system

Abstract

The system according to this embodiment aims to enable smooth and real-time communication between people with hearing impairments and people with hearing. [Solution] The system according to the embodiment comprises a speech conversion unit, a sign language input unit, a sign language analysis unit, a conversion unit, a character provision unit, and a display unit. The speech conversion unit converts speech into text. The sign language input unit captures sign language movements with a camera. The sign language analysis unit analyzes the sign language movements captured by the sign language input unit. The conversion unit converts the information analyzed by the sign language analysis unit into speech or text. The character provision unit provides the character information converted by the speech conversion unit. The display unit displays the information converted by the conversion unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to achieve smooth and real-time communication between a person without hearing and a person with hearing.

[0005] The system according to the embodiment aims to enable a person without hearing and a person with hearing to communicate smoothly and in real time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a speech conversion unit, a sign language input unit, a sign language analysis unit, a conversion unit, a character provision unit, and a display unit. The speech conversion unit converts speech into text. The sign language input unit captures sign language movements with a camera. The sign language analysis unit analyzes the sign language movements captured by the sign language input unit. The conversion unit converts the information analyzed by the sign language analysis unit into speech or text. The character provision unit provides the character information converted by the speech conversion unit. The display unit displays the information converted by the conversion unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable smooth and real-time communication between people with hearing impairments and people with hearing. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The communication system according to an embodiment of the present invention is a system that enables smooth and real-time communication between deaf and hearing individuals. In this communication system, when a hearing person speaks, their voice is input to a generation AI, which instantly converts the voice into text and provides that textual information to the deaf person. Next, when the deaf person expresses themselves using sign language, their sign language movements are captured by a camera and input to the generation AI, which analyzes the sign language movements and converts them into voice or text for transmission to the hearing person. This mechanism not only allows people with different communication styles to effectively exchange information but also realizes barrier-free communication. For example, if a deaf person expresses "thank you" in sign language, the sign language is converted into voice or text by the generation AI and transmitted to the hearing person. Similarly, if a hearing person says "good morning," their voice is converted into text by the generation AI and displayed to the deaf person. This service not only enables people with different communication styles to effectively exchange information but also realizes barrier-free communication. As a result, the communication system enables smooth and real-time communication between deaf and hearing individuals.

[0029] The communication system according to this embodiment includes a voice conversion unit, a sign language input unit, a sign language analysis unit, a conversion unit, a character provision unit, and a display unit. The voice conversion unit converts speech into text. The voice conversion unit instantly converts speech into text using, for example, a generation AI. The generation AI analyzes speech using speech recognition technology and natural language processing technology and converts it into text. For example, the generation AI receives speech as input, analyzes the speech, and outputs character information. The sign language input unit captures sign language movements with a camera. The sign language input unit captures sign language movements using, for example, a camera. The camera takes high-resolution images of the sign language movements and inputs the images to the generation AI. The sign language analysis unit analyzes the sign language movements captured by the sign language input unit. The sign language analysis unit analyzes sign language movements using, for example, a generation AI. The generation AI analyzes sign language movements using image processing technology and machine learning algorithms and understands their meaning. The conversion unit converts the information analyzed by the sign language analysis unit into speech or text. The conversion unit converts sign language actions into speech or text using, for example, a generation AI. The generation AI converts sign language actions into speech or text using speech synthesis technology and text generation technology. The text provision unit provides the text information converted by the speech conversion unit. The text provision unit provides the text information converted using, for example, a generation AI. The generation AI transmits the text information to a display device and provides it to a person with hearing impairment. The display unit displays the information converted by the conversion unit. The display unit displays the information converted using, for example, a generation AI. The generation AI displays the information on the display and provides it to a person with hearing. As a result, the communication system according to the embodiment enables smooth and real-time communication between a person with hearing impairment and a person with hearing impairment.

[0030] The speech conversion unit converts speech into text. For example, it can instantly convert speech into text using a generative AI. The generative AI analyzes speech using speech recognition and natural language processing technologies and converts it into text. Specifically, the speech conversion unit collects speech using a high-precision microphone and transmits the speech data to the generative AI. The generative AI analyzes the speech data and extracts speech features. Speech recognition technology analyzes the speech waveform and identifies phonemes and words. Furthermore, natural language processing technology is used to analyze the identified words based on context and convert them into accurate strings. For example, the generative AI considers the intonation and accent of the speech and generates a string appropriate to the context. This enables the speech conversion unit to achieve real-time, high-precision text conversion, providing instant text information to people with hearing impairments. Furthermore, the speech conversion unit can support multiple languages ​​and appropriately convert speech from different languages ​​into text. This allows the speech conversion unit to support international communication and be used by a wide range of users.

[0031] The sign language input unit captures sign language movements with a camera. For example, the camera captures sign language movements in high resolution, and the video is input to the generation AI. Specifically, the sign language input unit uses multiple cameras to capture sign language movements from multiple angles. This allows for accurate capture of subtle movements and changes in facial expressions in sign language. The camera captures video at a high frame rate and transmits it to the generation AI in real time. The generation AI analyzes the received video data and recognizes sign language movements. Using image processing technology, it analyzes the position, shape, and movement patterns of the hands to understand the meaning of the sign language. Furthermore, the generation AI considers the context of the sign language and changes in facial expressions to perform a more accurate analysis. As a result, the sign language input unit can capture sign language movements with high accuracy and input them to the generation AI. Moreover, the sign language input unit can accommodate different sign language dialects and individual sign language quirks, making it usable by a wide range of users.

[0032] The sign language analysis unit analyzes sign language movements captured by the sign language input unit. For example, the sign language analysis unit uses generative AI to analyze sign language movements. The generative AI uses image processing technology and machine learning algorithms to analyze sign language movements and understand their meaning. Specifically, the sign language analysis unit uses generative AI to analyze sign language movements frame by frame, identifying hand position, shape, and movement patterns. Based on a pre-trained sign language database, the generative AI matches the sign language movements and identifies their meaning. Furthermore, the generative AI considers the context of the sign language and changes in facial expressions to perform a more accurate analysis. For example, if sign language movements are performed consecutively, the generative AI considers this continuity in its analysis and identifies a meaning appropriate to the context. This allows the sign language analysis unit to analyze sign language movements with high accuracy and understand their meaning. Moreover, the sign language analysis unit can handle different sign language dialects and individual sign language quirks, making it usable by a wide range of users.

[0033] The conversion unit converts the information analyzed by the sign language analysis unit into speech or text. For example, the conversion unit uses a generation AI to convert sign language movements into speech or text. The generation AI uses speech synthesis and text generation technologies to convert sign language movements into speech or text. Specifically, the conversion unit generates speech using speech synthesis technology based on the semantic information of the sign language received from the sign language analysis unit. The generation AI synthesizes speech appropriate to the meaning of the sign language and outputs the speech in real time. Furthermore, it uses text generation technology to convert the meaning of the sign language into text information and transmits it to a display device. This allows the conversion unit to instantly convert sign language movements into speech or text, providing information to hearing individuals. Additionally, the conversion unit can support multiple languages ​​and appropriately convert sign language in different languages ​​into speech or text. This enables the conversion unit to support international communication and be used by a wide range of users.

[0034] The text provider unit provides text information converted by the speech conversion unit. For example, the text provider unit provides text information converted using a generation AI. The generation AI transmits the text information to a display device, providing it to individuals with hearing impairments. Specifically, the text provider unit transmits the text information received from the speech conversion unit to the display device in real time. The display device uses a high-resolution display to clearly show the text information. Furthermore, the text provider unit adjusts the font size and color of the text information to achieve a visually easy-to-read display. This allows for the immediate provision of text information to individuals with hearing impairments. Additionally, the text provider unit is compatible with multiple display devices, providing text information to various devices such as smartphones, tablets, and personal computers. This allows the text provider unit to be used by a wide range of users.

[0035] The display unit displays the information converted by the conversion unit. For example, the display unit displays information converted using a generation AI. The generation AI displays the information on the display, providing it to hearing users. Specifically, the display unit displays the audio and text information received from the conversion unit on the display in real time. The display achieves high resolution and clear display, providing visually easy-to-understand information. Furthermore, the display unit can display audio information as subtitles, providing visual information to hearing users as well. This allows the display unit to accurately convey the meaning of sign language to hearing users. Additionally, the display unit supports multiple display modes, allowing users to select the display method according to their preferences. For example, it can offer various display methods, such as a mode that displays text information in a larger font or a mode that emphasizes audio information. This enables the display unit to provide visually easy-to-understand information to a wide range of users.

[0036] The speech conversion unit can instantly convert speech into text using a generative AI. For example, the speech conversion unit instantly converts speech into text using a generative AI. The generative AI analyzes speech using speech recognition and natural language processing technologies and converts it into text. For example, the generative AI receives speech as input, analyzes the speech, and outputs text information. This enables real-time communication by instantly converting speech into text. The generative AI converts speech into text using, for example, a speech recognition model. The speech recognition model has learned from a large amount of speech data, enabling highly accurate speech recognition. The generative AI extracts features from speech and converts them into text based on those features. For example, the generative AI analyzes the waveform data of speech, identifies phonemes and words, and converts them into text. The generative AI understands the context of speech and generates appropriate strings of characters. For example, the generative AI considers the context before and after the speech to generate accurate strings of characters. This allows the generative AI to instantly convert speech into text.

[0037] The sign language input unit can capture sign language movements using a camera. For example, the camera captures sign language movements. The camera records the sign language movements in high resolution and inputs the video into the generating AI. The generating AI analyzes the sign language movements and understands their meaning. For example, the camera records sign language movements in real time and transmits the video to the generating AI. The generating AI analyzes the video data and identifies the sign language movements. It is important for the camera to record in high resolution to accurately capture sign language movements. For example, the camera records in high resolution to capture the fine details of sign language movements and facial expressions. It is also important for the camera to set appropriate shooting angles and frame rates to capture sign language movements. This allows the camera to accurately capture sign language movements. The generating AI analyzes the video data captured by the camera and identifies the sign language movements. The generating AI uses image processing techniques and machine learning algorithms to analyze the sign language movements. For example, generative AI uses image processing techniques to analyze hand shapes and movements in order to identify sign language actions. Generative AI also learns sign language patterns using machine learning algorithms to identify sign language actions. This allows generative AI to accurately analyze sign language actions.

