System

Abstract

An object of the system according to the embodiment is to smoothly communicate with a person who does not master sign language.SOLUTION: A system includes a sign language recognition unit, a character generation unit, and a voice generation unit. The sign language recognition unit recognizes a sign language action. The character generation unit converts the sign language action recognized by the sign language recognition unit into character information. The sound generation unit converts the character information generated by the character generation unit into sound.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to communicate with people who have not mastered sign language, making it difficult to have smooth conversations.

[0005] The system according to the embodiment aims to facilitate smooth communication even with people who have not mastered sign language. [Means for solving the problem]

[0006] A system according to an embodiment includes a sign language recognition unit, a character generation unit, and a speech generation unit. The sign language recognition unit recognizes sign language actions. The character generation unit converts the sign language actions recognized by the sign language recognition unit into text information. The speech generation unit converts the text information generated by the character generation unit into speech. [Effects of the Invention]

[0007] The system according to the embodiment allows smooth communication even with people who have not mastered sign language. [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) The sign language interpretation system according to the embodiment of the present invention uses sign language interpretation AI that has been trained to understand sign language as a language, enabling a smooth conversation experience regardless of whether the person is deaf or hard of hearing. This allows the sign language interpretation system to communicate freely in sign language according to the purpose by transcribing sign language and generating speech.

[0029] A sign language interpretation system according to an embodiment includes a sign language recognition unit, a character generation unit, and a speech generation unit. The sign language recognition unit recognizes sign language movements. For example, the sign language recognition unit captures sign language movements using a camera or a sensor and analyzes the movements. The sign language recognition unit can also analyze hand movements, finger shapes, and movement speed. For example, the sign language recognition unit captures sign language movements with a camera and recognizes hand movements and finger shapes using a movement analysis algorithm. The sign language recognition unit can also measure the movement speed using a sensor and analyze the sign language movements. The character generation unit converts the sign language movements recognized by the sign language recognition unit into text information. For example, the character generation unit analyzes sign language movements and generates text information corresponding to the movements. The character generation unit can also analyze sign language movements in real time and generate text information. For example, the character generation unit analyzes sign language movements and generates text information corresponding to the movements in real time. The speech generation unit converts the text information generated by the character generation unit into speech. For example, the speech generation unit analyzes text information and generates speech corresponding to the text information. The speech generation unit can also generate natural speech using speech synthesis technology. For example, the speech generation unit analyzes text information and generates speech with natural pronunciation using speech synthesis technology. As a result, the sign language interpretation system according to the embodiment recognizes sign language gestures and converts them into text information and speech, thereby realizing a smooth conversation experience regardless of whether the person is deaf or not. For example, the sign language interpretation system converts a conversation using sign language into text information or speech in real time, supporting smooth communication between people who are deaf and people who can hear. The sign language interpretation system can also record a conversation using sign language and later play it back as text information or speech. For example, the sign language interpretation system can record sign language gestures, analyze the recorded data to generate text information or speech, and later play it back.

[0030] The sign language recognition unit analyzes not only hand movements but also facial expressions and body movements to generate more accurate text information. For example, the sign language recognition unit analyzes not only hand movements but also facial expressions and body movements simultaneously to more accurately understand the meaning of the sign language and generate text information. For example, when expressing "thank you" in sign language, the unit analyzes facial expressions and body movements to accurately express gratitude as text information. The sign language recognition unit also analyzes hand movements, facial expressions, and body movements in an integrated manner to understand the context of the sign language. For example, when expressing "good morning" in sign language, the unit analyzes the smiling face and forward leaning body posture to generate text information containing the nuance of greeting. The sign language recognition unit also analyzes hand movements, facial expressions, and body movements in real time to accurately capture the meaning of the sign language. For example, when expressing "goodbye" in sign language, the unit analyzes the sad facial expression and backward leaning body movement to generate text information containing the emotion of farewell. This allows for more accurate recognition of sign language actions and generation of text information, making it possible to accurately convey the meaning of the sign language.

[0031] The sign language recognition unit may have a function for automatically adjusting the background, lighting, and camera angle of the environment in which the sign language is used. For example, the sign language recognition unit automatically adjusts the background of the environment in which the sign language is used so that the sign language actions can be clearly recognized. For example, if the background is complex, a function for blurring the background can be added to make the hand movements stand out. The sign language recognition unit also automatically adjusts the brightness of the lighting so that the sign language actions are easier to see. For example, in a dark environment, the lighting can be brightened so that the hand movements can be clearly recognized. The sign language recognition unit also automatically adjusts the angle of the camera so that the sign language actions are captured at the optimal angle. For example, the camera can be adjusted to an angle that makes it easy to see the hand movements of the person using sign language, thereby improving recognition accuracy. In this way, the sign language recognition accuracy can be improved by automatically adjusting the environment in which the sign language is used.

[0032] The sign language recognition unit can have a function to display sign language as subtitles in real time and add sign language subtitles to video content. The sign language recognition unit, for example, analyzes sign language movements in real time and displays them as subtitles. For example, when saying "hello" in sign language, the subtitle "hello" is added to the video content in real time. The sign language recognition unit also analyzes sign language movements and automatically adds sign language subtitles to video content. For example, when saying "thank you" in sign language, the subtitle "thank you" is added to the video content. The sign language recognition unit also analyzes sign language movements and automatically adjusts the display position and font size of the subtitles. For example, when saying "goodbye" in sign language, the subtitles are adjusted so that they are displayed in an easy-to-read position. In this way, sign language is displayed as subtitles in real time, adding sign language subtitles to video content and supporting communication using sign language.

