System

Abstract

An object of a system according to an embodiment is to realize smooth communication between a person who speaks sign language and a hearing-impaired person or a hearing-impaired person.SOLUTION: A system includes a sign language recognition unit, a conversion unit, and a display unit. The sign language recognition unit recognizes a motion of a hand of a person speaking in sign language in real time. The conversion unit converts the content of the sign language recognized by the sign language recognition unit into natural language. The display unit displays the natural language converted by the conversion unit via a smartphone or a wearable device.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 that it is difficult to achieve smooth communication between sign language speakers and the hearing impaired or hard of hearing.

[0005] The system according to the embodiment aims to realize smooth communication between sign language speakers and the hearing impaired or hard of hearing. [Means for solving the problem]

[0006] The system according to the embodiment includes a sign language recognition unit, a conversion unit, and a display unit. The sign language recognition unit recognizes hand movements of a person speaking sign language in real time. The conversion unit converts the sign language content recognized by the sign language recognition unit into natural language. The display unit displays the natural language converted by the conversion unit via a smartphone or a wearable device. [Effects of the Invention]

[0007] The system according to the embodiment can realize smooth communication between people who speak sign language and people who are deaf or hard of hearing. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A sign language transcription system according to an embodiment of the present invention is a system that recognizes the hand movements of a person speaking sign language in real time, converts them into natural language, and displays them via a smartphone or wearable device. This enables smooth communication with the hearing impaired and hard of hearing.

[0029] A sign language transcription system according to an embodiment includes a sign language recognition unit, a conversion unit, and a display unit. The sign language recognition unit recognizes hand movements of a person speaking sign language in real time. For example, the sign language recognition unit captures hand movements using a camera or a sensor and analyzes the data. The sign language recognition unit also converts the hand movement data into sign language words or phrases. For example, the sign language recognition unit recognizes hand movements that express "hello" in sign language and acquires the text data. The conversion unit converts the sign language content recognized by the sign language recognition unit into natural language. For example, the conversion unit converts sign language words or phrases into natural language based on the context of surrounding sentences. The conversion unit also analyzes the sign language content and the surrounding sentences using a generative AI to generate an appropriate sentence in natural language. For example, when the sign language expression is "hello, how are you?", the conversion unit analyzes the sign language content and generates the natural sentence "hello, how are you?". The display unit displays the natural language converted by the conversion unit via a smartphone or a wearable device. For example, the display unit displays the generated text on a smartphone screen and allows the other party to communicate by showing it. The display unit can also display the sign language content in real time using a wearable device. For example, the display unit displays the sign language content using a smartwatch or AR glasses. This allows the sign language transcription system according to the embodiment to realize smooth communication with the hearing impaired and hard of hearing.

[0030] The sign language recognition unit can simultaneously analyze not only hand movements but also facial expressions and body movements, enabling more accurate sign language recognition. The sign language recognition unit, for example, builds a system that simultaneously analyzes not only hand movements but also facial expressions and body movements. For example, a camera can simultaneously capture hand movements and facial expressions, and the data from each can be integrated to analyze the content of the sign language. The sign language recognition unit also develops an algorithm that analyzes facial expressions and body movements. For example, it can read emotions from facial expressions and reflect them in the interpretation of the sign language. The sign language recognition unit also uses multiple sensors to simultaneously analyze hand movements, facial expressions, and body movements. For example, it can combine a sensor that captures hand movements with a camera that captures facial expressions. This improves the accuracy of sign language recognition by simultaneously analyzing not only hand movements but also facial expressions and body movements.

[0031] The sign language recognition unit performs 3D modeling using multiple cameras and sensors, enabling three-dimensional analysis of hand movements. The sign language recognition unit, for example, builds a system that uses multiple cameras to 3D model hand movements. For example, cameras are positioned to capture hand movements from different angles, generating three-dimensional data. The sign language recognition unit also develops an algorithm that uses sensors to 3D model hand movements. For example, sensors that accurately measure the position and angle of the hands are used to generate three-dimensional data. The sign language recognition unit also builds a system that uses 3D modeling technology to analyze hand movements three-dimensionally. For example, hand movements are converted into 3D models in real time, and the content of the sign language is analyzed based on that data. This improves the accuracy of sign language recognition by 3D modeling hand movements using multiple cameras and sensors.

[0032] The sign language recognition unit applies the technology for recognizing sign language movements to gesture recognition and body language analysis, and can be used to analyze different means of communication. For example, the sign language recognition unit builds a system that applies the technology for recognizing sign language movements to gesture recognition. For example, it analyzes hand movements to recognize specific gestures. The sign language recognition unit also develops an algorithm that applies the technology for recognizing sign language movements to body language analysis. For example, it analyzes body movements and posture to understand the intention of communication. The sign language recognition unit also builds a system that uses the technology for recognizing sign language movements to analyze different means of communication. For example, it analyzes hand movements and body movements to understand non-verbal communication. This makes it possible to analyze different means of communication by applying the sign language recognition technology to other gesture recognition and body language analysis.

[0033] The sign language recognition unit applies sign language recognition technology to sports and dance movement analysis, and can be used for performance evaluation and training. The sign language recognition unit, for example, builds a system that applies sign language recognition technology to sports movement analysis. For example, it analyzes athletes' movements to evaluate performance. The sign language recognition unit also develops an algorithm that applies sign language recognition technology to dance movement analysis. For example, it analyzes dancers' movements to evaluate the effectiveness of training. The sign language recognition unit also applies sign language recognition technology to sports and dance movement analysis, and builds a system that can be used for performance evaluation and training. For example, it creates a training plan based on movement data. In this way, applying sign language recognition technology to sports and dance movement analysis can be used for performance evaluation and training.