[0038] The sign language analysis unit can analyze sign language movements using generative AI. For example, the sign language analysis unit uses generative AI to analyze sign language movements. The generative AI analyzes sign language movements using image processing technology and machine learning algorithms to understand their meaning. For example, to analyze sign language movements, the generative AI uses image processing technology to analyze the shape and movement of the hands. To analyze sign language movements, the generative AI learns sign language patterns using machine learning algorithms. The generative AI has learned a large amount of sign language data to analyze sign language movements, enabling highly accurate sign language analysis. For example, to analyze sign language movements, the generative AI receives sign language video data as input, analyzes that video data, and understands the meaning of the sign language. To analyze sign language movements, the generative AI understands the context of the sign language and outputs appropriate analysis results. For example, the generative AI considers the movements before and after the sign language to output accurate analysis results. This allows the generative AI to accurately analyze sign language movements.

[0039] The conversion unit can convert sign language movements into speech or text using a generative AI. For example, the conversion unit uses a generative AI to convert sign language movements into speech or text. The generative AI uses speech synthesis and text generation technologies to convert sign language movements into speech or text. For example, to convert sign language movements into speech, the generative AI analyzes the sign language movements using speech synthesis technology and generates speech based on the analysis results. To convert sign language movements into text, the generative AI analyzes the sign language movements using text generation technology and generates text based on the analysis results. The generative AI has learned from a large amount of sign language data, enabling highly accurate conversion. For example, to convert sign language movements into speech or text, the generative AI receives sign language video data as input, analyzes the video data, and generates speech or text. The generative AI understands the context of sign language and outputs appropriate conversion results. For example, the generative AI considers the movements before and after the sign language to output accurate conversion results. This allows the generating AI to accurately convert sign language gestures into speech or text.

[0040] The character provision unit can provide character information converted by the generation AI. For example, the character provision unit provides character information converted using the generation AI. The generation AI transmits the character information to a display device and provides it to a person with hearing impairment. For example, the generation AI displays the converted character information on a display and provides it to a person with hearing impairment. The generation AI selects an appropriate display format and font size to provide the character information. For example, the generation AI selects an appropriate font size and display format to display the character information in an easy-to-read manner. The generation AI considers the type and resolution of the display device to provide the character information. For example, the generation AI displays the character information in the optimal format according to the resolution and size of the display. This allows the generation AI to accurately provide the converted character information.

[0041] The display unit can display information converted by the generating AI. For example, the display unit displays information converted using the generating AI. The generating AI displays information on the display and provides it to hearing individuals. For example, the generating AI displays converted information on the display and provides it to hearing individuals. The generating AI selects an appropriate display format and layout to display the information. For example, the generating AI selects an appropriate font size and display format to display the information clearly. The generating AI considers the type and resolution of the display to display the information. For example, the generating AI displays the information in the optimal format according to the resolution and size of the display. This allows the generating AI to accurately display the converted information.

[0042] The speech conversion unit can improve conversion accuracy by considering the speaker's accent and dialect during speech conversion. For example, the speech conversion unit uses a generation AI to convert speech to text while considering the speaker's accent and dialect. The generation AI identifies the speaker's accent and dialect using a speech database and speech feature extraction technology. For example, if the speaker uses Kansai dialect, the generation AI captures its features and converts them accurately to text. If the speaker has a foreign accent, the generation AI considers that accent when converting to text. If the speaker uses a local dialect, the generation AI refers to a dialect dictionary and converts it accurately to text. This improves the accuracy of speech conversion by considering the speaker's accent and dialect. The generation AI extracts speech features and converts them to text based on those features. For example, the generation AI analyzes the waveform data of the speech, identifies phonemes and words, and converts them to text. The generation AI understands the context of the speech and generates an appropriate string of characters. For example, the generation AI considers the context before and after the speech to generate an accurate string of characters. This allows the generation AI to convert speech to text while considering the speaker's accent and dialect.

[0043] The speech conversion unit can improve conversion accuracy by removing background noise during speech conversion. For example, the speech conversion unit uses a generation AI to remove background noise and convert speech to text. The generation AI removes background noise using noise cancellation and filtering technologies. For example, if the speaker is speaking in a noisy environment, the generation AI filters out background noise and converts speech to text. If the speaker is speaking in a place with strong wind noise, the generation AI removes wind noise and converts speech to text. If the speaker is speaking in a place with music playing, the generation AI removes the music and converts speech to text. This improves the accuracy of speech conversion by removing background noise. The generation AI extracts features from the speech and converts them to text based on those features. For example, the generation AI analyzes the waveform data of the speech, identifies phonemes and words, and converts them to text. The generation AI understands the context of the speech and generates appropriate strings of characters. For example, the generation AI considers the context before and after the speech to generate accurate strings of characters. This allows the generation AI to remove background noise and convert speech to text.

[0044] The speech conversion unit can improve conversion accuracy by considering the speaker's age and gender during speech conversion. For example, the speech conversion unit uses a generation AI to convert speech to text while considering the speaker's age and gender. The generation AI identifies the speaker's age and gender using speech feature extraction technology and database referencing technology. For example, if the speaker is a child, the generation AI captures their characteristics and converts them accurately to text. If the speaker is an elderly person, the generation AI captures their characteristics and converts them accurately to text. If the speaker is female, the generation AI captures her characteristics and converts them accurately to text. This improves the accuracy of speech conversion by considering the speaker's age and gender. The generation AI extracts speech features and converts them to text based on those features. For example, the generation AI analyzes the waveform data of the speech, identifies phonemes and words, and converts them to text. The generation AI understands the context of the speech and generates an appropriate string of characters. For example, the generation AI considers the context before and after the speech to generate an accurate string of characters. This allows the generation AI to convert speech to text while considering the speaker's age and gender.

[0045] The speech conversion unit can adjust the conversion speed according to the speaker's speaking speed during speech conversion. For example, the speech conversion unit uses a generation AI to convert speech to text according to the speaker's speaking speed. The generation AI identifies the speaker's speaking speed using real-time processing technology and speed detection algorithms. For example, if the speaker is speaking quickly, the generation AI converts speech to text at a high speed according to the speaker's speed. If the speaker is speaking slowly, the generation AI converts speech to text slowly according to the speaker's speed. If the speaker is speaking intermittently, the generation AI converts speech to text at the appropriate timing. By adjusting the conversion speed according to the speaker's speaking speed, the accuracy of speech conversion is improved. The generation AI extracts features of the speech and converts them to text based on those features. For example, the generation AI analyzes the waveform data of the speech, identifies phonemes and words, and converts them to text. The generation AI understands the context of the speech and generates appropriate strings of characters. For example, the generation AI considers the context before and after the speech to generate accurate strings of characters. As a result, the generation AI can convert speech to text according to the speaker's speaking speed.

[0046] The sign language input unit can improve input accuracy by taking into account the speed of sign language movements during input. For example, the sign language input unit uses a generation AI to input sign language while considering the speed of the movements. The generation AI identifies the speed of sign language movements using motion detection algorithms and speed analysis techniques. For example, if the user performs sign language quickly, the generation AI adjusts the input accuracy according to that speed. If the user performs sign language slowly, the generation AI adjusts the input accuracy according to that speed. If the user performs sign language intermittently, the generation AI adjusts the input accuracy according to that speed. This improves the accuracy of sign language input by considering the speed of the movements. The generation AI analyzes the movements of the sign language and adjusts the input accuracy based on the speed of those movements. For example, the generation AI detects the speed of the sign language movements in real time and dynamically adjusts the input accuracy according to that speed. The generation AI sets appropriate input parameters considering the speed of the sign language movements. For example, the generation AI adjusts the sensor sensitivity and analysis algorithm parameters according to the speed of the sign language movements. This allows the generation AI to accurately input sign language while considering the speed of the movements.

[0047] The sign language input unit can improve input accuracy by considering the range of movement of sign language during input. For example, the sign language input unit inputs sign language while considering the range of movement of sign language using a generation AI. The generation AI identifies the range of movement of sign language using a movement range detection algorithm and range analysis technology. For example, if the user performs sign language with a wide range of movement, the generation AI adjusts the input accuracy according to that range. If the user performs sign language with a narrow range of movement, the generation AI adjusts the input accuracy according to that range. If the user performs sign language with an irregular range of movement, the generation AI adjusts the input accuracy according to that range. In this way, the accuracy of sign language input is improved by considering the range of movement of sign language. The generation AI analyzes the movement of sign language and adjusts the input accuracy based on that range of movement. For example, the generation AI detects the range of movement of sign language in real time and dynamically adjusts the input accuracy according to that range. The generation AI sets appropriate input parameters considering the range of movement of sign language. For example, the generation AI adjusts the sensor sensitivity and analysis algorithm parameters according to the range of movement of sign language. This allows the generating AI to accurately input sign language, taking into account the range of movement in sign language.

[0048] The sign language analysis unit can improve the accuracy of its analysis by considering the context of the sign language during analysis. For example, the sign language analysis unit analyzes sign language while considering its context using a generative AI. The generative AI identifies the context of sign language using a contextual analysis algorithm and a contextual database. For example, the generative AI improves the accuracy of its analysis based on the context by considering the actions before and after the sign language. The generative AI improves accuracy by analyzing the context in order to understand the meaning of the sign language. The generative AI improves accuracy by considering the context in order to analyze the sequence of sign language actions. As a result, the accuracy of sign language analysis is improved by considering the context of the sign language. The generative AI analyzes the actions of sign language and adjusts the analysis accuracy based on their context. For example, the generative AI detects the context of the sign language in real time and dynamically adjusts the analysis accuracy according to that context. The generative AI sets appropriate analysis parameters considering the context of the sign language. For example, the generative AI adjusts the parameters of the analysis algorithm according to the context of the sign language. As a result, the generative AI can accurately analyze sign language while considering its context.

[0049] The sign language analysis unit can improve the accuracy of sign language analysis by detecting subtle differences in sign language movements. For example, the sign language analysis unit uses a generative AI to detect subtle differences in sign language movements and analyze the sign language. The generative AI identifies subtle differences in sign language movements using high-precision sensors and motion analysis algorithms. For example, the generative AI improves analysis accuracy by detecting subtle differences in finger movements in sign language. The generative AI improves analysis accuracy by detecting subtle differences in the orientation of the palm in sign language. The generative AI improves analysis accuracy by detecting subtle differences in the position of the hand in sign language. In this way, the accuracy of sign language analysis is improved by detecting subtle differences in sign language movements. The generative AI analyzes sign language movements and adjusts the analysis accuracy based on these subtle differences. For example, the generative AI detects subtle differences in sign language movements in real time and dynamically adjusts the analysis accuracy according to these differences. The generative AI sets appropriate analysis parameters considering the subtle differences in sign language movements. For example, the generative AI adjusts the parameters of its analysis algorithm in response to subtle differences in sign language movements. This allows the generative AI to detect these subtle differences in sign language movements and accurately analyze the sign language.