[0033] The sign language recognition unit can be equipped with a function to automatically translate sign language into other languages ​​and support international communication. The sign language recognition unit, for example, analyzes sign language movements and automatically translates into other languages. For example, when saying "hello" in sign language, it translates it into "Hello" in English and displays it. The sign language recognition unit also analyzes sign language movements and automatically translates it into multiple languages. For example, when saying "thank you" in sign language, it translates it into "Merci" in French or "Gracias" in Spanish and displays it. The sign language recognition unit also analyzes sign language movements and displays the translation results in real time. For example, when saying "goodbye" in sign language, the translation results are displayed in real time to support international communication. In this way, automatic translation of sign language into other languages ​​supports international communication.

[0034] The sign language recognition unit analyzes not only hand movements but also mouth movements and the presence or absence of vocalizations to generate more natural-sounding speech. For example, the sign language recognition unit analyzes not only hand movements but also mouth movements and the presence or absence of vocalizations to generate more natural-sounding speech. For example, when expressing "good morning" in sign language, the unit analyzes mouth movements and vocalizations to generate natural-sounding speech. The sign language recognition unit also comprehensively analyzes hand movements, mouth movements, and the presence or absence of vocalizations to understand the context of the sign language. For example, when expressing "thank you" in sign language, the unit analyzes mouth movements and vocalizations to generate natural-sounding speech that conveys gratitude. The sign language recognition unit also analyzes hand movements, mouth movements, and the presence or absence of vocalizations in real time to accurately capture the meaning of the sign language. For example, when expressing "goodbye" in sign language, the unit analyzes mouth movements and vocalizations to generate natural-sounding speech that conveys the emotion of farewell. This allows the unit to recognize sign language movements more naturally and generate speech, thereby accurately conveying the meaning of the sign language.

[0035] The sign language recognition unit may have a function for customizing the tone and accent of the voice to match the individuality of the person using the sign language. The sign language recognition unit customizes the tone and accent of the voice to match the individuality of the person using the sign language, for example. For example, when expressing "hello" in sign language, a voice that reflects the characteristics of the person's voice is generated. The sign language recognition unit also analyzes the tone and accent of the person using the sign language and reflects these characteristics in the voice generation. For example, when expressing "thank you" in sign language, a voice that reflects the tone and accent of the person using the sign language is generated. The sign language recognition unit also provides a function for adjusting the tone and accent of the voice to match the individuality of the person using the sign language. For example, when expressing "goodbye" in sign language, a voice that reflects the characteristics of the person's voice is generated. In this way, voice customization to match the individuality of the person using the sign language enables more natural communication.

[0036] The sign language recognition unit can have a function to convert sign language into not only speech but also music and sound effects. The sign language recognition unit, for example, analyzes sign language movements and converts them into music as well as speech. For example, when expressing "good morning" in sign language, refreshing morning music is generated. The sign language recognition unit also analyzes sign language movements and converts them into sound effects. For example, when expressing "thank you" in sign language, a sound effect expressing gratitude is generated. The sign language recognition unit also analyzes sign language movements and generates speech that combines music and sound effects. For example, when expressing "goodbye" in sign language, music and sound effects expressing the emotion of farewell are generated. In this way, converting sign language into music and sound effects enables applications in the entertainment field.

[0037] The sign language recognition unit can have a function to automatically translate sign language into other spoken languages ​​and build a multilingual voice interpretation system. The sign language recognition unit, for example, analyzes sign language movements and automatically translates them into other spoken languages. For example, when expressing "hello" in sign language, it translates it into "Hello" in English and outputs it as voice. The sign language recognition unit also analyzes sign language movements and automatically translates it into multiple spoken languages. For example, when expressing "thank you" in sign language, it translates it into "Merci" in French or "Gracias" in Spanish and outputs it as voice. The sign language recognition unit also analyzes sign language movements and outputs the translation results as voice in real time. For example, when expressing "goodbye" in sign language, it outputs the translation results as voice in real time, building a multilingual voice interpretation system. In this way, by automatically translating sign language into other spoken languages, a multilingual voice interpretation system can be built and international communication can be supported.

[0038] The sign language recognition unit analyzes not only the sign language actions but also facial expressions and body movements to generate more natural sign language expressions. For example, the sign language recognition unit simultaneously analyzes not only the sign language actions but also facial expressions and body movements to generate more natural sign language expressions. For example, when expressing "thank you" in sign language, the unit analyzes the smiling expression and forward leaning body posture to generate a natural sign language expression that includes gratitude. The sign language recognition unit also analyzes the sign language actions, facial expressions, and body movements in an integrated manner to understand the context of the sign language. For example, when expressing "good morning" in sign language, the unit analyzes the smiling expression on the face and the forward leaning body posture to generate a natural sign language expression that includes the nuance of greeting. The sign language recognition unit also analyzes the sign language actions, facial expressions, and body movements in real time to accurately capture the meaning of the sign language. For example, when expressing "goodbye" in sign language, the unit analyzes the sad expression on the face and the backward leaning body posture to generate a natural sign language expression that includes the emotion of farewell. This allows the system to analyze not only sign language actions but also facial expressions and body movements, generating more natural sign language expressions and supporting two-way communication.