[0034] The conversion unit, when converting sign language content into natural language, takes into account linguistic nuances depending on the region and culture, thereby enabling more appropriate translation. For example, the conversion unit builds a system that takes into account linguistic nuances depending on the region and culture when converting sign language content into natural language. For example, it reflects expressions and dialects specific to the region. The conversion unit also develops an algorithm that takes into account linguistic nuances depending on the region and culture in order to convert sign language content into natural language. For example, it performs appropriate translation based on cultural background. The conversion unit also builds a system that takes into account linguistic nuances depending on the region and culture when converting sign language content into natural language, thereby enabling more appropriate translation. For example, it uses a language database for each region. This allows for more appropriate translation by taking into account linguistic nuances depending on the region and culture.

[0035] The conversion unit can learn the speaker's personal speaking style and habits when converting sign language content into natural language, and provide individually optimized translations. For example, the conversion unit builds a system that learns the speaker's personal speaking style and habits when converting sign language content into natural language. For example, it optimizes the translation based on the speaker's past data. The conversion unit also develops an algorithm that learns the speaker's personal speaking style and habits in order to convert sign language content into natural language. For example, it analyzes the speaker's characteristics and reflects them in the translation. The conversion unit also builds a system that learns the speaker's personal speaking style and habits when converting sign language content into natural language, and provides individually optimized translations. For example, it creates a profile for each speaker. This makes it possible to learn the speaker's personal speaking style and habits and provide individually optimized translations.

[0036] The conversion unit applies the technology for converting sign language content into natural language to translation between languages, thereby supporting communication between different languages. The conversion unit, for example, builds a system that applies the technology for converting sign language content into natural language to translation between other languages. For example, it translates from English to Japanese. The conversion unit also develops an algorithm that applies the technology for converting sign language content into natural language to support communication between different languages. For example, it builds a translation system that supports multiple languages. The conversion unit also builds a system that applies the technology for converting sign language content into natural language to translation between other languages, thereby supporting communication between different languages. For example, it provides a real-time translation function. As a result, communication between different languages ​​is supported by applying the technology for converting sign language content into natural language to translation between other languages.

[0037] The conversion unit can improve the accuracy of speech-to-text conversion by combining technology that converts sign language content into natural language with speech recognition. The conversion unit, for example, builds a system that combines technology that converts sign language content into natural language with speech recognition. For example, it integrates and analyzes speech data and sign language data. The conversion unit also develops an algorithm that combines technology that converts sign language content into natural language with speech recognition to improve the accuracy of speech-to-text conversion. For example, it analyzes speech and sign language data simultaneously. The conversion unit also builds a system that combines technology that converts sign language content into natural language with speech recognition to improve the accuracy of speech-to-text conversion. For example, it integrates and analyzes speech and sign language data. As a result, by combining technology that converts sign language content into natural language with speech recognition, the accuracy of speech-to-text conversion is improved.

[0038] The display unit uses speech synthesis technology to output the generated natural-sounding sentences as audio, making it possible to accommodate visually impaired people. The display unit, for example, builds a system that uses speech synthesis technology to output the generated natural-sounding sentences as audio. For example, a speech output function is added to a smartphone or wearable device. The display unit also develops an algorithm that uses speech synthesis technology to output the generated natural-sounding sentences as audio. For example, the tone and speed of the voice are adjusted to generate natural-sounding voices. The display unit also builds a system that uses speech synthesis technology to output the generated natural-sounding sentences as audio, making it possible to accommodate visually impaired people. For example, a wearable device with a speech output function is developed. This makes it possible to output the generated natural-sounding sentences as audio using speech synthesis technology, making it possible to accommodate visually impaired people.

[0039] The display unit uses AR technology to display the generated natural-looking sentences superimposed on real space, thereby realizing intuitive communication. The display unit, for example, builds a system that uses AR technology to display the generated natural-looking sentences superimposed on real space. For example, the sentences are displayed in real space using a smartphone camera. The display unit also develops an algorithm that uses AR technology to display the generated natural-looking sentences superimposed on real space. For example, the position and size of the sentences are adjusted before displaying them. The display unit also builds a system that uses AR technology to display the generated natural-looking sentences superimposed on real space, thereby realizing intuitive communication. For example, the sentences are displayed using AR glasses. In this way, intuitive communication is realized by displaying the generated natural-looking sentences superimposed on real space using AR technology.

[0040] The display unit can display the generated natural-sounding sentences not only on smartphones and wearable devices, but also on smart glasses and projectors, enabling use on various devices. The display unit, for example, builds a system that displays the generated natural-sounding sentences using smart glasses and projectors. For example, it displays the sentences on smart glasses. The display unit also develops an algorithm that uses not only smartphones and wearable devices, but also smart glasses and projectors, to make the generated natural-sounding sentences available on various devices. For example, it provides an optimal display method for each device. The display unit also builds a system that displays the generated natural-sounding sentences not only on smartphones and wearable devices, but also on smart glasses and projectors, enabling use on various devices. For example, it projects the sentences onto a wall using a projector. As a result, the generated natural-sounding sentences can be displayed not only on smartphones and wearable devices, but also on smart glasses and projectors, enabling use on various devices.

