Japanese text at the same time, and provide visual feedback.

[0064] The voice recognition unit supports voice conversion from languages other than English to Japanese and can facilitate communication between multiple languages. For example, the voice recognition unit supports voice conversion from languages other than English (such as Chinese, Spanish) to Japanese and promotes communication between multiple languages. For example, when someone says "Hello" in Chinese, it outputs "Konnichiwa" in voice. Also, the voice recognition unit can perform real-time voice conversion from languages other than English to Japanese. In addition, the voice recognition unit can use generative AI to perform high-precision voice conversion from languages other than English to Japanese. This can facilitate communication between multiple languages.

[0065] The voice recognition unit can use the emotion estimation function to display the emotions of English speakers in real time and improve the quality of communication. For example, the voice recognition unit uses the emotion estimation function to display the emotions of English speakers in real time and improve the quality of communication. For example, when someone says "Thank you!" in English, if the feeling of gratitude is strong, it displays "Gratitude". Also, the voice recognition unit can use generative AI to display the emotions of English speakers in real time using the emotion estimation function. In addition, the voice recognition unit can use machine learning algorithms to display the emotions of English speakers with high precision using the emotion estimation function. This can display the emotions of English speakers in real time and improve the quality of communication.

[0066] The text generation unit can generate more accurate text based on the speaker's accent and dialect when converting speech to text. For example, the text generation unit generates more accurate text by taking into account the speaker's accent and dialect when converting speech to text. For example, the Kansai dialect phrase "Nandeyanen" can be accurately converted into "Doshutedayo." The text generation unit can also analyze the speaker's accent and dialect in real time when converting speech to text. The text generation unit can also use generative AI to analyze the speaker's accent and dialect with high accuracy when converting speech to text. This allows the speaker's accent and dialect to be taken into account and more accurate text to be generated.

[0067] The text generation unit can provide more appropriate text based on contextual information and related background knowledge to understand the content of the speech. For example, the text generation unit can refer to contextual information and related background knowledge to understand the content of the speech and provide more appropriate text. For example, the text generation unit can analyze the context before and after the conversation and appropriately convert "It's nice weather today" into "The weather is nice today." The text generation unit can also refer to contextual information and related background knowledge in real time to understand the content of the speech. The text generation unit can also use generative AI to refer to contextual information and related background knowledge with high accuracy to understand the content of the speech. This allows the text generation unit to refer to contextual information and background knowledge and provide more appropriate text.

[0068] The text generation unit can use the emotion estimation function to estimate the speaker's emotion and reflect expressions corresponding to that emotion in the text. The text generation unit can, for example, use the emotion estimation function to estimate the speaker's emotion and reflect expressions corresponding to that emotion in the text. For example, if the voice expresses a strong feeling of gratitude, such as "Thank you!", the emotion can be reflected in the text as "Thank you!" The text generation unit can also use the emotion estimation function to estimate the speaker's emotion in real time. The text generation unit can also use the emotion estimation function to use a generation AI to estimate the speaker's emotion with high accuracy. This allows expressions corresponding to the speaker's emotion to be reflected in the text.

[0069] The text generation unit not only converts speech to text but also converts speech to sign language at the same time, thereby providing feedback to the hearing impaired. The text generation unit not only converts speech to text but also converts speech to sign language at the same time, thereby providing feedback to the hearing impaired. For example, when someone says "hello" in speech, it displays "hello" in text and simultaneously expresses "hello" in sign language. The text generation unit can also convert speech to text and sign language in real time. The text generation unit can also use generative AI to convert speech to text and sign language with high accuracy. This allows speech to be converted to text and sign language at the same time, thereby providing feedback to the hearing impaired.

[0070] The text generation unit can support multiple languages ​​when converting speech to text and can also enable text conversion between different languages. For example, the text generation unit can support multiple languages ​​when converting speech to text and can also enable text conversion between different languages. For example, English speech can be converted into Japanese text. The text generation unit can also support multiple languages ​​in real time when converting speech to text. The text generation unit can also use generative AI to support multiple languages ​​with high accuracy when converting speech to text. This makes it possible to support multiple languages ​​and enable text conversion between different languages.