[0039] The sign language recognition unit may have a function for customizing sign language expressions to suit the personality and habits of the person using the sign language. The sign language recognition unit customizes sign language expressions to suit the personality and habits of the person using the sign language, for example. For example, when saying "hello" in sign language, a sign language expression that reflects the unique movements of the person using the sign language is generated. The sign language recognition unit also analyzes the characteristics of the movements of the person using the sign language and reflects those characteristics in the sign language expression. For example, when saying "thank you" in sign language, a sign language expression that reflects the unique movements of the person using the sign language is generated. The sign language recognition unit also provides a function for adjusting sign language expressions to suit the personality and habits of the person using the sign language. For example, when saying "goodbye" in sign language, a sign language expression that reflects the unique movements of the person using the sign language is generated. In this way, by customizing sign language expressions to suit the personality and habits of the person using the sign language, more natural communication is possible.

[0040] The sign language recognition unit can have a function of converting sign language into an avatar in real time to support communication in a virtual space. The sign language recognition unit, for example, analyzes sign language movements in real time and converts them into an avatar. For example, when saying "hello" in sign language, the avatar generates a movement to express "hello" in sign language. The sign language recognition unit also analyzes sign language movements to support communication in a virtual space. For example, when saying "thank you" in sign language, the avatar generates a movement to express "thank you" in sign language. The sign language recognition unit also analyzes sign language movements and generates avatar movements in real time. For example, when saying "goodbye" in sign language, the avatar generates a movement to express "goodbye" in sign language. In this way, communication in a virtual space is supported by converting sign language into an avatar in real time.

[0041] The sign language recognition unit may have a function of automatically translating sign language into other gesture languages ​​and supporting communication between different gesture languages. The sign language recognition unit, for example, analyzes sign language movements and automatically translates them into other gesture languages. For example, when expressing "hello" in sign language, it translates it into "Hello" in American Sign Language and displays it. The sign language recognition unit also analyzes sign language movements and automatically translates them into multiple gesture languages. For example, when expressing "thank you" in sign language, it translates it into "Thank you" in British Sign Language or "Merci" in French Sign Language and displays it. The sign language recognition unit also analyzes sign language movements and displays the translation results in real time. For example, when expressing "goodbye" in sign language, it displays the translation results in real time to support communication between different gesture languages. In this way, by automatically translating sign language into other gesture languages, communication between different gesture languages ​​can be supported.

[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 can not only recognize sign language movements, but also have a function to monitor the health condition of the person using the sign language. For example, when analyzing hand movements, finger shape, and movement speed, the sign language recognition unit can detect hand tremors or unnatural movements and notify the user of any abnormal health condition. The sign language recognition unit can also measure the heart rate and body temperature of the person using the sign language using a sensor to monitor the health condition in real time. For example, if the heart rate of the person using the sign language suddenly increases, the unit can notify the user of the possibility of stress or poor health. The sign language recognition unit can also record the health condition of the person using the sign language, which can be used for regular health checks. This allows the health condition of the person using the sign language to be monitored and any abnormalities to be detected early, thereby supporting health management.

[0044] The sign language recognition unit can analyze not only hand movements but also environmental sounds around the person signing, and can be equipped with a function to complement the meaning of the sign language. For example, the sign language recognition unit uses a microphone to pick up sounds generated around the person signing, analyzes the sounds, and understands the context of the sign language. The sign language recognition unit can also analyze environmental sounds around the person signing and generate text information that complements the meaning of the sign language. For example, when expressing "a car is coming" in sign language, the unit analyzes the sounds of surrounding cars and generates text information indicating the approach of the car. The sign language recognition unit can also analyze environmental sounds around the person signing and link them to sign language movements to generate more accurate sign language expressions. This enables more accurate communication by analyzing environmental sounds around the person signing and complementing the meaning of the sign language.

[0045] The sign language recognition unit can have a function of analyzing the gestures of a person using sign language and suggesting an appropriate action based on the gestures. For example, when a person using sign language expresses "I want a drink," the sign language recognition unit can suggest the location of a nearby vending machine. Also, when a person using sign language expresses "I want directions," the sign language recognition unit can suggest the location of the nearest information center. Also, when a person using sign language expresses "I need help," the sign language recognition unit can suggest the location of a nearby first-aid station. In this way, communication using sign language can be supported by analyzing the gestures of a person using sign language and suggesting an appropriate action based on the gestures.

[0046] The sign language recognition unit can have a function of analyzing the movements of a person using sign language and suggesting exercises based on those movements. For example, the sign language recognition unit can analyze the movements of a person using sign language and suggest shoulder stretches if the person has stiff shoulders. The sign language recognition unit can also analyze the movements of a person using sign language and suggest lower back exercises if the person has lower back pain. The sign language recognition unit can also analyze the movements of a person using sign language and suggest exercises to relax if the person is feeling tired all over. In this way, health management can be supported by analyzing the movements of a person using sign language and suggesting exercises based on those movements.

[0047] The sign language recognition unit may have a function of analyzing the movements of a person using sign language and providing appropriate learning content based on the movements. For example, the sign language recognition unit may analyze the movements of a person using sign language and provide learning materials to support learning of sign language. The sign language recognition unit may also analyze the movements of a person using sign language and provide video tutorials to support sign language practice. The sign language recognition unit may also analyze the movements of a person using sign language and provide exercises to support improvement of sign language proficiency. In this way, sign language learning can be supported by analyzing the movements of a person using sign language and providing appropriate learning content based on the movements.