[0050] The sign language analysis unit can improve the accuracy of its analysis by considering regional differences in sign language. For example, the sign language analysis unit analyzes sign language while considering regional differences using a generative AI. The generative AI identifies regional differences in sign language using a regional database and region-specific motion analysis technology. For example, the generative AI improves the accuracy of its analysis by considering the differences in sign language from region to region. The generative AI improves accuracy by analyzing region-specific sign language movements. The generative AI improves the accuracy of its analysis by considering the differences in the meaning of sign language from region to region. As a result, the accuracy of sign language analysis is improved by considering regional differences in sign language. The generative AI analyzes sign language movements and adjusts the analysis accuracy based on those regional differences. For example, the generative AI detects regional differences in sign language in real time and dynamically adjusts the analysis accuracy according to those differences. The generative AI sets appropriate analysis parameters considering regional differences in sign language. For example, the generative AI adjusts the parameters of the analysis algorithm according to the regional differences in sign language. As a result, the generative AI can accurately analyze sign language while considering regional differences in sign language.

[0051] The sign language analysis unit can improve the accuracy of its analysis by considering the speed of sign language movements during the analysis. For example, the sign language analysis unit analyzes sign language while considering the speed of sign language movements using a generative AI. The generative AI identifies the speed of sign language movements using a speed detection algorithm and motion analysis technology. For example, when sign language is performed quickly, the generative AI adjusts the analysis accuracy according to the speed. When sign language is performed slowly, the generative AI adjusts the analysis accuracy according to the speed. When sign language is performed intermittently, the generative AI adjusts the analysis accuracy according to the speed. This improves the accuracy of sign language analysis by considering the speed of sign language movements. The generative AI analyzes sign language movements and adjusts the analysis accuracy based on their speed. For example, the generative AI detects the speed of sign language movements in real time and dynamically adjusts the analysis accuracy according to that speed. The generative AI sets appropriate analysis parameters considering the speed of sign language movements. For example, the generative AI adjusts the parameters of the analysis algorithm according to the speed of sign language movements. This allows the generative AI to accurately analyze sign language while considering the speed of sign language movements.

[0052] The conversion unit can improve conversion accuracy by considering the meaning of sign language movements during conversion. For example, the conversion unit converts sign language by considering the meaning of sign language movements using a generative AI. The generative AI identifies the meaning of sign language movements using a semantic analysis algorithm and a movement database. For example, the generative AI analyzes the meaning to understand the meaning of sign language movements and improves accuracy. The generative AI analyzes the meaning to improve accuracy by considering the context of sign language movements. The generative AI analyzes the meaning to improve accuracy by considering the continuity of sign language movements and improves accuracy. As a result, conversion accuracy is improved by considering the meaning of sign language movements. The generative AI analyzes sign language movements and adjusts the conversion accuracy based on their meaning. For example, the generative AI detects the meaning of sign language movements in real time and dynamically adjusts the conversion accuracy according to that meaning. The generative AI sets appropriate conversion parameters considering the meaning of sign language movements. For example, the generative AI adjusts the parameters of the conversion algorithm according to the meaning of the sign language movements. As a result, the generative AI can accurately convert sign language by considering the meaning of sign language movements.

[0053] The conversion unit can improve conversion accuracy by considering the continuity of sign language movements during conversion. For example, the conversion unit uses a generation AI to convert sign language while considering the continuity of sign language movements. The generation AI identifies the continuity of sign language movements using a continuous motion analysis algorithm or motion sequence analysis technology. For example, if the sign language movements are continuous, the generation AI improves conversion accuracy by considering that continuity. If the sign language movements are discontinuous, the generation AI improves conversion accuracy by considering that discontinuity. If the sign language movements are complex, the generation AI improves conversion accuracy by considering that complexity. In this way, conversion accuracy is improved by considering the continuity of sign language movements. The generation AI analyzes the sign language movements and adjusts the conversion accuracy based on their continuity. For example, the generation AI detects the continuity of sign language movements in real time and dynamically adjusts the conversion accuracy according to that continuity. The generation AI sets appropriate conversion parameters considering the continuity of sign language movements. For example, the generation AI adjusts the parameters of the conversion algorithm according to the continuity of sign language movements. This allows the generating AI to accurately translate sign language by taking into account the continuity of the sign language movements.

[0054] The conversion unit can improve conversion accuracy by considering the frequency of sign language movements during conversion. For example, the conversion unit converts sign language by considering the frequency of sign language movements using a generation AI. The generation AI identifies the frequency of sign language movements using a frequency analysis algorithm and a movement database. For example, the generation AI improves conversion accuracy by prioritizing the analysis of frequently used sign language movements. The generation AI improves analysis accuracy by identifying rarely used sign language movements. The generation AI adjusts the conversion accuracy based on the frequency of sign language movements. This improves conversion accuracy by considering the frequency of sign language movements. The generation AI analyzes sign language movements and adjusts the conversion accuracy based on their frequency. For example, the generation AI detects the frequency of sign language movements in real time and dynamically adjusts the conversion accuracy according to that frequency. The generation AI sets appropriate conversion parameters considering the frequency of sign language movements. For example, the generation AI adjusts the parameters of the conversion algorithm according to the frequency of sign language movements. This allows the generation AI to accurately convert sign language by considering the frequency of sign language movements.

[0055] The conversion unit can improve conversion accuracy by considering the intensity of sign language movements during conversion. For example, the conversion unit converts sign language by considering the intensity of sign language movements using a generation AI. The generation AI identifies the intensity of sign language movements using an intensity analysis algorithm and a movement database. For example, if a sign language movement is strong, the generation AI improves conversion accuracy by considering its strength. If a sign language movement is weak, the generation AI improves conversion accuracy by considering its weakness. The generation AI adjusts the conversion accuracy based on the intensity of the sign language movements. As a result, conversion accuracy is improved by considering the intensity of sign language movements. The generation AI analyzes sign language movements and adjusts the conversion accuracy based on their intensity. For example, the generation AI detects the intensity of sign language movements in real time and dynamically adjusts the conversion accuracy according to that intensity. The generation AI sets appropriate conversion parameters considering the intensity of sign language movements. For example, the generation AI adjusts the parameters of the conversion algorithm according to the intensity of the sign language movements. As a result, the generation AI can accurately convert sign language by considering the intensity of sign language movements.

[0056] The text provider can customize the display method based on the user's visual preferences when providing text. For example, the text provider analyzes the user's visual preferences using a generative AI and customizes the display method based on those preferences. The generative AI identifies the user's visual preferences using a customization algorithm and user profile. For example, the generative AI analyzes the user's preferred colors and displays the text in those colors. The generative AI analyzes the user's preferred font style and displays the text in that font. The generative AI analyzes the user's preferred font size and displays the text in that size. This allows for more appropriate display by customizing the display method based on the user's visual preferences. The generative AI detects the user's visual preferences in real time and dynamically adjusts the display method according to those preferences. The generative AI sets appropriate display parameters based on the user's visual preferences. For example, the generative AI adjusts the parameters of the display algorithm according to the user's visual preferences. This allows the generative AI to accurately display text based on the user's visual preferences.

[0057] The character provisioning unit can select the optimal display method when providing characters, taking into account the user's device information. For example, the character provisioning unit analyzes the user's device information using a generation AI and selects the optimal display method based on that information. The generation AI identifies the user's device information using a device information analysis algorithm and a device profile. For example, if the user is using a smartphone, the generation AI provides a display method that matches the screen size. If the user is using a tablet, the generation AI provides a display method optimized for a large screen. If the user is using a smartwatch, the generation AI provides a concise and highly visible display method. In this way, the optimal display method can be provided by taking the user's device information into consideration. The generation AI detects the user's device information in real time and dynamically adjusts the display method according to that information. The generation AI sets appropriate display parameters based on the user's device information. For example, the generation AI adjusts the parameters of the display algorithm according to the user's device information. In this way, the generation AI can accurately display characters based on the user's device information.

[0058] The display unit can select the optimal display method by referring to the user's past display history when displaying content. For example, the display unit uses a generation AI to analyze the user's past display history and selects the optimal display method based on that history. The generation AI identifies the user's past display history using a history analysis algorithm and a history database. For example, the generation AI refers to and provides display methods that the user has preferred to use in the past. The generation AI refers to and provides fonts and colors that the user has used in the past. The generation AI refers to and provides layouts that the user has used in the past. In this way, the optimal display method can be provided by referring to the user's past display history. The generation AI detects the user's past display history in real time and dynamically adjusts the display method according to that history. The generation AI sets appropriate display parameters based on the user's past display history. For example, the generation AI adjusts the parameters of the display algorithm according to the user's past display history. In this way, the generation AI can accurately display characters based on the user's past display history.

[0059] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, the display unit analyzes the user's device information using a generation AI and selects the optimal display method based on that information. The generation AI identifies the user's device information using a device information analysis algorithm and a device profile. For example, if the user is using a smartphone, the generation AI provides a display method that matches the screen size. If the user is using a tablet, the generation AI provides a display method optimized for a large screen. If the user is using a smartwatch, the generation AI provides a concise and highly visible display method. In this way, the optimal display method can be provided by taking into account the user's device information. The generation AI detects the user's device information in real time and dynamically adjusts the display method according to that information. The generation AI sets appropriate display parameters based on the user's device information. For example, the generation AI adjusts the parameters of the display algorithm according to the user's device information. In this way, the generation AI can accurately display characters based on the user's device information.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The communication system can also be equipped with a translation unit. This unit uses generative AI to translate between different languages. For example, if a deaf person expresses something in sign language, the generative AI can convert the sign language into text, and the translation unit can then translate that text into another language. This allows users who speak different languages ​​to communicate smoothly. The translation unit can support translation between multiple languages, such as English to Japanese or French to Spanish. Furthermore, the translation unit can automatically select the translation language based on the user's language settings. This enables real-time communication between users who speak different languages.

[0062] The communication system may also include a context analysis unit. This unit uses generative AI to analyze the context of the communication and generate an appropriate response based on that context. For example, if a deaf person asks a question using sign language, the generative AI analyzes the context of the question, and the context analysis unit generates an appropriate response. This enables more natural communication. The context analysis unit can generate responses by considering, for example, past conversation history or the current flow of conversation. Furthermore, the context analysis unit can collect and analyze data from multiple sources to accurately understand the user's intent. This allows for an accurate understanding of the user's intent and the provision of appropriate responses.