[0071] The text generation unit can use the emotion estimation function to display the speaker's emotion in real time to help understand the text. The text generation unit can use, for example, the emotion estimation function to display the speaker's emotion in real time to help understand the text. For example, when a voice says "Thank you!", if the feeling of gratitude is strong, it can display "Thanks." The text generation unit can also use generative AI to display the speaker's emotion in real time using the emotion estimation function. The text generation unit can also use a machine learning algorithm to display the speaker's emotion with high accuracy using the emotion estimation function. This makes it possible to display the speaker's emotion in real time to help understand the text.

[0072] The character recognition unit takes into account handwriting and different font styles when recognizing French characters, allowing it to generate more accurate Japanese characters. For example, the character recognition unit can accurately convert the handwritten "Bonjour" into the Japanese character "Hello." The character recognition unit can also analyze handwriting and different font styles in real time when recognizing French characters. The character recognition unit can also use generative AI to analyze handwriting and different font styles with high accuracy when recognizing French characters. This allows it to generate more accurate Japanese characters by taking into account handwriting and different font styles.

[0073] The character recognition unit can use the emotion estimation function to estimate the emotion contained in the content of the French text and reflect an expression corresponding to that emotion in the Japanese text. For example, the character recognition unit can use the emotion estimation function to estimate the emotion contained in the content of the French text and reflect an expression corresponding to that emotion in the Japanese text. For example, if "Merci beaucoup!" expresses a strong feeling of gratitude, the emotion can be reflected as "Thank you so much!". The character recognition unit can also use the emotion estimation function to estimate the emotion contained in the content of the French text in real time. The character recognition unit can also use the emotion estimation function to use a generative AI to estimate the emotion contained in the content of the French text with high accuracy. This makes it possible to estimate the emotion contained in the content of the French text and reflect an expression corresponding to that emotion in the Japanese text.

[0074] The character recognition unit can not only convert French characters to Japanese characters, but also convert the French characters to Japanese speech simultaneously, providing both visual and auditory feedback. For example, the character recognition unit can not only convert French characters to Japanese characters, but also convert the French characters to Japanese speech simultaneously, providing both visual and auditory feedback. For example, the character recognition unit can display the written word "Bonjour" as "Hello" in Japanese characters and simultaneously output "Hello" in Japanese speech. The character recognition unit can also convert French characters to Japanese characters and Japanese speech in real time. The character recognition unit can also use generative AI to convert French characters to Japanese characters and Japanese speech with high accuracy. This allows the character recognition unit to convert French characters to Japanese characters and Japanese speech simultaneously, providing both visual and auditory feedback.

[0075] The character recognition unit also supports character conversion from languages ​​other than French to Japanese, thereby facilitating multilingual document understanding. The character recognition unit also supports character conversion from languages ​​other than French (e.g., German, Italian) to Japanese, thereby facilitating multilingual document understanding. For example, it converts the German "Guten Tag" into the Japanese character "Hello." The character recognition unit can also perform real-time character conversion from languages ​​other than French to Japanese. The character recognition unit can also use generative AI to perform character conversion from languages ​​other than French to Japanese with high accuracy. This can facilitate multilingual document understanding.

[0076] The character recognition unit can use the emotion estimation function to display the emotion contained in the content of French text in real time, thereby assisting in document understanding. The character recognition unit can, for example, use the emotion estimation function to display the emotion contained in the content of French text in real time, thereby assisting in document understanding. For example, if the sentence "Merci beaucoup!" expresses a strong feeling of gratitude, it can display "Thanks." The character recognition unit can also use generative AI to display the emotion contained in the content of French text in real time, using the emotion estimation function. The character recognition unit can also use a machine learning algorithm to display the emotion contained in the content of French text with high accuracy, using the emotion estimation function. This allows the emotion contained in the content of French text to be displayed in real time, thereby assisting in document understanding.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The glasses-type wearable device may further include an environmental sound recognition unit. The environmental sound recognition unit recognizes surrounding environmental sounds and provides appropriate feedback to the user. For example, it may recognize the sound of a car horn and issue a warning to the user. The environmental sound recognition unit may also analyze surrounding sounds in real time and provide appropriate information to the user. Furthermore, the environmental sound recognition unit may use noise canceling technology to recognize specific sounds (e.g., ambulance sirens) with high accuracy. This allows the user to respond appropriately to surrounding environmental sounds.