[0048] The sign language recognition unit can have a function of analyzing the movements of a person using sign language and suggesting an appropriate relaxation method based on those movements. For example, the sign language recognition unit can analyze the movements of a person using sign language and suggest deep breathing if the person feels tense. The sign language recognition unit can also analyze the movements of a person using sign language and suggest light stretching if the person feels tired. The sign language recognition unit can also analyze the movements of a person using sign language and suggest music for relaxation. In this way, health management can be supported by analyzing the movements of a person using sign language and suggesting an appropriate relaxation method based on those movements.

[0049] The sign language recognition unit can have a function of analyzing the movements of a person using sign language and suggesting an appropriate meal based on the movements. For example, the sign language recognition unit can analyze the movements of a person using sign language and suggest a high-energy meal if the person is lacking in energy. The sign language recognition unit can also analyze the movements of a person using sign language and suggest a balanced meal if the person's nutritional balance is unbalanced. The sign language recognition unit can also analyze the movements of a person using sign language and suggest an easy-to-digest meal if the person is not feeling well. In this way, health management can be supported by analyzing the movements of a person using sign language and suggesting an appropriate meal based on the movements.

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

[0051] Step 1: The sign language recognition unit recognizes sign language movements. For example, the sign language recognition unit captures sign language movements using a camera or a sensor and analyzes the movements. The sign language recognition unit can also analyze hand movements, finger shapes, and movement speed. For example, the sign language recognition unit can capture sign language movements with a camera and recognize hand movements and finger shapes using a movement analysis algorithm. The sign language recognition unit can also measure the movement speed using a sensor and analyze the sign language movements. Step 2: The character generation unit converts the sign language actions recognized by the sign language recognition unit into character information. For example, the character generation unit analyzes the sign language actions and generates character information corresponding to the actions. The character generation unit can also analyze the sign language actions in real time and generate character information. For example, the character generation unit analyzes the sign language actions and generates character information corresponding to the actions in real time. Step 3: The voice generation unit converts the text information generated by the text generation unit into voice. For example, the voice generation unit analyzes the text information and generates voice corresponding to the text information. The voice generation unit can also generate natural voice using voice synthesis technology. For example, the voice generation unit analyzes the text information and generates voice with natural pronunciation using voice synthesis technology.

[0052] (Example 2) The sign language interpretation system according to the embodiment of the present invention uses sign language interpretation AI that has been trained to understand sign language as a language, enabling a smooth conversation experience regardless of whether the person is deaf or hard of hearing. This allows the sign language interpretation system to communicate freely in sign language according to the purpose by transcribing sign language and generating speech.

[0053] A sign language interpretation system according to an embodiment includes a sign language recognition unit, a character generation unit, and a speech generation unit. The sign language recognition unit recognizes sign language movements. For example, the sign language recognition unit captures sign language movements using a camera or a sensor and analyzes the movements. The sign language recognition unit can also analyze hand movements, finger shapes, and movement speed. For example, the sign language recognition unit captures sign language movements with a camera and recognizes hand movements and finger shapes using a movement analysis algorithm. The sign language recognition unit can also measure the movement speed using a sensor and analyze the sign language movements. The character generation unit converts the sign language movements recognized by the sign language recognition unit into text information. For example, the character generation unit analyzes sign language movements and generates text information corresponding to the movements. The character generation unit can also analyze sign language movements in real time and generate text information. For example, the character generation unit analyzes sign language movements and generates text information corresponding to the movements in real time. The speech generation unit converts the text information generated by the character generation unit into speech. For example, the speech generation unit analyzes text information and generates speech corresponding to the text information. The speech generation unit can also generate natural speech using speech synthesis technology. For example, the speech generation unit analyzes text information and generates speech with natural pronunciation using speech synthesis technology. As a result, the sign language interpretation system according to the embodiment recognizes sign language gestures and converts them into text information and speech, thereby realizing a smooth conversation experience regardless of whether the person is deaf or not. For example, the sign language interpretation system converts a conversation using sign language into text information or speech in real time, supporting smooth communication between people who are deaf and people who can hear. The sign language interpretation system can also record a conversation using sign language and later play it back as text information or speech. For example, the sign language interpretation system can record sign language gestures, analyze the recorded data to generate text information or speech, and later play it back.

[0054] The sign language recognition unit analyzes not only hand movements but also facial expressions and body movements to generate more accurate text information. For example, the sign language recognition unit analyzes not only hand movements but also facial expressions and body movements simultaneously to more accurately understand the meaning of the sign language and generate text information. For example, when expressing "thank you" in sign language, the unit analyzes facial expressions and body movements to accurately express gratitude as text information. The sign language recognition unit also analyzes hand movements, facial expressions, and body movements in an integrated manner to understand the context of the sign language. For example, when expressing "good morning" in sign language, the unit analyzes the smiling face and forward leaning body posture to generate text information containing the nuance of greeting. The sign language recognition unit also analyzes hand movements, facial expressions, and body movements in real time to accurately capture the meaning of the sign language. For example, when expressing "goodbye" in sign language, the unit analyzes the sad facial expression and backward leaning body movement to generate text information containing the emotion of farewell. This allows for more accurate recognition of sign language actions and generation of text information, making it possible to accurately convey the meaning of the sign language.

[0055] The sign language recognition unit may have a function for automatically adjusting the background, lighting, and camera angle of the environment in which the sign language is used. For example, the sign language recognition unit automatically adjusts the background of the environment in which the sign language is used so that the sign language actions can be clearly recognized. For example, if the background is complex, a function for blurring the background can be added to make the hand movements stand out. The sign language recognition unit also automatically adjusts the brightness of the lighting so that the sign language actions are easier to see. For example, in a dark environment, the lighting can be brightened so that the hand movements can be clearly recognized. The sign language recognition unit also automatically adjusts the angle of the camera so that the sign language actions are captured at the optimal angle. For example, the camera can be adjusted to an angle that makes it easy to see the hand movements of the person using sign language, thereby improving recognition accuracy. In this way, the sign language recognition accuracy can be improved by automatically adjusting the environment in which the sign language is used.