[0063] The communication system can also include a user profile management unit. This unit uses generative AI to manage each user's profile and provides customized services based on that profile. For example, it can store the sign language style and preferences of deaf individuals in their profile, and the generative AI can use this information to improve the accuracy of sign language analysis. This enables communication optimized for each user. The user profile management unit can also manage, for example, a user's language settings, preferred font size, and display format. Furthermore, the user profile management unit can provide personalized services based on a user's past communication history. This allows users to communicate more comfortably.

[0064] The communication system can also be equipped with a real-time translation unit. This unit uses generative AI to perform real-time translation between different languages. For example, if a deaf person expresses something in sign language, the generative AI converts the sign language into text, and the real-time translation unit translates that text into a different language. This allows users who speak different languages ​​to communicate smoothly. The real-time translation unit can support translation between multiple languages, such as English to Japanese or French to Spanish. Furthermore, the real-time translation unit can automatically select the translation language based on the user's language settings. This enables real-time communication between users who speak different languages.

[0065] The communication system can also include a learning support unit to assist the user's learning. This unit uses generative AI to analyze the user's learning progress and provide appropriate learning support based on that analysis. For example, when a deaf person expresses themselves using sign language, the generative AI analyzes their learning progress from the sign language movements, and the learning support unit provides appropriate learning support based on that information. This allows the user to learn effectively. The learning support unit can also monitor the user's learning progress and provide appropriate learning advice based on that information. Furthermore, the learning support unit can provide a customized learning plan based on the user's learning style. This allows the user to learn using the method best suited to them.

[0066] The communication system can also include a privacy protection unit to further safeguard user privacy. This unit uses generation AI to protect user privacy information and prevent unauthorized access. For example, if a deaf person expresses something in sign language, the generation AI can convert the sign language into text, and the privacy protection unit can then encrypt and protect that text information. This ensures user privacy. The privacy protection unit can, for example, encrypt user personal information and communication content to protect it from unauthorized access. Furthermore, the privacy protection unit can restrict the scope of information sharing based on the user's privacy settings. This allows users to communicate with peace of mind.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The speech conversion unit converts speech into text. For example, it uses a generation AI to instantly convert speech into text. The generation AI analyzes speech using speech recognition technology and natural language processing technology and outputs text information. Step 2: The sign language input unit captures sign language movements with a camera. For example, a high-resolution camera is used to film sign language movements, and the video is input into the generation AI. Step 3: The sign language analysis unit analyzes the sign language movements captured by the sign language input unit. For example, it analyzes the sign language movements using generative AI and understands their meaning using image processing technology and machine learning algorithms. Step 4: The conversion unit converts the information analyzed by the sign language analysis unit into speech or text. For example, it uses generation AI to convert sign language actions into speech or text using speech synthesis technology or text generation technology. Step 5: The text provision unit provides the text information converted by the speech conversion unit. For example, it sends the text information converted using generation AI to a display device and provides it to people with hearing impairments. Step 6: The display unit displays the information converted by the conversion unit. For example, the information converted using the generation AI is displayed on the screen and provided to people with hearing.

[0069] (Example of form 2) The communication system according to an embodiment of the present invention is a system that enables smooth and real-time communication between deaf and hearing individuals. In this communication system, when a hearing person speaks, their voice is input to a generation AI, which instantly converts the voice into text and provides that textual information to the deaf person. Next, when the deaf person expresses themselves using sign language, their sign language movements are captured by a camera and input to the generation AI, which analyzes the sign language movements and converts them into voice or text for transmission to the hearing person. This mechanism not only allows people with different communication styles to effectively exchange information but also realizes barrier-free communication. For example, if a deaf person expresses "thank you" in sign language, the sign language is converted into voice or text by the generation AI and transmitted to the hearing person. Similarly, if a hearing person says "good morning," their voice is converted into text by the generation AI and displayed to the deaf person. This service not only enables people with different communication styles to effectively exchange information but also realizes barrier-free communication. As a result, the communication system enables smooth and real-time communication between deaf and hearing individuals.

[0070] The communication system according to this embodiment includes a voice conversion unit, a sign language input unit, a sign language analysis unit, a conversion unit, a character provision unit, and a display unit. The voice conversion unit converts speech into text. The voice conversion unit instantly converts speech into text using, for example, a generation AI. The generation AI analyzes speech using speech recognition technology and natural language processing technology and converts it into text. For example, the generation AI receives speech as input, analyzes the speech, and outputs character information. The sign language input unit captures sign language movements with a camera. The sign language input unit captures sign language movements using, for example, a camera. The camera takes high-resolution images of the sign language movements and inputs the images to the generation AI. The sign language analysis unit analyzes the sign language movements captured by the sign language input unit. The sign language analysis unit analyzes sign language movements using, for example, a generation AI. The generation AI analyzes sign language movements using image processing technology and machine learning algorithms and understands their meaning. The conversion unit converts the information analyzed by the sign language analysis unit into speech or text. The conversion unit converts sign language actions into speech or text using, for example, a generation AI. The generation AI converts sign language actions into speech or text using speech synthesis technology and text generation technology. The text provision unit provides the text information converted by the speech conversion unit. The text provision unit provides the text information converted using, for example, a generation AI. The generation AI transmits the text information to a display device and provides it to a person with hearing impairment. The display unit displays the information converted by the conversion unit. The display unit displays the information converted using, for example, a generation AI. The generation AI displays the information on the display and provides it to a person with hearing. As a result, the communication system according to the embodiment enables smooth and real-time communication between a person with hearing impairment and a person with hearing impairment.

[0071] The speech conversion unit converts speech into text. For example, it can instantly convert speech into text using a generative AI. The generative AI analyzes speech using speech recognition and natural language processing technologies and converts it into text. Specifically, the speech conversion unit collects speech using a high-precision microphone and transmits the speech data to the generative AI. The generative AI analyzes the speech data and extracts speech features. Speech recognition technology analyzes the speech waveform and identifies phonemes and words. Furthermore, natural language processing technology is used to analyze the identified words based on context and convert them into accurate strings. For example, the generative AI considers the intonation and accent of the speech and generates a string appropriate to the context. This enables the speech conversion unit to achieve real-time, high-precision text conversion, providing instant text information to people with hearing impairments. Furthermore, the speech conversion unit can support multiple languages ​​and appropriately convert speech from different languages ​​into text. This allows the speech conversion unit to support international communication and be used by a wide range of users.

[0072] The sign language input unit captures sign language movements with a camera. For example, the camera captures sign language movements in high resolution, and the video is input to the generation AI. Specifically, the sign language input unit uses multiple cameras to capture sign language movements from multiple angles. This allows for accurate capture of subtle movements and changes in facial expressions in sign language. The camera captures video at a high frame rate and transmits it to the generation AI in real time. The generation AI analyzes the received video data and recognizes sign language movements. Using image processing technology, it analyzes the position, shape, and movement patterns of the hands to understand the meaning of the sign language. Furthermore, the generation AI considers the context of the sign language and changes in facial expressions to perform a more accurate analysis. As a result, the sign language input unit can capture sign language movements with high accuracy and input them to the generation AI. Moreover, the sign language input unit can accommodate different sign language dialects and individual sign language quirks, making it usable by a wide range of users.

[0073] The sign language analysis unit analyzes sign language movements captured by the sign language input unit. For example, the sign language analysis unit uses generative AI to analyze sign language movements. The generative AI uses image processing technology and machine learning algorithms to analyze sign language movements and understand their meaning. Specifically, the sign language analysis unit uses generative AI to analyze sign language movements frame by frame, identifying hand position, shape, and movement patterns. Based on a pre-trained sign language database, the generative AI matches the sign language movements and identifies their meaning. Furthermore, the generative AI considers the context of the sign language and changes in facial expressions to perform a more accurate analysis. For example, if sign language movements are performed consecutively, the generative AI considers this continuity in its analysis and identifies a meaning appropriate to the context. This allows the sign language analysis unit to analyze sign language movements with high accuracy and understand their meaning. Moreover, the sign language analysis unit can handle different sign language dialects and individual sign language quirks, making it usable by a wide range of users.

[0074] The conversion unit converts the information analyzed by the sign language analysis unit into speech or text. For example, the conversion unit uses a generation AI to convert sign language movements into speech or text. The generation AI uses speech synthesis and text generation technologies to convert sign language movements into speech or text. Specifically, the conversion unit generates speech using speech synthesis technology based on the semantic information of the sign language received from the sign language analysis unit. The generation AI synthesizes speech appropriate to the meaning of the sign language and outputs the speech in real time. Furthermore, it uses text generation technology to convert the meaning of the sign language into text information and transmits it to a display device. This allows the conversion unit to instantly convert sign language movements into speech or text, providing information to hearing individuals. Additionally, the conversion unit can support multiple languages ​​and appropriately convert sign language in different languages ​​into speech or text. This enables the conversion unit to support international communication and be used by a wide range of users.

[0075] The text provider unit provides text information converted by the speech conversion unit. For example, the text provider unit provides text information converted using a generation AI. The generation AI transmits the text information to a display device, providing it to individuals with hearing impairments. Specifically, the text provider unit transmits the text information received from the speech conversion unit to the display device in real time. The display device uses a high-resolution display to clearly show the text information. Furthermore, the text provider unit adjusts the font size and color of the text information to achieve a visually easy-to-read display. This allows for the immediate provision of text information to individuals with hearing impairments. Additionally, the text provider unit is compatible with multiple display devices, providing text information to various devices such as smartphones, tablets, and personal computers. This allows the text provider unit to be used by a wide range of users.

[0076] The display unit displays the information converted by the conversion unit. For example, the display unit displays information converted using a generation AI. The generation AI displays the information on the display, providing it to hearing users. Specifically, the display unit displays the audio and text information received from the conversion unit on the display in real time. The display achieves high resolution and clear display, providing visually easy-to-understand information. Furthermore, the display unit can display audio information as subtitles, providing visual information to hearing users as well. This allows the display unit to accurately convey the meaning of sign language to hearing users. Additionally, the display unit supports multiple display modes, allowing users to select the display method according to their preferences. For example, it can offer various display methods, such as a mode that displays text information in a larger font or a mode that emphasizes audio information. This enables the display unit to provide visually easy-to-understand information to a wide range of users.

[0077] The speech conversion unit can instantly convert speech into text using a generative AI. For example, the speech conversion unit instantly converts speech into text using a generative AI. The generative AI analyzes speech using speech recognition and natural language processing technologies and converts it into text. For example, the generative AI receives speech as input, analyzes the speech, and outputs text information. This enables real-time communication by instantly converting speech into text. The generative AI converts speech into text using, for example, a speech recognition model. The speech recognition model has learned from a large amount of speech data, enabling highly accurate speech recognition. The generative AI extracts features from speech and converts them into text based on those features. For example, the generative AI analyzes the waveform data of speech, identifies phonemes and words, and converts them into text. The generative AI understands the context of speech and generates appropriate strings of characters. For example, the generative AI considers the context before and after the speech to generate accurate strings of characters. This allows the generative AI to instantly convert speech into text.