[0079] The sign language recognition unit can be equipped with a function to monitor the health condition of the user who is signing. For example, it can analyze the speed and rhythm of hand movements to estimate the user's level of fatigue. The sign language recognition unit can also detect abnormalities in the user's hand movements and notify the user of changes in their health condition in real time. Furthermore, the sign language recognition unit can use generative AI to monitor the user's health condition with high accuracy. This allows the user's health condition to be constantly monitored and appropriate measures to be taken.

[0080] The sign language recognition unit can estimate the emotion of the user using sign language and display emoji or icons corresponding to that emotion. For example, when expressing "thank you" in sign language, a heart icon can be displayed if the user is feeling very grateful. The sign language recognition unit can also use the emotion estimation function to estimate the emotion of the user using sign language in real time and display appropriate emoji or icons. Furthermore, the sign language recognition unit can also use generative AI to estimate the emotion of the user using sign language with high accuracy using the emotion estimation function. This allows the user's emotion to be visually expressed, improving the quality of communication.

[0081] The sign language recognition unit can estimate the emotion of the user using sign language and play music that corresponds to that emotion. For example, when the user expresses "happy" in sign language, cheerful music can be played. The sign language recognition unit can also use the emotion estimation function to estimate the emotion of the user using sign language in real time and play appropriate music. Furthermore, the sign language recognition unit can also use the emotion estimation function to use generative AI to estimate the emotion of the user using sign language with high accuracy. This makes it possible to provide music that corresponds to the user's emotion, promoting relaxation and a change of mood.

[0082] The sign language recognition unit can estimate the emotion of the user using sign language and release an aroma corresponding to that emotion. For example, when the user expresses "relaxation" in sign language, the scent of lavender is released. The sign language recognition unit can also use the emotion estimation function to estimate the emotion of the user using sign language in real time and release an appropriate aroma. Furthermore, the sign language recognition unit can also use the emotion estimation function to use a generation AI to estimate the emotion of the user using sign language with high accuracy. This allows the system to provide an aroma corresponding to the user's emotion, promoting relaxation and a change of mood.

[0083] The sign language recognition unit can estimate the emotion of a user using sign language and turn on a light of a color corresponding to that emotion. For example, when expressing "fun" in sign language, a bright colored light is turned on. The sign language recognition unit can also use an emotion estimation function to estimate the emotion of a user using sign language in real time and turn on a light of an appropriate color. Furthermore, the sign language recognition unit can also use a generative AI to estimate the emotion of a user using sign language with high accuracy using the emotion estimation function. This makes it possible to provide a light of a color corresponding to the user's emotion and enhance visual feedback.

[0084] The glasses-type wearable device may further include a location information acquisition unit. The location information acquisition unit acquires the user's current location and provides appropriate information. For example, if the user is in a tourist spot, information about that location may be displayed. The location information acquisition unit may also acquire the user's current location in real time and provide appropriate navigation. Furthermore, the location information acquisition unit may use GPS technology to acquire the user's current location with high accuracy. This allows the user to obtain information according to their current location.

[0085] The glasses-type wearable device may further include a weather information acquisition unit. The weather information acquisition unit acquires current weather information and provides appropriate feedback to the user. For example, if it looks like it's going to rain, it may notify the user to take an umbrella. The weather information acquisition unit may also acquire current weather information in real time and provide appropriate advice. Furthermore, the weather information acquisition unit may use a weather database to acquire weather information with high accuracy. This allows the user to take appropriate action depending on the current weather.

[0086] The glasses-type wearable device may further include a health management unit. The health management unit monitors the user's health condition and provides appropriate feedback. For example, it measures heart rate and blood pressure and notifies the user if an abnormality is detected. The health management unit may also monitor the user's health condition in real time and provide appropriate advice. Furthermore, the health management unit may use a biosensor to monitor the user's health condition with high accuracy. This allows the user to constantly monitor their own health condition and take appropriate measures.