[0056] The sign language recognition unit can use the emotion estimation function to analyze the emotion of a person using sign language and generate text information corresponding to that emotion. The sign language recognition unit, for example, analyzes the facial expressions and body movements of the person using sign language to estimate the emotion. For example, when expressing "thank you" in sign language, the unit analyzes a smiling expression and generates text information containing a feeling of gratitude. The sign language recognition unit also analyzes the tone and speed of the person using voice to estimate the emotion. For example, when expressing "I'm sorry" in sign language, if the voice tone is low and the speed is slow, text information containing an apology is generated. The sign language recognition unit also analyzes the overall movements of the person using sign language to estimate the emotion. For example, when expressing "goodbye" in sign language, if the body movements are slow and the expression is sad, text information containing an emotion of farewell is generated. In this way, by analyzing the emotion of a person using sign language and generating text information corresponding to that emotion, communication that includes emotion is possible.

[0057] The sign language recognition unit can have a function to display sign language as subtitles in real time and add sign language subtitles to video content. The sign language recognition unit, for example, analyzes sign language movements in real time and displays them as subtitles. For example, when saying "hello" in sign language, the subtitle "hello" is added to the video content in real time. The sign language recognition unit also analyzes sign language movements and automatically adds sign language subtitles to video content. For example, when saying "thank you" in sign language, the subtitle "thank you" is added to the video content. The sign language recognition unit also analyzes sign language movements and automatically adjusts the display position and font size of the subtitles. For example, when saying "goodbye" in sign language, the subtitles are adjusted so that they are displayed in an easy-to-read position. In this way, sign language is displayed as subtitles in real time, adding sign language subtitles to video content and supporting communication using sign language.

[0058] The sign language recognition unit can be equipped with a function to automatically translate sign language into other languages ​​and support international communication. The sign language recognition unit, for example, analyzes sign language movements and automatically translates into other languages. For example, when saying "hello" in sign language, it translates it into "Hello" in English and displays it. The sign language recognition unit also analyzes sign language movements and automatically translates it into multiple languages. For example, when saying "thank you" in sign language, it translates it into "Merci" in French or "Gracias" in Spanish and displays it. The sign language recognition unit also analyzes sign language movements and displays the translation results in real time. For example, when saying "goodbye" in sign language, the translation results are displayed in real time to support international communication. In this way, automatic translation of sign language into other languages ​​supports international communication.

[0059] The sign language recognition unit can be equipped with a function that uses an emotion estimation function to analyze the emotion of a person using sign language and provide feedback according to that emotion. The sign language recognition unit, for example, analyzes the emotion of a person using sign language and provides feedback according to that emotion. For example, when expressing "thank you" in sign language, feedback that emphasizes gratitude is provided. The sign language recognition unit also analyzes the emotion of a person using sign language and provides feedback that elicits positive emotions. For example, when expressing "I'm sorry" in sign language, encouraging feedback is provided. The sign language recognition unit also analyzes the emotion of a person using sign language and provides feedback that alleviates negative emotions. For example, when expressing "goodbye" in sign language, feedback that alleviates the sadness of parting is provided. In this way, by analyzing the emotion of a person using sign language and providing feedback according to that emotion, communication that includes emotions becomes possible.

[0060] The sign language recognition unit analyzes not only hand movements but also mouth movements and the presence or absence of vocalizations to generate more natural-sounding speech. For example, the sign language recognition unit analyzes not only hand movements but also mouth movements and the presence or absence of vocalizations to generate more natural-sounding speech. For example, when expressing "good morning" in sign language, the unit analyzes mouth movements and vocalizations to generate natural-sounding speech. The sign language recognition unit also comprehensively analyzes hand movements, mouth movements, and the presence or absence of vocalizations to understand the context of the sign language. For example, when expressing "thank you" in sign language, the unit analyzes mouth movements and vocalizations to generate natural-sounding speech that conveys gratitude. The sign language recognition unit also analyzes hand movements, mouth movements, and the presence or absence of vocalizations in real time to accurately capture the meaning of the sign language. For example, when expressing "goodbye" in sign language, the unit analyzes mouth movements and vocalizations to generate natural-sounding speech that conveys the emotion of farewell. This allows the unit to recognize sign language movements more naturally and generate speech, thereby accurately conveying the meaning of the sign language.

[0061] The sign language recognition unit may have a function for customizing the tone and accent of the voice to match the individuality of the person using the sign language. The sign language recognition unit customizes the tone and accent of the voice to match the individuality of the person using the sign language, for example. For example, when expressing "hello" in sign language, a voice that reflects the characteristics of the person's voice is generated. The sign language recognition unit also analyzes the tone and accent of the person using the sign language and reflects these characteristics in the voice generation. For example, when expressing "thank you" in sign language, a voice that reflects the tone and accent of the person using the sign language is generated. The sign language recognition unit also provides a function for adjusting the tone and accent of the voice to match the individuality of the person using the sign language. For example, when expressing "goodbye" in sign language, a voice that reflects the characteristics of the person's voice is generated. In this way, voice customization to match the individuality of the person using the sign language enables more natural communication.