[0041] The display unit can integrate the generated natural-sentences into a chatbot and a virtual assistant to realize interactive communication. The display unit, for example, builds a system that integrates the generated natural-sentences into a chatbot or a virtual assistant. For example, the chatbot conducts a dialogue based on the generated sentence. The display unit also develops an algorithm that integrates the generated natural-sentences into a chatbot or a virtual assistant to use the generated natural-sentences in interactive communication. For example, the display unit generates an appropriate response based on the generated sentence. The display unit also builds a system that integrates the generated natural-sentences into a chatbot or a virtual assistant to realize interactive communication. For example, the virtual assistant conducts a dialogue with a user based on the generated sentence. In this way, interactive communication is realized by integrating the generated natural-sentences into a chatbot or a virtual assistant.

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

[0043] The sign language recognition unit applies the technology for recognizing sign language movements to gesture recognition and body language analysis, and can be used to analyze different means of communication. For example, the sign language recognition unit builds a system that applies the technology for recognizing sign language movements to gesture recognition. For example, it analyzes hand movements to recognize specific gestures. The sign language recognition unit also develops an algorithm that applies the technology for recognizing sign language movements to body language analysis. For example, it analyzes body movements and posture to understand the intention of communication. The sign language recognition unit also builds a system that uses the technology for recognizing sign language movements to analyze different means of communication. For example, it analyzes hand movements and body movements to understand non-verbal communication. This makes it possible to analyze different means of communication by applying the sign language recognition technology to other gesture recognition and body language analysis.

[0044] The sign language recognition unit applies sign language recognition technology to sports and dance movement analysis, and can be used for performance evaluation and training. The sign language recognition unit, for example, builds a system that applies sign language recognition technology to sports movement analysis. For example, it analyzes athletes' movements to evaluate performance. The sign language recognition unit also develops an algorithm that applies sign language recognition technology to dance movement analysis. For example, it analyzes dancers' movements to evaluate the effectiveness of training. The sign language recognition unit also applies sign language recognition technology to sports and dance movement analysis, and builds a system that can be used for performance evaluation and training. For example, it creates a training plan based on movement data. In this way, applying sign language recognition technology to sports and dance movement analysis can be used for performance evaluation and training.

[0045] The conversion unit, when converting sign language content into natural language, takes into account linguistic nuances depending on the region and culture, thereby enabling more appropriate translation. For example, the conversion unit builds a system that takes into account linguistic nuances depending on the region and culture when converting sign language content into natural language. For example, it reflects expressions and dialects specific to the region. The conversion unit also develops an algorithm that takes into account linguistic nuances depending on the region and culture in order to convert sign language content into natural language. For example, it performs appropriate translation based on cultural background. The conversion unit also builds a system that takes into account linguistic nuances depending on the region and culture when converting sign language content into natural language, thereby enabling more appropriate translation. For example, it uses a language database for each region. This allows for more appropriate translation by taking into account linguistic nuances depending on the region and culture.

[0046] The conversion unit can learn the speaker's personal speaking style and habits when converting sign language content into natural language, and provide individually optimized translations. For example, the conversion unit builds a system that learns the speaker's personal speaking style and habits when converting sign language content into natural language. For example, it optimizes the translation based on the speaker's past data. The conversion unit also develops an algorithm that learns the speaker's personal speaking style and habits in order to convert sign language content into natural language. For example, it analyzes the speaker's characteristics and reflects them in the translation. The conversion unit also builds a system that learns the speaker's personal speaking style and habits when converting sign language content into natural language, and provides individually optimized translations. For example, it creates a profile for each speaker. This makes it possible to learn the speaker's personal speaking style and habits and provide individually optimized translations.

[0047] The display unit uses speech synthesis technology to output the generated natural-sounding sentences as audio, making it possible to accommodate visually impaired people. The display unit, for example, builds a system that uses speech synthesis technology to output the generated natural-sounding sentences as audio. For example, a speech output function is added to a smartphone or wearable device. The display unit also develops an algorithm that uses speech synthesis technology to output the generated natural-sounding sentences as audio. For example, the tone and speed of the voice are adjusted to generate natural-sounding voices. The display unit also builds a system that uses speech synthesis technology to output the generated natural-sounding sentences as audio, making it possible to accommodate visually impaired people. For example, a wearable device with a speech output function is developed. This makes it possible to output the generated natural-sounding sentences as audio using speech synthesis technology, making it possible to accommodate visually impaired people.

[0048] The display unit uses AR technology to display the generated natural-looking sentences superimposed on real space, thereby realizing intuitive communication. The display unit, for example, builds a system that uses AR technology to display the generated natural-looking sentences superimposed on real space. For example, the sentences are displayed in real space using a smartphone camera. The display unit also develops an algorithm that uses AR technology to display the generated natural-looking sentences superimposed on real space. For example, the position and size of the sentences are adjusted before displaying them. The display unit also builds a system that uses AR technology to display the generated natural-looking sentences superimposed on real space, thereby realizing intuitive communication. For example, the sentences are displayed using AR glasses. In this way, intuitive communication is realized by displaying the generated natural-looking sentences superimposed on real space using AR technology.