[0078] The sign language input unit can capture sign language movements using a camera. For example, the camera captures sign language movements. The camera records the sign language movements in high resolution and inputs the video into the generating AI. The generating AI analyzes the sign language movements and understands their meaning. For example, the camera records sign language movements in real time and transmits the video to the generating AI. The generating AI analyzes the video data and identifies the sign language movements. It is important for the camera to record in high resolution to accurately capture sign language movements. For example, the camera records in high resolution to capture the fine details of sign language movements and facial expressions. It is also important for the camera to set appropriate shooting angles and frame rates to capture sign language movements. This allows the camera to accurately capture sign language movements. The generating AI analyzes the video data captured by the camera and identifies the sign language movements. The generating AI uses image processing techniques and machine learning algorithms to analyze the sign language movements. For example, generative AI uses image processing techniques to analyze hand shapes and movements in order to identify sign language actions. Generative AI also learns sign language patterns using machine learning algorithms to identify sign language actions. This allows generative AI to accurately analyze sign language actions.

[0079] The sign language analysis unit can analyze sign language movements using generative AI. For example, the sign language analysis unit uses generative AI to analyze sign language movements. The generative AI analyzes sign language movements using image processing technology and machine learning algorithms to understand their meaning. For example, to analyze sign language movements, the generative AI uses image processing technology to analyze the shape and movement of the hands. To analyze sign language movements, the generative AI learns sign language patterns using machine learning algorithms. The generative AI has learned a large amount of sign language data to analyze sign language movements, enabling highly accurate sign language analysis. For example, to analyze sign language movements, the generative AI receives sign language video data as input, analyzes that video data, and understands the meaning of the sign language. To analyze sign language movements, the generative AI understands the context of the sign language and outputs appropriate analysis results. For example, the generative AI considers the movements before and after the sign language to output accurate analysis results. This allows the generative AI to accurately analyze sign language movements.

[0080] The conversion unit can convert sign language movements into speech or text using a generative AI. For example, the conversion unit uses a generative AI to convert sign language movements into speech or text. The generative AI uses speech synthesis and text generation technologies to convert sign language movements into speech or text. For example, to convert sign language movements into speech, the generative AI analyzes the sign language movements using speech synthesis technology and generates speech based on the analysis results. To convert sign language movements into text, the generative AI analyzes the sign language movements using text generation technology and generates text based on the analysis results. The generative AI has learned from a large amount of sign language data, enabling highly accurate conversion. For example, to convert sign language movements into speech or text, the generative AI receives sign language video data as input, analyzes the video data, and generates speech or text. The generative AI understands the context of sign language and outputs appropriate conversion results. For example, the generative AI considers the movements before and after the sign language to output accurate conversion results. This allows the generating AI to accurately convert sign language gestures into speech or text.

[0081] The character provision unit can provide character information converted by the generation AI. For example, the character provision unit provides character information converted using the generation AI. The generation AI transmits the character information to a display device and provides it to a person with hearing impairment. For example, the generation AI displays the converted character information on a display and provides it to a person with hearing impairment. The generation AI selects an appropriate display format and font size to provide the character information. For example, the generation AI selects an appropriate font size and display format to display the character information in an easy-to-read manner. The generation AI considers the type and resolution of the display device to provide the character information. For example, the generation AI displays the character information in the optimal format according to the resolution and size of the display. This allows the generation AI to accurately provide the converted character information.

[0082] The display unit can display information converted by the generating AI. For example, the display unit displays information converted using the generating AI. The generating AI displays information on the display and provides it to hearing individuals. For example, the generating AI displays converted information on the display and provides it to hearing individuals. The generating AI selects an appropriate display format and layout to display the information. For example, the generating AI selects an appropriate font size and display format to display the information clearly. The generating AI considers the type and resolution of the display to display the information. For example, the generating AI displays the information in the optimal format according to the resolution and size of the display. This allows the generating AI to accurately display the converted information.

[0083] The speech conversion unit can estimate the user's emotions and adjust the accuracy of speech conversion based on those emotions. For example, the speech conversion unit uses a generative AI to estimate the user's emotions and adjusts the accuracy of speech conversion based on those emotions. The generative AI estimates the user's emotions using facial recognition technology and speech analysis technology. For example, the generative AI captures the user's facial expressions with a camera and analyzes the facial expression data to estimate emotions. The generative AI analyzes the user's voice and estimates emotions based on that voice data. The generative AI dynamically changes the parameters of the speech recognition model to adjust the accuracy of speech conversion based on the user's emotions. For example, if the user is nervous, the generative AI analyzes the tone of voice and converts it to text more clearly. If the user is relaxed, the generative AI considers the intonation of the voice and performs natural text conversion. If the user is excited, the generative AI reflects the intensity of the voice when converting to text. This allows for more appropriate text conversion by adjusting the accuracy of speech conversion according to the user's emotions.

[0084] The speech conversion unit can improve conversion accuracy by considering the speaker's accent and dialect during speech conversion. For example, the speech conversion unit uses a generation AI to convert speech to text while considering the speaker's accent and dialect. The generation AI identifies the speaker's accent and dialect using a speech database and speech feature extraction technology. For example, if the speaker uses Kansai dialect, the generation AI captures its features and converts them accurately to text. If the speaker has a foreign accent, the generation AI considers that accent when converting to text. If the speaker uses a local dialect, the generation AI refers to a dialect dictionary and converts it accurately to text. This improves the accuracy of speech conversion by considering the speaker's accent and dialect. The generation AI extracts speech features and converts them to text based on those features. For example, the generation AI analyzes the waveform data of the speech, identifies phonemes and words, and converts them to text. The generation AI understands the context of the speech and generates an appropriate string of characters. For example, the generation AI considers the context before and after the speech to generate an accurate string of characters. This allows the generation AI to convert speech to text while considering the speaker's accent and dialect.

[0085] The speech conversion unit can improve conversion accuracy by removing background noise during speech conversion. For example, the speech conversion unit uses a generation AI to remove background noise and convert speech to text. The generation AI removes background noise using noise cancellation and filtering technologies. For example, if the speaker is speaking in a noisy environment, the generation AI filters out background noise and converts speech to text. If the speaker is speaking in a place with strong wind noise, the generation AI removes wind noise and converts speech to text. If the speaker is speaking in a place with music playing, the generation AI removes the music and converts speech to text. This improves the accuracy of speech conversion by removing background noise. The generation AI extracts features from the speech and converts them to text based on those features. For example, the generation AI analyzes the waveform data of the speech, identifies phonemes and words, and converts them to text. The generation AI understands the context of the speech and generates appropriate strings of characters. For example, the generation AI considers the context before and after the speech to generate accurate strings of characters. This allows the generation AI to remove background noise and convert speech to text.

[0086] The speech conversion unit can estimate the user's emotions and adjust the display method of the converted text based on the estimated emotions. For example, the speech conversion unit uses a generation AI to estimate the user's emotions and adjusts the display method of the text based on those emotions. The generation AI estimates the user's emotions using facial recognition technology and voice analysis technology. For example, the generation AI captures the user's facial expression with a camera and analyzes the facial expression data to estimate emotions. The generation AI analyzes the user's voice and estimates emotions based on the voice data. The generation AI dynamically changes the font size, color, and layout to adjust the display method of the text based on the user's emotions. For example, if the user is nervous, the generation AI selects a simple and highly legible font. If the user is relaxed, the generation AI selects a colorful and cheerful font. If the user is in a hurry, the generation AI selects a large and easy-to-read font. This allows for more appropriate display by adjusting the display method of the text according to the user's emotions.

[0087] The speech conversion unit can improve conversion accuracy by considering the speaker's age and gender during speech conversion. For example, the speech conversion unit uses a generation AI to convert speech to text while considering the speaker's age and gender. The generation AI identifies the speaker's age and gender using speech feature extraction technology and database referencing technology. For example, if the speaker is a child, the generation AI captures their characteristics and converts them accurately to text. If the speaker is an elderly person, the generation AI captures their characteristics and converts them accurately to text. If the speaker is female, the generation AI captures her characteristics and converts them accurately to text. This improves the accuracy of speech conversion by considering the speaker's age and gender. The generation AI extracts speech features and converts them to text based on those features. For example, the generation AI analyzes the waveform data of the speech, identifies phonemes and words, and converts them to text. The generation AI understands the context of the speech and generates an appropriate string of characters. For example, the generation AI considers the context before and after the speech to generate an accurate string of characters. This allows the generation AI to convert speech to text while considering the speaker's age and gender.

[0088] The speech conversion unit can adjust the conversion speed according to the speaker's speaking speed during speech conversion. For example, the speech conversion unit uses a generation AI to convert speech to text according to the speaker's speaking speed. The generation AI identifies the speaker's speaking speed using real-time processing technology and speed detection algorithms. For example, if the speaker is speaking quickly, the generation AI converts speech to text at a high speed according to the speaker's speed. If the speaker is speaking slowly, the generation AI converts speech to text slowly according to the speaker's speed. If the speaker is speaking intermittently, the generation AI converts speech to text at the appropriate timing. By adjusting the conversion speed according to the speaker's speaking speed, the accuracy of speech conversion is improved. The generation AI extracts features of the speech and converts them to text based on those features. For example, the generation AI analyzes the waveform data of the speech, identifies phonemes and words, and converts them to text. The generation AI understands the context of the speech and generates appropriate strings of characters. For example, the generation AI considers the context before and after the speech to generate accurate strings of characters. As a result, the generation AI can convert speech to text according to the speaker's speaking speed.

[0089] The sign language input unit can estimate the user's emotions and adjust the sensitivity of the sign language input based on those estimated emotions. For example, the sign language input unit uses generative AI to estimate the user's emotions and adjusts the sensitivity of the sign language input based on those emotions. The generative AI estimates the user's emotions using facial recognition technology and voice analysis technology. For example, the generative AI captures the user's facial expressions with a camera and analyzes the facial expression data to estimate emotions. The generative AI analyzes the user's voice and estimates emotions based on the voice data. The generative AI dynamically changes the sensor sensitivity to adjust the sensitivity of the sign language input based on the user's emotions. For example, if the user is nervous, the generative AI will capture sign language movements more sensitively. If the user is relaxed, the generative AI will capture sign language movements more naturally. If the user is in a hurry, the generative AI will capture sign language movements quickly. By adjusting the sensitivity of the sign language input according to the user's emotions, more appropriate sign language input becomes possible.