[0062] The sign language recognition unit can use the emotion estimation function to analyze the emotion of a person using sign language and generate an audio tone corresponding to that emotion. The sign language recognition unit, for example, analyzes the emotion of a person using sign language and generates an audio tone corresponding to that emotion. For example, when expressing "thank you" in sign language, a warm audio tone reflecting gratitude is generated. The sign language recognition unit also analyzes the emotion of a person using sign language and generates an audio tone that elicits positive emotions. For example, when expressing "congratulations" in sign language, a bright audio tone reflecting joy is generated. The sign language recognition unit also analyzes the emotion of a person using sign language and generates an audio tone that softens negative emotions. For example, when expressing "I'm sorry" in sign language, a calm audio tone reflecting an apology is generated. In this way, by analyzing the emotion of a person using sign language and generating an audio tone corresponding to that emotion, communication that includes emotions is possible.

[0063] The sign language recognition unit can have a function to convert sign language into not only speech but also music and sound effects. The sign language recognition unit, for example, analyzes sign language movements and converts them into music as well as speech. For example, when expressing "good morning" in sign language, refreshing morning music is generated. The sign language recognition unit also analyzes sign language movements and converts them into sound effects. For example, when expressing "thank you" in sign language, a sound effect expressing gratitude is generated. The sign language recognition unit also analyzes sign language movements and generates speech that combines music and sound effects. For example, when expressing "goodbye" in sign language, music and sound effects expressing the emotion of farewell are generated. In this way, converting sign language into music and sound effects enables applications in the entertainment field.

[0064] The sign language recognition unit can have a function to automatically translate sign language into other spoken languages ​​and build a multilingual voice interpretation system. The sign language recognition unit, for example, analyzes sign language movements and automatically translates them into other spoken languages. For example, when expressing "hello" in sign language, it translates it into "Hello" in English and outputs it as voice. The sign language recognition unit also analyzes sign language movements and automatically translates it into multiple spoken languages. For example, when expressing "thank you" in sign language, it translates it into "Merci" in French or "Gracias" in Spanish and outputs it as voice. The sign language recognition unit also analyzes sign language movements and outputs the translation results as voice in real time. For example, when expressing "goodbye" in sign language, it outputs the translation results as voice in real time, building a multilingual voice interpretation system. In this way, by automatically translating sign language into other spoken languages, a multilingual voice interpretation system can be built and international communication can be supported.

[0065] The sign language recognition unit can be equipped with a function to analyze the emotion of a person using sign language by using an emotion estimation function and provide audio feedback according to that emotion. The sign language recognition unit, for example, analyzes the emotion of a person using sign language and provides audio feedback according to that emotion. For example, when expressing "thank you" in sign language, warm audio feedback reflecting gratitude is provided. The sign language recognition unit also analyzes the emotion of a person using sign language and provides audio feedback that elicits positive emotions. For example, when expressing "congratulations" in sign language, bright audio feedback reflecting joy is provided. The sign language recognition unit also analyzes the emotion of a person using sign language and provides audio feedback that softens negative emotions. For example, when expressing "I'm sorry" in sign language, calm audio feedback reflecting an apology is provided. In this way, by analyzing the emotion of a person using sign language and providing audio feedback according to that emotion, communication that includes emotions is possible.

[0066] The sign language recognition unit analyzes not only the sign language actions but also facial expressions and body movements to generate more natural sign language expressions. For example, the sign language recognition unit simultaneously analyzes not only the sign language actions but also facial expressions and body movements to generate more natural sign language expressions. For example, when expressing "thank you" in sign language, the unit analyzes the smiling expression and forward leaning body posture to generate a natural sign language expression that includes gratitude. The sign language recognition unit also analyzes the sign language actions, facial expressions, and body movements in an integrated manner to understand the context of the sign language. For example, when expressing "good morning" in sign language, the unit analyzes the smiling expression on the face and the forward leaning body posture to generate a natural sign language expression that includes the nuance of greeting. The sign language recognition unit also analyzes the sign language actions, facial expressions, and body movements in real time to accurately capture the meaning of the sign language. For example, when expressing "goodbye" in sign language, the unit analyzes the sad expression on the face and the backward leaning body posture to generate a natural sign language expression that includes the emotion of farewell. This allows the system to analyze not only sign language actions but also facial expressions and body movements, generating more natural sign language expressions and supporting two-way communication.

[0067] The sign language recognition unit may have a function for customizing sign language expressions to suit the personality and habits of the person using the sign language. The sign language recognition unit customizes sign language expressions to suit the personality and habits of the person using the sign language, for example. For example, when saying "hello" in sign language, a sign language expression that reflects the unique movements of the person using the sign language is generated. The sign language recognition unit also analyzes the characteristics of the movements of the person using the sign language and reflects those characteristics in the sign language expression. For example, when saying "thank you" in sign language, a sign language expression that reflects the unique movements of the person using the sign language is generated. The sign language recognition unit also provides a function for adjusting sign language expressions to suit the personality and habits of the person using the sign language. For example, when saying "goodbye" in sign language, a sign language expression that reflects the unique movements of the person using the sign language is generated. In this way, by customizing sign language expressions to suit the personality and habits of the person using the sign language, more natural communication is possible.