[0049] The display unit can display the generated natural-sounding sentences not only on smartphones and wearable devices, but also on smart glasses and projectors, enabling use on various devices. The display unit, for example, builds a system that displays the generated natural-sounding sentences using smart glasses and projectors. For example, it displays the sentences on smart glasses. The display unit also develops an algorithm that uses not only smartphones and wearable devices, but also smart glasses and projectors, to make the generated natural-sounding sentences available on various devices. For example, it provides an optimal display method for each device. The display unit also builds a system that displays the generated natural-sounding sentences not only on smartphones and wearable devices, but also on smart glasses and projectors, enabling use on various devices. For example, it projects the sentences onto a wall using a projector. As a result, the generated natural-sounding sentences can be displayed not only on smartphones and wearable devices, but also on smart glasses and projectors, enabling use on various devices.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The sign language recognition unit recognizes the hand movements of the person speaking sign language in real time. For example, the sign language recognition unit captures hand movements using a camera or sensor and analyzes the data. The sign language recognition unit also converts the hand movement data into sign language words or phrases. For example, the sign language recognition unit recognizes hand movements that express "hello" in sign language and acquires them as text data. Step 2: The conversion unit converts the sign language content recognized by the sign language recognition unit into natural language. For example, the conversion unit converts sign language words and phrases into natural language based on the context of the surrounding text. The conversion unit also uses generative AI to analyze the sign language content and the surrounding text to generate an appropriate natural language sentence. For example, if the sign language phrase is "Hello, how are you?", the conversion unit analyzes the sign language content and generates the natural sentence "Hello, how are you?" Step 3: The display unit displays the natural language converted by the conversion unit via a smartphone or wearable device. For example, the display unit displays the generated text on a smartphone screen and communicates with the other person by showing it to them. The display unit can also display the sign language content in real time using a wearable device. For example, the display unit displays the sign language content using a smartwatch or AR glasses.

[0052] (Example 2) A sign language transcription system according to an embodiment of the present invention is a system that recognizes the hand movements of a person speaking sign language in real time, converts them into natural language, and displays them via a smartphone or wearable device. This enables smooth communication with the hearing impaired and hard of hearing.

[0053] A sign language transcription system according to an embodiment includes a sign language recognition unit, a conversion unit, and a display unit. The sign language recognition unit recognizes hand movements of a person speaking sign language in real time. For example, the sign language recognition unit captures hand movements using a camera or a sensor and analyzes the data. The sign language recognition unit also converts the hand movement data into sign language words or phrases. For example, the sign language recognition unit recognizes hand movements that express "hello" in sign language and acquires the text data. The conversion unit converts the sign language content recognized by the sign language recognition unit into natural language. For example, the conversion unit converts sign language words or phrases into natural language based on the context of surrounding sentences. The conversion unit also analyzes the sign language content and the surrounding sentences using a generative AI to generate an appropriate sentence in natural language. For example, when the sign language expression is "hello, how are you?", the conversion unit analyzes the sign language content and generates the natural sentence "hello, how are you?". The display unit displays the natural language converted by the conversion unit via a smartphone or a wearable device. For example, the display unit displays the generated text on a smartphone screen and allows the other party to communicate by showing it. The display unit can also display the sign language content in real time using a wearable device. For example, the display unit displays the sign language content using a smartwatch or AR glasses. This allows the sign language transcription system according to the embodiment to realize smooth communication with the hearing impaired and hard of hearing.

[0054] The sign language recognition unit can simultaneously analyze not only hand movements but also facial expressions and body movements, enabling more accurate sign language recognition. The sign language recognition unit, for example, builds a system that simultaneously analyzes not only hand movements but also facial expressions and body movements. For example, a camera can simultaneously capture hand movements and facial expressions, and the data from each can be integrated to analyze the content of the sign language. The sign language recognition unit also develops an algorithm that analyzes facial expressions and body movements. For example, it can read emotions from facial expressions and reflect them in the interpretation of the sign language. The sign language recognition unit also uses multiple sensors to simultaneously analyze hand movements, facial expressions, and body movements. For example, it can combine a sensor that captures hand movements with a camera that captures facial expressions. This improves the accuracy of sign language recognition by simultaneously analyzing not only hand movements but also facial expressions and body movements.

[0055] The sign language recognition unit performs 3D modeling using multiple cameras and sensors, enabling three-dimensional analysis of hand movements. The sign language recognition unit, for example, builds a system that uses multiple cameras to 3D model hand movements. For example, cameras are positioned to capture hand movements from different angles, generating three-dimensional data. The sign language recognition unit also develops an algorithm that uses sensors to 3D model hand movements. For example, sensors that accurately measure the position and angle of the hands are used to generate three-dimensional data. The sign language recognition unit also builds a system that uses 3D modeling technology to analyze hand movements three-dimensionally. For example, hand movements are converted into 3D models in real time, and the content of the sign language is analyzed based on that data. This improves the accuracy of sign language recognition by 3D modeling hand movements using multiple cameras and sensors.

[0056] The sign language recognition unit can use the emotion estimation function to estimate the emotion of a person speaking sign language and correct the interpretation of the sign language based on that emotion. The sign language recognition unit, for example, uses the emotion estimation function to build a system that estimates the emotion of a person speaking sign language in real time. For example, it analyzes facial expressions and vocal tone to calculate an emotion score. The sign language recognition unit also develops an algorithm that uses the emotion estimation function to estimate the emotion of a person speaking sign language in order to correct the interpretation of the sign language. For example, it corrects the content of the sign language based on the emotion score. The sign language recognition unit also uses the emotion estimation function to estimate the emotion of a person speaking sign language and builds a system that corrects the interpretation of the sign language based on that emotion. For example, if the emotion score is high, the content of the sign language is emphasized. In this way, more accurate sign language recognition is possible by using the emotion estimation function to estimate the emotion of a person speaking sign language and correcting the interpretation of the sign language based on that emotion.