[0087] The glasses-type wearable device can further include a learning support unit. The learning support unit supports the user's learning and provides appropriate feedback. For example, if the user is learning a foreign language, it can provide advice on the pronunciation and grammar of that language. The learning support unit can also monitor the user's learning progress in real time and propose an appropriate learning plan. Furthermore, the learning support unit can use generative AI to support the user's learning with high accuracy. This allows the user to progress with their learning effectively.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The sign language recognition unit recognizes the sign language. For example, the sign language recognition unit captures sign language movements with a camera and analyzes the movements to recognize the sign language. The sign language recognition unit can also recognize sign language movements in real time. Furthermore, the sign language recognition unit can use multiple cameras to recognize sign language movements with high accuracy. Step 2: The speech generation unit converts the sign language recognized by the sign language recognition unit into speech. For example, the speech generation unit converts the sign language into speech using a generation AI (e.g., a text generation AI or a multimodal generation AI). The speech generation unit can also generate appropriate speech depending on the content of the sign language. Furthermore, the speech generation unit can also generate natural speech based on the movement of the sign language. Step 3: The speech recognition unit recognizes English speech. For example, the speech recognition unit analyzes English speech data and recognizes its content. The speech recognition unit can also recognize English speech in real time. Furthermore, the speech recognition unit can use noise canceling technology to recognize English speech with high accuracy. Step 4: The translation unit converts the English speech recognized by the speech recognition unit into Japanese speech. For example, the translation unit uses a generative AI to convert the English speech into Japanese speech. The translation unit can also generate appropriate Japanese speech depending on the content of the English speech. Furthermore, the translation unit can convert the English speech into natural Japanese speech. Step 5: The text generator converts the speech into text. For example, the text generator uses a generative AI to convert speech into text. The text generator can also generate appropriate text depending on the content of the speech. Furthermore, the text generator can convert speech into natural-sounding text. Step 6: The character recognition unit recognizes French characters. For example, the character recognition unit analyzes French character data and recognizes its content. The character recognition unit can also recognize French characters in real time. Furthermore, the character recognition unit can use OCR technology to recognize French characters with high accuracy. Step 7: The translation unit converts the French characters recognized by the character recognition unit into Japanese characters. For example, the translation unit converts the French characters into Japanese characters using a generation AI. The translation unit can also generate appropriate Japanese characters depending on the content of the French characters. Furthermore, the translation unit can convert the French characters into natural Japanese characters.

[0090] 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.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0103] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0104] 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.

[0105] 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.

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

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] 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.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0134] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0156] 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]

[0157] 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 sign language recognition unit that recognizes sign language; a speech generation unit that converts the sign language recognized by the sign language recognition unit into speech; a speech recognition unit that recognizes English speech; a translation unit that converts the English speech recognized by the speech recognition unit into Japanese speech; a text generation unit that converts speech into text; a character recognition unit for recognizing French characters; a translation unit that converts the French characters recognized by the character recognition unit into Japanese characters. A system characterized by:

2. The sign language recognition unit Analyzes not only hand movements but also facial expressions and body movements to generate more natural voices 2. The system of claim 1.

3. The voice recognition unit When recognizing English speech, generate more natural Japanese speech based on the speaker's accent or dialect.

2. The system of claim 1.

4. The text generation unit Generate more accurate speech-to-text based on the speaker's accent or dialect 2. The system of claim 1.

5. The character recognition unit Recognizes French characters, taking into account handwriting and different font styles, and produces more accurate Japanese characters.

2. The system of claim 1.

6. The sign language recognition unit Using emotion estimation functionality, the system estimates the emotion of the sign language user and generates speech with a tone and intonation that matches that emotion.

2. The system of claim 1.

7. The voice recognition unit Using emotion estimation functionality, the emotion of the English speaker is estimated and Japanese speech is generated with a tone and intonation that corresponds to that emotion.

2. The system of claim 1.

8. The text generation unit Using emotion estimation functionality, the speaker's emotions are estimated and expressions corresponding to those emotions are reflected in the text.

2. The system of claim 1.

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