[0068] The sign language recognition unit can use the emotion estimation function to analyze the emotion of a person using sign language and generate a sign language expression corresponding to that emotion. The sign language recognition unit, for example, analyzes the emotion of a person using sign language and generates a sign language expression corresponding to that emotion. For example, when expressing "thank you" in sign language, a warm sign language expression reflecting gratitude is generated. The sign language recognition unit also analyzes the emotion of a person using sign language and generates a sign language expression that elicits positive emotions. For example, when expressing "congratulations" in sign language, a cheerful sign language expression reflecting joy is generated. The sign language recognition unit also analyzes the emotion of a person using sign language and generates a sign language expression that softens negative emotions. For example, when expressing "I'm sorry" in sign language, a gentle sign language expression reflecting an apology is generated. In this way, by analyzing the emotion of a person using sign language and generating a sign language expression corresponding to that emotion, communication that includes emotions is possible.

[0069] The sign language recognition unit can have a function of converting sign language into an avatar in real time to support communication in a virtual space. The sign language recognition unit, for example, analyzes sign language movements in real time and converts them into an avatar. For example, when saying "hello" in sign language, the avatar generates a movement to express "hello" in sign language. The sign language recognition unit also analyzes sign language movements to support communication in a virtual space. For example, when saying "thank you" in sign language, the avatar generates a movement to express "thank you" in sign language. The sign language recognition unit also analyzes sign language movements and generates avatar movements in real time. For example, when saying "goodbye" in sign language, the avatar generates a movement to express "goodbye" in sign language. In this way, communication in a virtual space is supported by converting sign language into an avatar in real time.

[0070] The sign language recognition unit may have a function of automatically translating sign language into other gesture languages ​​and supporting communication between different gesture languages. The sign language recognition unit, for example, analyzes sign language movements and automatically translates them into other gesture languages. For example, when expressing "hello" in sign language, it translates it into "Hello" in American Sign Language and displays it. The sign language recognition unit also analyzes sign language movements and automatically translates them into multiple gesture languages. For example, when expressing "thank you" in sign language, it translates it into "Thank you" in British Sign Language or "Merci" in French Sign Language and displays it. The sign language recognition unit also analyzes sign language movements and displays the translation results in real time. For example, when expressing "goodbye" in sign language, it displays the translation results in real time to support communication between different gesture languages. In this way, by automatically translating sign language into other gesture languages, communication between different gesture languages ​​can be supported.

[0071] The sign language recognition unit can be equipped with a function that uses an emotion estimation function to analyze the emotion of a person using sign language and generate facial expressions and movements of an avatar according to that emotion. The sign language recognition unit, for example, analyzes the emotion of a person using sign language and generates facial expressions and movements of an avatar according to that emotion. For example, when expressing "thank you" in sign language, the avatar generates a movement to express "thank you" in sign language with a smile. The sign language recognition unit also analyzes the emotion of a person using sign language and generates facial expressions and movements of an avatar that elicit positive emotions. For example, when expressing "congratulations" in sign language, the avatar generates a movement to express "congratulations" in sign language with a happy expression. The sign language recognition unit also analyzes the emotion of a person using sign language and generates facial expressions and movements of an avatar that alleviate negative emotions. For example, when expressing "I'm sorry" in sign language, the avatar generates a movement to express "I'm sorry" in sign language with a calm expression. This makes it possible to analyze the emotions of the person signing and generate facial expressions and movements of the avatar that correspond to those emotions, thereby enabling communication that includes emotions.

[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 can not only recognize sign language movements, but also have a function to monitor the health condition of the person using the sign language. For example, when analyzing hand movements, finger shape, and movement speed, the sign language recognition unit can detect hand tremors or unnatural movements and notify the user of any abnormal health condition. The sign language recognition unit can also measure the heart rate and body temperature of the person using the sign language using a sensor to monitor the health condition in real time. For example, if the heart rate of the person using the sign language suddenly increases, the unit can notify the user of the possibility of stress or poor health. The sign language recognition unit can also record the health condition of the person using the sign language, which can be used for regular health checks. This allows the health condition of the person using the sign language to be monitored and any abnormalities to be detected early, thereby supporting health management.

[0074] The sign language recognition unit can analyze not only hand movements but also environmental sounds around the person signing, and can be equipped with a function to complement the meaning of the sign language. For example, the sign language recognition unit uses a microphone to pick up sounds generated around the person signing, analyzes the sounds, and understands the context of the sign language. The sign language recognition unit can also analyze environmental sounds around the person signing and generate text information that complements the meaning of the sign language. For example, when expressing "a car is coming" in sign language, the unit analyzes the sounds of surrounding cars and generates text information indicating the approach of the car. The sign language recognition unit can also analyze environmental sounds around the person signing and link them to sign language movements to generate more accurate sign language expressions. This enables more accurate communication by analyzing environmental sounds around the person signing and complementing the meaning of the sign language.

[0075] The sign language recognition unit can have a function of analyzing the gestures of a person using sign language and suggesting an appropriate action based on the gestures. For example, when a person using sign language expresses "I want a drink," the sign language recognition unit can suggest the location of a nearby vending machine. Also, when a person using sign language expresses "I want directions," the sign language recognition unit can suggest the location of the nearest information center. Also, when a person using sign language expresses "I need help," the sign language recognition unit can suggest the location of a nearby first-aid station. In this way, communication using sign language can be supported by analyzing the gestures of a person using sign language and suggesting an appropriate action based on the gestures.

[0076] The sign language recognition unit can be equipped with a function that uses the emotion estimation function to analyze the emotion of the person using sign language and provide music that corresponds to that emotion. For example, the sign language recognition unit can analyze the emotion of the person using sign language and provide calm music if the person wants to relax. The sign language recognition unit can also analyze the emotion of the person using sign language and provide lively music if the person wants to cheer up. The sign language recognition unit can also analyze the emotion of the person using sign language and provide soothing music if the person wants to ease sadness. In this way, communication that includes emotion is possible by analyzing the emotion of the person using sign language and providing music that corresponds to that emotion.