[0057] The sign language recognition unit applies the technology for recognizing sign language movements to gesture recognition and body language analysis, and can be used to analyze different means of communication. For example, the sign language recognition unit builds a system that applies the technology for recognizing sign language movements to gesture recognition. For example, it analyzes hand movements to recognize specific gestures. The sign language recognition unit also develops an algorithm that applies the technology for recognizing sign language movements to body language analysis. For example, it analyzes body movements and posture to understand the intention of communication. The sign language recognition unit also builds a system that uses the technology for recognizing sign language movements to analyze different means of communication. For example, it analyzes hand movements and body movements to understand non-verbal communication. This makes it possible to analyze different means of communication by applying the sign language recognition technology to other gesture recognition and body language analysis.

[0058] The sign language recognition unit applies sign language recognition technology to sports and dance movement analysis, and can be used for performance evaluation and training. The sign language recognition unit, for example, builds a system that applies sign language recognition technology to sports movement analysis. For example, it analyzes athletes' movements to evaluate performance. The sign language recognition unit also develops an algorithm that applies sign language recognition technology to dance movement analysis. For example, it analyzes dancers' movements to evaluate the effectiveness of training. The sign language recognition unit also applies sign language recognition technology to sports and dance movement analysis, and builds a system that can be used for performance evaluation and training. For example, it creates a training plan based on movement data. In this way, applying sign language recognition technology to sports and dance movement analysis can be used for performance evaluation and training.

[0059] The sign language recognition unit uses an emotion estimation function to display the emotion of a person speaking sign language in real time, thereby improving the quality of communication. The sign language recognition unit, for example, uses the emotion estimation function to build a system that displays the emotion of a person speaking sign language in real time. For example, an emotion score is displayed on a screen. The sign language recognition unit also develops an algorithm that uses the emotion estimation function to display the emotion of a person speaking sign language in real time. For example, the intensity of the emotion is displayed based on the emotion score. The sign language recognition unit also uses the emotion estimation function to build a system that displays the emotion of a person speaking sign language in real time, thereby improving the quality of communication. For example, if the emotion score is high, the emotion is displayed in an emphasized manner. In this way, the emotion estimation function is used to display the emotion of a person speaking sign language in real time, thereby improving the quality of communication.

[0060] The conversion unit, when converting sign language content into natural language, takes into account linguistic nuances depending on the region and culture, thereby enabling more appropriate translation. For example, the conversion unit builds a system that takes into account linguistic nuances depending on the region and culture when converting sign language content into natural language. For example, it reflects expressions and dialects specific to the region. The conversion unit also develops an algorithm that takes into account linguistic nuances depending on the region and culture in order to convert sign language content into natural language. For example, it performs appropriate translation based on cultural background. The conversion unit also builds a system that takes into account linguistic nuances depending on the region and culture when converting sign language content into natural language, thereby enabling more appropriate translation. For example, it uses a language database for each region. This allows for more appropriate translation by taking into account linguistic nuances depending on the region and culture.

[0061] The conversion unit can learn the speaker's personal speaking style and habits when converting sign language content into natural language, and provide individually optimized translations. For example, the conversion unit builds a system that learns the speaker's personal speaking style and habits when converting sign language content into natural language. For example, it optimizes the translation based on the speaker's past data. The conversion unit also develops an algorithm that learns the speaker's personal speaking style and habits in order to convert sign language content into natural language. For example, it analyzes the speaker's characteristics and reflects them in the translation. The conversion unit also builds a system that learns the speaker's personal speaking style and habits when converting sign language content into natural language, and provides individually optimized translations. For example, it creates a profile for each speaker. This makes it possible to learn the speaker's personal speaking style and habits and provide individually optimized translations.

[0062] The conversion unit can use the emotion estimation function to generate natural-looking words that reflect the emotions contained in the sign language content. The conversion unit, for example, uses the emotion estimation function to build a system that generates natural-looking words that reflect the emotions contained in the sign language content. For example, the conversion unit adjusts the tone of the sentence based on the emotion score. The conversion unit also develops an algorithm that uses the emotion estimation function to generate natural-looking words that reflect the emotions contained in the sign language content. For example, the conversion unit selects appropriate expressions based on the emotion score. The conversion unit also builds a system that uses the emotion estimation function to generate natural-looking words that reflect the emotions contained in the sign language content. For example, if the emotion score is high, the conversion unit generates sentences that emphasize the emotions. As a result, by using the emotion estimation function to generate natural-looking words that reflect the emotions contained in the sign language content, more emotional communication is possible.

[0063] The conversion unit applies the technology for converting sign language content into natural language to translation between languages, thereby supporting communication between different languages. The conversion unit, for example, builds a system that applies the technology for converting sign language content into natural language to translation between other languages. For example, it translates from English to Japanese. The conversion unit also develops an algorithm that applies the technology for converting sign language content into natural language to support communication between different languages. For example, it builds a translation system that supports multiple languages. The conversion unit also builds a system that applies the technology for converting sign language content into natural language to translation between other languages, thereby supporting communication between different languages. For example, it provides a real-time translation function. As a result, communication between different languages ​​is supported by applying the technology for converting sign language content into natural language to translation between other languages.