[0090] The sign language input unit can improve input accuracy by taking into account the speed of sign language movements during input. For example, the sign language input unit uses a generation AI to input sign language while considering the speed of the movements. The generation AI identifies the speed of sign language movements using motion detection algorithms and speed analysis techniques. For example, if the user performs sign language quickly, the generation AI adjusts the input accuracy according to that speed. If the user performs sign language slowly, the generation AI adjusts the input accuracy according to that speed. If the user performs sign language intermittently, the generation AI adjusts the input accuracy according to that speed. This improves the accuracy of sign language input by considering the speed of the movements. The generation AI analyzes the movements of the sign language and adjusts the input accuracy based on the speed of those movements. For example, the generation AI detects the speed of the sign language movements in real time and dynamically adjusts the input accuracy according to that speed. The generation AI sets appropriate input parameters considering the speed of the sign language movements. For example, the generation AI adjusts the sensor sensitivity and analysis algorithm parameters according to the speed of the sign language movements. This allows the generation AI to accurately input sign language while considering the speed of the movements.

[0091] The sign language input unit can estimate the user's emotions and determine the priority of sign language input based on those estimated emotions. For example, the sign language input unit uses generative AI to estimate the user's emotions and then determines the priority of sign language input based on those emotions. The generative AI estimates the user's emotions using facial recognition technology and voice analysis technology. For example, the generative AI captures the user's facial expressions with a camera and analyzes the facial expression data to estimate emotions. The generative AI analyzes the user's voice and estimates emotions based on the voice data. The generative AI uses a priority algorithm to determine the priority of sign language input based on the user's emotions. For example, if the user is nervous, the generative AI will prioritize capturing important sign language movements. If the user is relaxed, the generative AI will capture all sign language movements evenly. If the user is in a hurry, the generative AI will quickly capture sign language movements. This allows for more appropriate sign language input by determining the priority of sign language input according to the user's emotions.

[0092] The sign language input unit can improve input accuracy by considering the range of movement of sign language during input. For example, the sign language input unit inputs sign language while considering the range of movement of sign language using a generation AI. The generation AI identifies the range of movement of sign language using a movement range detection algorithm and range analysis technology. For example, if the user performs sign language with a wide range of movement, the generation AI adjusts the input accuracy according to that range. If the user performs sign language with a narrow range of movement, the generation AI adjusts the input accuracy according to that range. If the user performs sign language with an irregular range of movement, the generation AI adjusts the input accuracy according to that range. In this way, the accuracy of sign language input is improved by considering the range of movement of sign language. The generation AI analyzes the movement of sign language and adjusts the input accuracy based on that range of movement. For example, the generation AI detects the range of movement of sign language in real time and dynamically adjusts the input accuracy according to that range. The generation AI sets appropriate input parameters considering the range of movement of sign language. For example, the generation AI adjusts the sensor sensitivity and analysis algorithm parameters according to the range of movement of sign language. This allows the generating AI to accurately input sign language, taking into account the range of movement in sign language.

[0093] The sign language analysis unit can estimate the user's emotions and adjust the accuracy of the sign language analysis based on those estimated emotions. For example, the sign language analysis unit uses generative AI to estimate the user's emotions and adjusts the accuracy of the sign language analysis based on those emotions. The generative AI estimates the user's emotions using facial recognition technology and voice analysis technology. For example, the generative AI captures the user's facial expressions with a camera and analyzes the facial expression data to estimate emotions. The generative AI analyzes the user's voice and estimates emotions based on the voice data. The generative AI dynamically changes the parameters of the analysis algorithm to adjust the accuracy of the sign language analysis based on the user's emotions. For example, if the user is nervous, the generative AI analyzes subtle sign language movements more sensitively. If the user is relaxed, the generative AI analyzes natural sign language movements. If the user is in a hurry, the generative AI analyzes sign language movements quickly. By adjusting the accuracy of the sign language analysis according to the user's emotions, more appropriate sign language analysis becomes possible.

[0094] The sign language analysis unit can improve the accuracy of its analysis by considering the context of the sign language during analysis. For example, the sign language analysis unit analyzes sign language while considering its context using a generative AI. The generative AI identifies the context of sign language using a contextual analysis algorithm and a contextual database. For example, the generative AI improves the accuracy of its analysis based on the context by considering the actions before and after the sign language. The generative AI improves accuracy by analyzing the context in order to understand the meaning of the sign language. The generative AI improves accuracy by considering the context in order to analyze the sequence of sign language actions. As a result, the accuracy of sign language analysis is improved by considering the context of the sign language. The generative AI analyzes the actions of sign language and adjusts the analysis accuracy based on their context. For example, the generative AI detects the context of the sign language in real time and dynamically adjusts the analysis accuracy according to that context. The generative AI sets appropriate analysis parameters considering the context of the sign language. For example, the generative AI adjusts the parameters of the analysis algorithm according to the context of the sign language. As a result, the generative AI can accurately analyze sign language while considering its context.

[0095] The sign language analysis unit can improve the accuracy of sign language analysis by detecting subtle differences in sign language movements. For example, the sign language analysis unit uses a generative AI to detect subtle differences in sign language movements and analyze the sign language. The generative AI identifies subtle differences in sign language movements using high-precision sensors and motion analysis algorithms. For example, the generative AI improves analysis accuracy by detecting subtle differences in finger movements in sign language. The generative AI improves analysis accuracy by detecting subtle differences in the orientation of the palm in sign language. The generative AI improves analysis accuracy by detecting subtle differences in the position of the hand in sign language. In this way, the accuracy of sign language analysis is improved by detecting subtle differences in sign language movements. The generative AI analyzes sign language movements and adjusts the analysis accuracy based on these subtle differences. For example, the generative AI detects subtle differences in sign language movements in real time and dynamically adjusts the analysis accuracy according to these differences. The generative AI sets appropriate analysis parameters considering the subtle differences in sign language movements. For example, the generative AI adjusts the parameters of its analysis algorithm in response to subtle differences in sign language movements. This allows the generative AI to detect these subtle differences in sign language movements and accurately analyze the sign language.

[0096] The sign language analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the sign language analysis unit uses a generative AI to estimate the user's emotions and adjusts the display method of the analysis results based on those emotions. The generative AI estimates the user's emotions using facial recognition technology and voice analysis technology. For example, the generative AI captures the user's facial expressions with a camera and analyzes the facial expression data to estimate emotions. The generative AI analyzes the user's voice and estimates emotions based on the voice data. The generative AI dynamically changes the font size, color, and layout to adjust the display method of the analysis results based on the user's emotions. For example, if the user is nervous, the generative AI provides a simple and highly visible display method. If the user is relaxed, the generative AI provides a colorful and fun display method. If the user is in a hurry, the generative AI provides a large and easy-to-read display method. In this way, by adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible.

[0097] The sign language analysis unit can improve the accuracy of its analysis by considering regional differences in sign language. For example, the sign language analysis unit analyzes sign language while considering regional differences using a generative AI. The generative AI identifies regional differences in sign language using a regional database and region-specific motion analysis technology. For example, the generative AI improves the accuracy of its analysis by considering the differences in sign language from region to region. The generative AI improves accuracy by analyzing region-specific sign language movements. The generative AI improves the accuracy of its analysis by considering the differences in the meaning of sign language from region to region. As a result, the accuracy of sign language analysis is improved by considering regional differences in sign language. The generative AI analyzes sign language movements and adjusts the analysis accuracy based on those regional differences. For example, the generative AI detects regional differences in sign language in real time and dynamically adjusts the analysis accuracy according to those differences. The generative AI sets appropriate analysis parameters considering regional differences in sign language. For example, the generative AI adjusts the parameters of the analysis algorithm according to the regional differences in sign language. As a result, the generative AI can accurately analyze sign language while considering regional differences in sign language.

[0098] The sign language analysis unit can improve the accuracy of its analysis by considering the speed of sign language movements during the analysis. For example, the sign language analysis unit analyzes sign language while considering the speed of sign language movements using a generative AI. The generative AI identifies the speed of sign language movements using a speed detection algorithm and motion analysis technology. For example, when sign language is performed quickly, the generative AI adjusts the analysis accuracy according to the speed. When sign language is performed slowly, the generative AI adjusts the analysis accuracy according to the speed. When sign language is performed intermittently, the generative AI adjusts the analysis accuracy according to the speed. This improves the accuracy of sign language analysis by considering the speed of sign language movements. The generative AI analyzes sign language movements and adjusts the analysis accuracy based on their speed. For example, the generative AI detects the speed of sign language movements in real time and dynamically adjusts the analysis accuracy according to that speed. The generative AI sets appropriate analysis parameters considering the speed of sign language movements. For example, the generative AI adjusts the parameters of the analysis algorithm according to the speed of sign language movements. This allows the generative AI to accurately analyze sign language while considering the speed of sign language movements.

[0099] The conversion unit can estimate the user's emotions and adjust the presentation of the conversion results based on those estimated emotions. For example, the conversion unit uses a generative AI to estimate the user's emotions and adjusts the presentation of the conversion results based on those emotions. The generative AI estimates the user's emotions using facial recognition technology and voice analysis technology. For example, the generative AI captures the user's facial expressions with a camera and analyzes the facial expression data to estimate emotions. The generative AI analyzes the user's voice and estimates emotions based on the voice data. The generative AI dynamically changes font size, color, and layout to adjust the presentation of the conversion results based on the user's emotions. For example, if the user is nervous, the generative AI provides a simple and highly visible presentation. If the user is relaxed, the generative AI provides a colorful and fun presentation. If the user is in a hurry, the generative AI provides a large and easy-to-read presentation. By adjusting the presentation of the conversion results according to the user's emotions, more appropriate presentation becomes possible.

[0100] The conversion unit can improve conversion accuracy by considering the meaning of sign language movements during conversion. For example, the conversion unit converts sign language by considering the meaning of sign language movements using a generative AI. The generative AI identifies the meaning of sign language movements using a semantic analysis algorithm and a movement database. For example, the generative AI analyzes the meaning to understand the meaning of sign language movements and improves accuracy. The generative AI analyzes the meaning to improve accuracy by considering the context of sign language movements. The generative AI analyzes the meaning to improve accuracy by considering the continuity of sign language movements and improves accuracy. As a result, conversion accuracy is improved by considering the meaning of sign language movements. The generative AI analyzes sign language movements and adjusts the conversion accuracy based on their meaning. For example, the generative AI detects the meaning of sign language movements in real time and dynamically adjusts the conversion accuracy according to that meaning. The generative AI sets appropriate conversion parameters considering the meaning of sign language movements. For example, the generative AI adjusts the parameters of the conversion algorithm according to the meaning of the sign language movements. As a result, the generative AI can accurately convert sign language by considering the meaning of sign language movements.