[0077] The sign language recognition unit can have a function of analyzing the movements of a person using sign language and suggesting exercises based on those movements. For example, the sign language recognition unit can analyze the movements of a person using sign language and suggest shoulder stretches if the person has stiff shoulders. The sign language recognition unit can also analyze the movements of a person using sign language and suggest lower back exercises if the person has lower back pain. The sign language recognition unit can also analyze the movements of a person using sign language and suggest exercises to relax if the person is feeling tired all over. In this way, health management can be supported by analyzing the movements of a person using sign language and suggesting exercises based on those movements.

[0078] The sign language recognition unit may have a function of analyzing the movements of a person using sign language and providing appropriate learning content based on the movements. For example, the sign language recognition unit may analyze the movements of a person using sign language and provide learning materials to support learning of sign language. The sign language recognition unit may also analyze the movements of a person using sign language and provide video tutorials to support sign language practice. The sign language recognition unit may also analyze the movements of a person using sign language and provide exercises to support improvement of sign language proficiency. In this way, sign language learning can be supported by analyzing the movements of a person using sign language and providing appropriate learning content based on the movements.

[0079] The sign language recognition unit can be equipped with a function that uses the emotion estimation function to analyze the emotion of the person using sign language and provide advice according to that emotion. For example, the sign language recognition unit can analyze the emotion of the person using sign language and provide advice to relax if the person is feeling stressed. The sign language recognition unit can also analyze the emotion of the person using sign language and provide advice to cheer up if the person is feeling depressed. The sign language recognition unit can also analyze the emotion of the person using sign language and provide advice to stay calm if the person is feeling angry. In this way, communication that includes emotion is possible by analyzing the emotion of the person using sign language and providing advice according to that emotion.

[0080] The sign language recognition unit can have a function of analyzing the movements of a person using sign language and suggesting an appropriate relaxation method based on those movements. For example, the sign language recognition unit can analyze the movements of a person using sign language and suggest deep breathing if the person feels tense. The sign language recognition unit can also analyze the movements of a person using sign language and suggest light stretching if the person feels tired. The sign language recognition unit can also analyze the movements of a person using sign language and suggest music for relaxation. In this way, health management can be supported by analyzing the movements of a person using sign language and suggesting an appropriate relaxation method based on those movements.

[0081] The sign language recognition unit can have a function of analyzing the movements of a person using sign language and suggesting an appropriate meal based on the movements. For example, the sign language recognition unit can analyze the movements of a person using sign language and suggest a high-energy meal if the person is lacking in energy. The sign language recognition unit can also analyze the movements of a person using sign language and suggest a balanced meal if the person's nutritional balance is unbalanced. The sign language recognition unit can also analyze the movements of a person using sign language and suggest an easy-to-digest meal if the person is not feeling well. In this way, health management can be supported by analyzing the movements of a person using sign language and suggesting an appropriate meal based on the movements.

[0082] The sign language recognition unit can be equipped with a function that uses the emotion estimation function to analyze the emotion of the person using sign language and provide entertainment content according to that emotion. For example, the sign language recognition unit can analyze the emotion of the person using sign language and provide a calming movie if the person wants to relax. The sign language recognition unit can also analyze the emotion of the person using sign language and provide a comedy movie if the person wants to cheer up. The sign language recognition unit can also analyze the emotion of the person using sign language and provide an emotional movie if the person wants to alleviate sadness. In this way, communication that includes emotion is possible by analyzing the emotion of the person using sign language and providing entertainment content according to that emotion.

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

[0084] Step 1: The sign language recognition unit recognizes sign language movements. For example, the sign language recognition unit captures sign language movements using a camera or a sensor and analyzes the movements. The sign language recognition unit can also analyze hand movements, finger shapes, and movement speed. For example, the sign language recognition unit can capture sign language movements with a camera and recognize hand movements and finger shapes using a movement analysis algorithm. The sign language recognition unit can also measure the movement speed using a sensor and analyze the sign language movements. Step 2: The character generation unit converts the sign language actions recognized by the sign language recognition unit into character information. For example, the character generation unit analyzes the sign language actions and generates character information corresponding to the actions. The character generation unit can also analyze the sign language actions in real time and generate character information. For example, the character generation unit analyzes the sign language actions and generates character information corresponding to the actions in real time. Step 3: The voice generation unit converts the text information generated by the text generation unit into voice. For example, the voice generation unit analyzes the text information and generates voice corresponding to the text information. The voice generation unit can also generate natural voice using voice synthesis technology. For example, the voice generation unit analyzes the text information and generates voice with natural pronunciation using voice synthesis technology.

[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 sign language actions; a character generation unit that converts the sign language actions recognized by the sign language recognition unit into character information; a voice generating unit that converts the character information generated by the character generating unit into voice; A system characterized by:

2. The sign language recognition unit Analyzing not only the hand movements but also the facial expressions and body movements to generate more accurate text information 2. The system of claim 1.

3. The sign language recognition unit The system has a function to automatically adjust the background, lighting, and camera angle of the environment in which the sign language is spoken.

2. The system of claim 1.

4. The sign language recognition unit Analyzing the emotion of the person using the sign language and generating the character information according to the emotion 2. The system of claim 1.

5. The sign language recognition unit The sign language is displayed as subtitles in real time, and the function of adding sign language subtitles to video content is provided.

2. The system of claim 1.

Images