[0064] The conversion unit can improve the accuracy of speech-to-text conversion by combining technology that converts sign language content into natural language with speech recognition. The conversion unit, for example, builds a system that combines technology that converts sign language content into natural language with speech recognition. For example, it integrates and analyzes speech data and sign language data. The conversion unit also develops an algorithm that combines technology that converts sign language content into natural language with speech recognition to improve the accuracy of speech-to-text conversion. For example, it analyzes speech and sign language data simultaneously. The conversion unit also builds a system that combines technology that converts sign language content into natural language with speech recognition to improve the accuracy of speech-to-text conversion. For example, it integrates and analyzes speech and sign language data. As a result, by combining technology that converts sign language content into natural language with speech recognition, the accuracy of speech-to-text conversion is improved.

[0065] The conversion unit uses the emotion estimation function to visually display emotions based on the content of the sign language, which can be used as an aid to communication. The conversion unit, for example, uses the emotion estimation function to build a system that visually displays emotions based on the content of the sign language. For example, it displays emotion scores using graphs or icons. The conversion unit also develops an algorithm that uses the emotion estimation function to visually display emotions based on the content of the sign language. For example, it provides visual feedback based on the emotion score. The conversion unit also uses the emotion estimation function to visually display emotions based on the content of the sign language, which can be used as an aid to communication. For example, if the emotion score is high, the emotion is displayed in an emphasized manner. In this way, visually displaying emotions based on the content of the sign language using the emotion estimation function can be used as an aid to communication.

[0066] The display unit uses speech synthesis technology to output the generated natural-sounding sentences as audio, making it possible to accommodate visually impaired people. The display unit, for example, builds a system that uses speech synthesis technology to output the generated natural-sounding sentences as audio. For example, a speech output function is added to a smartphone or wearable device. The display unit also develops an algorithm that uses speech synthesis technology to output the generated natural-sounding sentences as audio. For example, the tone and speed of the voice are adjusted to generate natural-sounding voices. The display unit also builds a system that uses speech synthesis technology to output the generated natural-sounding sentences as audio, making it possible to accommodate visually impaired people. For example, a wearable device with a speech output function is developed. This makes it possible to output the generated natural-sounding sentences as audio using speech synthesis technology, making it possible to accommodate visually impaired people.

[0067] The display unit uses AR technology to display the generated natural-looking sentences superimposed on real space, thereby realizing intuitive communication. The display unit, for example, builds a system that uses AR technology to display the generated natural-looking sentences superimposed on real space. For example, the sentences are displayed in real space using a smartphone camera. The display unit also develops an algorithm that uses AR technology to display the generated natural-looking sentences superimposed on real space. For example, the position and size of the sentences are adjusted before displaying them. The display unit also builds a system that uses AR technology to display the generated natural-looking sentences superimposed on real space, thereby realizing intuitive communication. For example, the sentences are displayed using AR glasses. In this way, intuitive communication is realized by displaying the generated natural-looking sentences superimposed on real space using AR technology.

[0068] The display unit can add emphasis to emotions in sentences generated using the emotion estimation function, thereby providing emotionally rich communication. The display unit, for example, builds a system that adds emphasis to emotions in sentences generated using the emotion estimation function. For example, the display unit adjusts the tone and expression of the sentence based on the emotion score. The display unit also develops an algorithm that uses the emotion estimation function to add emphasis to the generated sentences. For example, the display unit selects appropriate expressions based on the emotion score. The display unit also builds a system that adds emphasis to emotions in sentences generated using the emotion estimation function, thereby providing emotionally rich communication. For example, if the emotion score is high, the display unit generates sentences with emphasized emotions. As a result, emotionally rich communication is provided by adding emphasis to sentences generated using the emotion estimation function.

[0069] The display unit can display the generated natural-sounding sentences not only on smartphones and wearable devices, but also on smart glasses and projectors, enabling use on various devices. The display unit, for example, builds a system that displays the generated natural-sounding sentences using smart glasses and projectors. For example, it displays the sentences on smart glasses. The display unit also develops an algorithm that uses not only smartphones and wearable devices, but also smart glasses and projectors, to make the generated natural-sounding sentences available on various devices. For example, it provides an optimal display method for each device. The display unit also builds a system that displays the generated natural-sounding sentences not only on smartphones and wearable devices, but also on smart glasses and projectors, enabling use on various devices. For example, it projects the sentences onto a wall using a projector. As a result, the generated natural-sounding sentences can be displayed not only on smartphones and wearable devices, but also on smart glasses and projectors, enabling use on various devices.

[0070] The display unit can integrate the generated natural-sentences into a chatbot and a virtual assistant to realize interactive communication. The display unit, for example, builds a system that integrates the generated natural-sentences into a chatbot or a virtual assistant. For example, the chatbot conducts a dialogue based on the generated sentence. The display unit also develops an algorithm that integrates the generated natural-sentences into a chatbot or a virtual assistant to use the generated natural-sentences in interactive communication. For example, the display unit generates an appropriate response based on the generated sentence. The display unit also builds a system that integrates the generated natural-sentences into a chatbot or a virtual assistant to realize interactive communication. For example, the virtual assistant conducts a dialogue with a user based on the generated sentence. In this way, interactive communication is realized by integrating the generated natural-sentences into a chatbot or a virtual assistant.