[0101] The conversion unit can improve conversion accuracy by considering the continuity of sign language movements during conversion. For example, the conversion unit uses a generation AI to convert sign language while considering the continuity of sign language movements. The generation AI identifies the continuity of sign language movements using a continuous motion analysis algorithm or motion sequence analysis technology. For example, if the sign language movements are continuous, the generation AI improves conversion accuracy by considering that continuity. If the sign language movements are discontinuous, the generation AI improves conversion accuracy by considering that discontinuity. If the sign language movements are complex, the generation AI improves conversion accuracy by considering that complexity. In this way, conversion accuracy is improved by considering the continuity of sign language movements. The generation AI analyzes the sign language movements and adjusts the conversion accuracy based on their continuity. For example, the generation AI detects the continuity of sign language movements in real time and dynamically adjusts the conversion accuracy according to that continuity. The generation AI sets appropriate conversion parameters considering the continuity of sign language movements. For example, the generation AI adjusts the parameters of the conversion algorithm according to the continuity of sign language movements. This allows the generating AI to accurately translate sign language by taking into account the continuity of the sign language movements.

[0102] The conversion unit can estimate the user's emotions and adjust the display order of the conversion results based on the estimated emotions. For example, the conversion unit uses a generative AI to estimate the user's emotions and adjusts the display order of the conversion results based on those emotions. The generative AI estimates the user's emotions using facial recognition technology and voice analysis technology. For example, the generative AI captures the user's facial expressions with a camera and analyzes the facial expression data to estimate emotions. The generative AI analyzes the user's voice and estimates emotions based on the voice data. The generative AI uses a display order algorithm to adjust the display order of the conversion results based on the user's emotions. For example, if the user is tense, the generative AI prioritizes displaying important information. If the user is relaxed, the generative AI displays all information evenly. If the user is in a hurry, the generative AI quickly displays important information. By adjusting the display order of the conversion results according to the user's emotions, a more appropriate display becomes possible.

[0103] The conversion unit can improve conversion accuracy by considering the frequency of sign language movements during conversion. For example, the conversion unit converts sign language by considering the frequency of sign language movements using a generation AI. The generation AI identifies the frequency of sign language movements using a frequency analysis algorithm and a movement database. For example, the generation AI improves conversion accuracy by prioritizing the analysis of frequently used sign language movements. The generation AI improves analysis accuracy by identifying rarely used sign language movements. The generation AI adjusts the conversion accuracy based on the frequency of sign language movements. This improves conversion accuracy by considering the frequency of sign language movements. The generation AI analyzes sign language movements and adjusts the conversion accuracy based on their frequency. For example, the generation AI detects the frequency of sign language movements in real time and dynamically adjusts the conversion accuracy according to that frequency. The generation AI sets appropriate conversion parameters considering the frequency of sign language movements. For example, the generation AI adjusts the parameters of the conversion algorithm according to the frequency of sign language movements. This allows the generation AI to accurately convert sign language by considering the frequency of sign language movements.

[0104] The conversion unit can improve conversion accuracy by considering the intensity of sign language movements during conversion. For example, the conversion unit converts sign language by considering the intensity of sign language movements using a generation AI. The generation AI identifies the intensity of sign language movements using an intensity analysis algorithm and a movement database. For example, if a sign language movement is strong, the generation AI improves conversion accuracy by considering its strength. If a sign language movement is weak, the generation AI improves conversion accuracy by considering its weakness. The generation AI adjusts the conversion accuracy based on the intensity of the sign language movements. As a result, conversion accuracy is improved by considering the intensity of sign language movements. The generation AI analyzes sign language movements and adjusts the conversion accuracy based on their intensity. For example, the generation AI detects the intensity of sign language movements in real time and dynamically adjusts the conversion accuracy according to that intensity. The generation AI sets appropriate conversion parameters considering the intensity of sign language movements. For example, the generation AI adjusts the parameters of the conversion algorithm according to the intensity of the sign language movements. As a result, the generation AI can accurately convert sign language by considering the intensity of sign language movements.

[0105] The text provider can estimate the user's emotions and adjust the font and size of the text provided based on those estimated emotions. For example, the text provider can use a generative AI to estimate the user's emotions and adjust the font and size of the text based on those emotions. The generative AI estimates the user's emotions using facial recognition technology and voice analysis technology. For example, the generative AI can capture the user's facial expression with a camera and analyze the facial expression data to estimate emotions. The generative AI can also analyze the user's voice and estimate emotions based on that voice data. The generative AI uses font selection algorithms and size adjustment technologies to adjust the font and size of the text based on the user's emotions. For example, if the user is nervous, the generative AI will select a simple and highly legible font. If the user is relaxed, the generative AI will select a colorful and cheerful font. If the user is in a hurry, the generative AI will select a large and easy-to-read font. By adjusting the font and size of the text according to the user's emotions, a more appropriate display becomes possible.

[0106] The text provider can customize the display method based on the user's visual preferences when providing text. For example, the text provider analyzes the user's visual preferences using a generative AI and customizes the display method based on those preferences. The generative AI identifies the user's visual preferences using a customization algorithm and user profile. For example, the generative AI analyzes the user's preferred colors and displays the text in those colors. The generative AI analyzes the user's preferred font style and displays the text in that font. The generative AI analyzes the user's preferred font size and displays the text in that size. This allows for more appropriate display by customizing the display method based on the user's visual preferences. The generative AI detects the user's visual preferences in real time and dynamically adjusts the display method according to those preferences. The generative AI sets appropriate display parameters based on the user's visual preferences. For example, the generative AI adjusts the parameters of the display algorithm according to the user's visual preferences. This allows the generative AI to accurately display text based on the user's visual preferences.

[0107] The text provider can estimate the user's emotions and adjust the color of the text based on those emotions. For example, the text provider can use a generative AI to estimate the user's emotions and adjust the color of the text based on those emotions. The generative AI estimates the user's emotions using facial recognition technology and voice analysis technology. For example, the generative AI can capture the user's facial expression with a camera and analyze the facial expression data to estimate emotions. The generative AI can also analyze the user's voice and estimate emotions based on that voice data. The generative AI uses color selection algorithms and color adjustment technologies to adjust the color of the text based on the user's emotions. For example, if the user is tense, the generative AI will display text in a calm color. If the user is relaxed, the generative AI will display text in a bright color. If the user is in a hurry, the generative AI will display text in a highly visible color. By adjusting the color of the text according to the user's emotions, a more appropriate display becomes possible.

[0108] The character provisioning unit can select the optimal display method when providing characters, taking into account the user's device information. For example, the character provisioning unit analyzes the user's device information using a generation AI and selects the optimal display method based on that information. The generation AI identifies the user's device information using a device information analysis algorithm and a device profile. For example, if the user is using a smartphone, the generation AI provides a display method that matches the screen size. If the user is using a tablet, the generation AI provides a display method optimized for a large screen. If the user is using a smartwatch, the generation AI provides a concise and highly visible display method. In this way, the optimal display method can be provided by taking the user's device information into consideration. The generation AI detects the user's device information in real time and dynamically adjusts the display method according to that information. The generation AI sets appropriate display parameters based on the user's device information. For example, the generation AI adjusts the parameters of the display algorithm according to the user's device information. In this way, the generation AI can accurately display characters based on the user's device information.

[0109] The display unit can estimate the user's emotions and adjust the layout of the displayed content based on those emotions. For example, the display unit can use generative AI to estimate the user's emotions and adjust the layout of the displayed content based on those emotions. The generative AI estimates the user's emotions using facial recognition technology and voice analysis technology. For example, the generative AI can capture the user's facial expressions with a camera and analyze the facial expression data to estimate emotions. The generative AI can also analyze the user's voice and estimate emotions based on that voice data. The generative AI uses layout adjustment algorithms and layout design technologies to adjust the layout of the displayed content based on the user's emotions. For example, if the user is tense, the generative AI provides a simple and highly visible layout. If the user is relaxed, the generative AI provides a colorful and fun layout. If the user is in a hurry, the generative AI provides a large and easy-to-read layout. By adjusting the layout of the displayed content according to the user's emotions, a more appropriate display becomes possible.

[0110] The display unit can select the optimal display method by referring to the user's past display history when displaying content. For example, the display unit uses a generation AI to analyze the user's past display history and selects the optimal display method based on that history. The generation AI identifies the user's past display history using a history analysis algorithm and a history database. For example, the generation AI refers to and provides display methods that the user has preferred to use in the past. The generation AI refers to and provides fonts and colors that the user has used in the past. The generation AI refers to and provides layouts that the user has used in the past. In this way, the optimal display method can be provided by referring to the user's past display history. The generation AI detects the user's past display history in real time and dynamically adjusts the display method according to that history. The generation AI sets appropriate display parameters based on the user's past display history. For example, the generation AI adjusts the parameters of the display algorithm according to the user's past display history. In this way, the generation AI can accurately display characters based on the user's past display history.

[0111] The display unit can estimate the user's emotions and determine the priority of the displayed content based on those emotions. For example, the display unit uses generative AI to estimate the user's emotions and determines the priority of the displayed content based on those emotions. The generative AI estimates the user's emotions using facial recognition technology and voice analysis technology. For example, the generative AI captures the user's facial expressions with a camera and analyzes the facial expression data to estimate emotions. The generative AI analyzes the user's voice and estimates emotions based on the voice data. The generative AI uses a priority algorithm and emotion analysis results to determine the priority of the displayed content based on the user's emotions. For example, if the user is tense, the generative AI will prioritize displaying important information. If the user is relaxed, the generative AI will display general information evenly. If the user is in a hurry, the generative AI will quickly display important information. This allows for more appropriate display by determining the priority of the displayed content according to the user's emotions.