[0071] The display unit adds emotional feedback to sentences generated using the emotion estimation function, thereby providing communication that users can easily empathize with emotionally. The display unit, for example, builds a system that adds emotional feedback to sentences generated using the emotion estimation function. For example, the display unit adjusts the tone and expression of the sentences based on the emotion score. The display unit also develops an algorithm that uses the emotion estimation function to add emotional feedback to the generated sentences. For example, the display unit selects appropriate expressions based on the emotion score. The display unit also builds a system that adds emotional feedback to sentences generated using the emotion estimation function, thereby providing communication that users can easily empathize with emotionally. For example, if the emotion score is high, the display unit generates sentences that emphasize emotions. In this way, by adding emotional feedback to sentences generated using the emotion estimation function, communication that users can easily empathize with emotionally is provided.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The sign language recognition unit applies the technology for recognizing sign language movements to gesture recognition and body language analysis, and can be used to analyze different means of communication. For example, the sign language recognition unit builds a system that applies the technology for recognizing sign language movements to gesture recognition. For example, it analyzes hand movements to recognize specific gestures. The sign language recognition unit also develops an algorithm that applies the technology for recognizing sign language movements to body language analysis. For example, it analyzes body movements and posture to understand the intention of communication. The sign language recognition unit also builds a system that uses the technology for recognizing sign language movements to analyze different means of communication. For example, it analyzes hand movements and body movements to understand non-verbal communication. This makes it possible to analyze different means of communication by applying the sign language recognition technology to other gesture recognition and body language analysis.

[0074] The sign language recognition unit applies sign language recognition technology to sports and dance movement analysis, and can be used for performance evaluation and training. The sign language recognition unit, for example, builds a system that applies sign language recognition technology to sports movement analysis. For example, it analyzes athletes' movements to evaluate performance. The sign language recognition unit also develops an algorithm that applies sign language recognition technology to dance movement analysis. For example, it analyzes dancers' movements to evaluate the effectiveness of training. The sign language recognition unit also applies sign language recognition technology to sports and dance movement analysis, and builds a system that can be used for performance evaluation and training. For example, it creates a training plan based on movement data. In this way, applying sign language recognition technology to sports and dance movement analysis can be used for performance evaluation and training.

[0075] The sign language recognition unit uses an emotion estimation function to display the emotion of a person speaking sign language in real time, thereby improving the quality of communication. The sign language recognition unit, for example, uses the emotion estimation function to build a system that displays the emotion of a person speaking sign language in real time. For example, an emotion score is displayed on a screen. The sign language recognition unit also develops an algorithm that uses the emotion estimation function to display the emotion of a person speaking sign language in real time. For example, the intensity of the emotion is displayed based on the emotion score. The sign language recognition unit also uses the emotion estimation function to build a system that displays the emotion of a person speaking sign language in real time, thereby improving the quality of communication. For example, if the emotion score is high, the emotion is displayed in an emphasized manner. In this way, the emotion estimation function is used to display the emotion of a person speaking sign language in real time, thereby improving the quality of communication.

[0076] The conversion unit, when converting sign language content into natural language, takes into account linguistic nuances depending on the region and culture, thereby enabling more appropriate translation. For example, the conversion unit builds a system that takes into account linguistic nuances depending on the region and culture when converting sign language content into natural language. For example, it reflects expressions and dialects specific to the region. The conversion unit also develops an algorithm that takes into account linguistic nuances depending on the region and culture in order to convert sign language content into natural language. For example, it performs appropriate translation based on cultural background. The conversion unit also builds a system that takes into account linguistic nuances depending on the region and culture when converting sign language content into natural language, thereby enabling more appropriate translation. For example, it uses a language database for each region. This allows for more appropriate translation by taking into account linguistic nuances depending on the region and culture.

[0077] The conversion unit can learn the speaker's personal speaking style and habits when converting sign language content into natural language, and provide individually optimized translations. For example, the conversion unit builds a system that learns the speaker's personal speaking style and habits when converting sign language content into natural language. For example, it optimizes the translation based on the speaker's past data. The conversion unit also develops an algorithm that learns the speaker's personal speaking style and habits in order to convert sign language content into natural language. For example, it analyzes the speaker's characteristics and reflects them in the translation. The conversion unit also builds a system that learns the speaker's personal speaking style and habits when converting sign language content into natural language, and provides individually optimized translations. For example, it creates a profile for each speaker. This makes it possible to learn the speaker's personal speaking style and habits and provide individually optimized translations.

[0078] The conversion unit can use the emotion estimation function to generate natural-looking words that reflect the emotions contained in the sign language content. The conversion unit, for example, uses the emotion estimation function to build a system that generates natural-looking words that reflect the emotions contained in the sign language content. For example, the conversion unit adjusts the tone of the sentence based on the emotion score. The conversion unit also develops an algorithm that uses the emotion estimation function to generate natural-looking words that reflect the emotions contained in the sign language content. For example, the conversion unit selects appropriate expressions based on the emotion score. The conversion unit also builds a system that uses the emotion estimation function to generate natural-looking words that reflect the emotions contained in the sign language content. For example, if the emotion score is high, the conversion unit generates sentences that emphasize the emotions. As a result, by using the emotion estimation function to generate natural-looking words that reflect the emotions contained in the sign language content, more emotional communication is possible.

[0079] The display unit uses speech synthesis technology to output the generated natural-sounding sentences as audio, making it possible to accommodate visually impaired people. The display unit, for example, builds a system that uses speech synthesis technology to output the generated natural-sounding sentences as audio. For example, a speech output function is added to a smartphone or wearable device. The display unit also develops an algorithm that uses speech synthesis technology to output the generated natural-sounding sentences as audio. For example, the tone and speed of the voice are adjusted to generate natural-sounding voices. The display unit also builds a system that uses speech synthesis technology to output the generated natural-sounding sentences as audio, making it possible to accommodate visually impaired people. For example, a wearable device with a speech output function is developed. This makes it possible to output the generated natural-sounding sentences as audio using speech synthesis technology, making it possible to accommodate visually impaired people.

[0080] The display unit uses AR technology to display the generated natural-looking sentences superimposed on real space, thereby realizing intuitive communication. The display unit, for example, builds a system that uses AR technology to display the generated natural-looking sentences superimposed on real space. For example, the sentences are displayed in real space using a smartphone camera. The display unit also develops an algorithm that uses AR technology to display the generated natural-looking sentences superimposed on real space. For example, the position and size of the sentences are adjusted before displaying them. The display unit also builds a system that uses AR technology to display the generated natural-looking sentences superimposed on real space, thereby realizing intuitive communication. For example, the sentences are displayed using AR glasses. In this way, intuitive communication is realized by displaying the generated natural-looking sentences superimposed on real space using AR technology.

[0081] The display unit can add emphasis to emotions in sentences generated using the emotion estimation function, thereby providing emotionally rich communication. The display unit, for example, builds a system that adds emphasis to emotions in sentences generated using the emotion estimation function. For example, the display unit adjusts the tone and expression of the sentence based on the emotion score. The display unit also develops an algorithm that uses the emotion estimation function to add emphasis to the generated sentences. For example, the display unit selects appropriate expressions based on the emotion score. The display unit also builds a system that adds emphasis to emotions in sentences generated using the emotion estimation function, thereby providing emotionally rich communication. For example, if the emotion score is high, the display unit generates sentences with emphasized emotions. As a result, emotionally rich communication is provided by adding emphasis to sentences generated using the emotion estimation function.

[0082] The display unit can display the generated natural-sounding sentences not only on smartphones and wearable devices, but also on smart glasses and projectors, enabling use on various devices. The display unit, for example, builds a system that displays the generated natural-sounding sentences using smart glasses and projectors. For example, it displays the sentences on smart glasses. The display unit also develops an algorithm that uses not only smartphones and wearable devices, but also smart glasses and projectors, to make the generated natural-sounding sentences available on various devices. For example, it provides an optimal display method for each device. The display unit also builds a system that displays the generated natural-sounding sentences not only on smartphones and wearable devices, but also on smart glasses and projectors, enabling use on various devices. For example, it projects the sentences onto a wall using a projector. As a result, the generated natural-sounding sentences can be displayed not only on smartphones and wearable devices, but also on smart glasses and projectors, enabling use on various devices.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The sign language recognition unit recognizes the hand movements of the person speaking sign language in real time. For example, the sign language recognition unit captures hand movements using a camera or sensor and analyzes the data. The sign language recognition unit also converts the hand movement data into sign language words or phrases. For example, the sign language recognition unit recognizes hand movements that express "hello" in sign language and acquires them as text data. Step 2: The conversion unit converts the sign language content recognized by the sign language recognition unit into natural language. For example, the conversion unit converts sign language words and phrases into natural language based on the context of the surrounding text. The conversion unit also uses generative AI to analyze the sign language content and the surrounding text to generate an appropriate natural language sentence. For example, if the sign language phrase is "Hello, how are you?", the conversion unit analyzes the sign language content and generates the natural sentence "Hello, how are you?" Step 3: The display unit displays the natural language converted by the conversion unit via a smartphone or wearable device. For example, the display unit displays the generated text on a smartphone screen and communicates with the other person by showing it to them. The display unit can also display the sign language content in real time using a wearable device. For example, the display unit displays the sign language content using a smartwatch or AR glasses.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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 the hand movements of a person speaking sign language in real time; a conversion unit that converts the content of the sign language recognized by the sign language recognition unit into natural language; a display unit that displays the natural language converted by the conversion unit via a smartphone or a wearable device. A system characterized by:

2. The sign language recognition unit Analyze not only the hand movements but also the facial expressions and body movements simultaneously to perform more accurate sign language recognition.

2. The system of claim 1.

3. The sign language recognition unit 3D modeling is performed using multiple cameras and sensors, and the hand movements are analyzed in three dimensions.

2. The system of claim 1.

4. The sign language recognition unit Estimating the emotion of the person speaking the sign language and correcting the interpretation of the sign language based on the emotion.

2. The system of claim 1.

5. The sign language recognition unit The technology for recognizing sign language movements is applied to gesture recognition and body language analysis, and used to analyze the different communication methods.

2. The system of claim 1.

6. The sign language recognition unit Applying sign language recognition technology to the analysis of sports and dance movements for performance evaluation and training 2. The system of claim 1.

7. The sign language recognition unit Displaying the emotions of the sign language speaker in real time to improve the quality of communication 2. The system of claim 1.

8. The conversion unit When converting the sign language content into the natural language, the nuances of language according to region and culture are taken into consideration to provide a more appropriate translation.

2. The system of claim 1.

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