[0112] The display unit can select the optimal display method when displaying information, taking into account the user's device information. For example, the display unit analyzes the user's device information using a generation AI and selects the optimal display method based on that information. The generation AI identifies the user's device information using a device information analysis algorithm and a device profile. For example, if the user is using a smartphone, the generation AI provides a display method that matches the screen size. If the user is using a tablet, the generation AI provides a display method optimized for a large screen. If the user is using a smartwatch, the generation AI provides a concise and highly visible display method. In this way, the optimal display method can be provided by taking into account the user's device information. The generation AI detects the user's device information in real time and dynamically adjusts the display method according to that information. The generation AI sets appropriate display parameters based on the user's device information. For example, the generation AI adjusts the parameters of the display algorithm according to the user's device information. In this way, the generation AI can accurately display characters based on the user's device information.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The communication system can also be equipped with a translation unit. This unit uses generative AI to translate between different languages. For example, if a deaf person expresses something in sign language, the generative AI can convert the sign language into text, and the translation unit can then translate that text into another language. This allows users who speak different languages ​​to communicate smoothly. The translation unit can support translation between multiple languages, such as English to Japanese or French to Spanish. Furthermore, the translation unit can automatically select the translation language based on the user's language settings. This enables real-time communication between users who speak different languages.

[0115] The communication system can also be equipped with an emotional feedback unit. This unit uses generative AI to estimate the user's emotions and provides feedback based on those emotions. For example, when a deaf person expresses themselves using sign language, the generative AI estimates the emotion from the sign language movements, and the emotional feedback unit provides feedback corresponding to that emotion. This allows the user to confirm whether their emotions are being communicated correctly. The emotional feedback unit can, for example, provide positive feedback if the user is happy and display a supportive message if the user is sad. Furthermore, the emotional feedback unit can adjust the tone of communication based on the user's emotions, resulting in more emotionally richer communication.

[0116] The communication system may also include a context analysis unit. This unit uses generative AI to analyze the context of the communication and generate an appropriate response based on that context. For example, if a deaf person asks a question using sign language, the generative AI analyzes the context of the question, and the context analysis unit generates an appropriate response. This enables more natural communication. The context analysis unit can generate responses by considering, for example, past conversation history or the current flow of conversation. Furthermore, the context analysis unit can collect and analyze data from multiple sources to accurately understand the user's intent. This allows for an accurate understanding of the user's intent and the provision of appropriate responses.

[0117] The communication system can also include a user profile management unit. This unit uses generative AI to manage each user's profile and provides customized services based on that profile. For example, it can store the sign language style and preferences of deaf individuals in their profile, and the generative AI can use this information to improve the accuracy of sign language analysis. This enables communication optimized for each user. The user profile management unit can also manage, for example, a user's language settings, preferred font size, and display format. Furthermore, the user profile management unit can provide personalized services based on a user's past communication history. This allows users to communicate more comfortably.

[0118] The communication system can also be equipped with a real-time translation unit. This unit uses generative AI to perform real-time translation between different languages. For example, if a deaf person expresses something in sign language, the generative AI converts the sign language into text, and the real-time translation unit translates that text into a different language. This allows users who speak different languages ​​to communicate smoothly. The real-time translation unit can support translation between multiple languages, such as English to Japanese or French to Spanish. Furthermore, the real-time translation unit can automatically select the translation language based on the user's language settings. This enables real-time communication between users who speak different languages.

[0119] The communication system can also be equipped with an emotion analysis unit. This unit uses generative AI to analyze the user's emotions and provide appropriate feedback based on those emotions. For example, when a deaf person expresses themselves using sign language, the generative AI analyzes the emotions from the sign language movements, and the emotion analysis unit provides feedback corresponding to those emotions. This allows the user to confirm whether their emotions are being communicated correctly. The emotion analysis unit can, for example, provide positive feedback if the user is happy and display a supportive message if the user is sad. Furthermore, the emotion analysis unit can adjust the tone of communication based on the user's emotions, resulting in more emotionally richer communication.

[0120] The communication system can also include a health management unit that monitors the user's health status. This unit uses generative AI to monitor the user's health and provides appropriate advice based on that information. For example, if a deaf person expresses themselves using sign language, the generative AI can estimate their health status from the sign language movements, and the health management unit can provide advice based on that information. This allows users to understand their health status in real time. The health management unit can also monitor the user's heart rate and stress level, and provide appropriate advice based on that information. Furthermore, the health management unit can adjust the tone of communication based on the user's health status. This enables appropriate communication tailored to the user's health condition.

[0121] The communication system can also include a learning support unit to assist the user's learning. This unit uses generative AI to analyze the user's learning progress and provide appropriate learning support based on that analysis. For example, when a deaf person expresses themselves using sign language, the generative AI analyzes their learning progress from the sign language movements, and the learning support unit provides appropriate learning support based on that information. This allows the user to learn effectively. The learning support unit can also monitor the user's learning progress and provide appropriate learning advice based on that information. Furthermore, the learning support unit can provide a customized learning plan based on the user's learning style. This allows the user to learn using the method best suited to them.

[0122] The communication system may further include an emotion adjustment unit that estimates the user's emotions and adjusts the content of communication based on those emotions. The emotion adjustment unit uses generative AI to estimate the user's emotions and adjusts the content of communication based on those emotions. For example, when a deaf person expresses themselves using sign language, the generative AI estimates the emotion from the sign language movements, and the emotion adjustment unit provides communication content that corresponds to that emotion. This allows the user to communicate appropriately according to their emotions. For example, the emotion adjustment unit can provide relaxing content when the user is tense and cheerful content when the user is relaxed. The emotion adjustment unit can also adjust the tone of communication based on the user's emotions. This enables more emotionally rich communication.

[0123] The communication system can also include a privacy protection unit to further safeguard user privacy. This unit uses generation AI to protect user privacy information and prevent unauthorized access. For example, if a deaf person expresses something in sign language, the generation AI can convert the sign language into text, and the privacy protection unit can then encrypt and protect that text information. This ensures user privacy. The privacy protection unit can, for example, encrypt user personal information and communication content to protect it from unauthorized access. Furthermore, the privacy protection unit can restrict the scope of information sharing based on the user's privacy settings. This allows users to communicate with peace of mind.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The speech conversion unit converts speech into text. For example, it uses a generation AI to instantly convert speech into text. The generation AI analyzes speech using speech recognition technology and natural language processing technology and outputs text information. Step 2: The sign language input unit captures sign language movements with a camera. For example, a high-resolution camera is used to film sign language movements, and the video is input into the generation AI. Step 3: The sign language analysis unit analyzes the sign language movements captured by the sign language input unit. For example, it analyzes the sign language movements using generative AI and understands their meaning using image processing technology and machine learning algorithms. Step 4: The conversion unit converts the information analyzed by the sign language analysis unit into speech or text. For example, it uses generation AI to convert sign language actions into speech or text using speech synthesis technology or text generation technology. Step 5: The text provision unit provides the text information converted by the speech conversion unit. For example, it sends the text information converted using generation AI to a display device and provides it to people with hearing impairments. Step 6: The display unit displays the information converted by the conversion unit. For example, the information converted using the generation AI is displayed on the screen and provided to people with hearing.

[0126] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0127] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0128] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0129] Each of the multiple elements described above, including the voice conversion unit, sign language input unit, sign language analysis unit, conversion unit, character provision unit, and display unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the voice conversion unit acquires voice using the microphone 38B of the smart device 14 and converts it into characters using the specific processing unit 290 of the data processing unit 12. The sign language input unit captures sign language movements using the camera 42 of the smart device 14. The sign language analysis unit analyzes sign language movements using the specific processing unit 290 of the data processing unit 12. The conversion unit converts sign language movements into voice or characters using the specific processing unit 290 of the data processing unit 12. The character provision unit provides the character information converted by the specific processing unit 290 of the data processing unit 12. The display unit displays the converted information using the display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the voice conversion unit, sign language input unit, sign language analysis unit, conversion unit, character provision unit, and display unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the voice conversion unit acquires voice using the microphone 238 of the smart glasses 214 and converts it into characters using the specific processing unit 290 of the data processing unit 12. The sign language input unit captures sign language movements using the camera 42 of the smart glasses 214. The sign language analysis unit analyzes sign language movements using the specific processing unit 290 of the data processing unit 12. The conversion unit converts sign language movements into voice or characters using the specific processing unit 290 of the data processing unit 12. The character provision unit provides the character information converted by the specific processing unit 290 of the data processing unit 12. The display unit displays the converted information using the display of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the voice conversion unit, sign language input unit, sign language analysis unit, conversion unit, character provision unit, and display unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the voice conversion unit acquires voice using the microphone 238 of the headset terminal 314 and converts it into characters using the specific processing unit 290 of the data processing unit 12. The sign language input unit captures sign language movements using the camera 42 of the headset terminal 314. The sign language analysis unit analyzes sign language movements using the specific processing unit 290 of the data processing unit 12. The conversion unit converts sign language movements into voice or characters using the specific processing unit 290 of the data processing unit 12. The character provision unit provides the character information converted by the specific processing unit 290 of the data processing unit 12. The display unit displays the converted information using the display 343 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] As shown in Figure 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.

[0164] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0165] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0168] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0169] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0170] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0171] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0172] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0173] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0174] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0175] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0176] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0177] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0178] Each of the multiple elements described above, including the voice conversion unit, sign language input unit, sign language analysis unit, conversion unit, character provision unit, and display unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the voice conversion unit acquires voice using the microphone 238 of the robot 414 and converts it into characters using the specific processing unit 290 of the data processing unit 12. The sign language input unit captures sign language movements using the camera 42 of the robot 414. The sign language analysis unit analyzes sign language movements using the specific processing unit 290 of the data processing unit 12. The conversion unit converts sign language movements into voice or characters using the specific processing unit 290 of the data processing unit 12. The character provision unit provides the character information converted by the specific processing unit 290 of the data processing unit 12. The display unit displays the converted information using the display of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0179] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0180] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0181] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0182] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0183] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0184] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0185] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0186] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0187] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0188] 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.

[0189] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0190] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0191] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0192] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0193] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0194] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0195] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0196] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0197] (Note 1) A speech conversion unit that converts speech into text, A sign language input unit that captures sign language movements with a camera, A sign language analysis unit analyzes the sign language movements captured by the sign language input unit, A conversion unit that converts the information analyzed by the sign language analysis unit into speech or text, A character provisioning unit that provides character information converted by the voice conversion unit, The system includes a display unit that displays the information converted by the conversion unit. A system characterized by the following features. (Note 2) The aforementioned voice conversion unit is AI-generated speech instantly converts speech into text. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned sign language input unit is Capture sign language movements using a camera. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned sign language analysis unit is The AI ​​generates the signs to analyze the movements of sign language. The system described in Appendix 1, characterized by the features described herein. (Note 5) The conversion unit is Generative AI converts sign language gestures into speech and text. The system described in Appendix 1, characterized by the features described herein. (Note 6) The character provisioning unit is, Provides text information converted by a generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned display unit is Displaying information transformed by the generation AI. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned voice conversion unit is It estimates the user's emotions and adjusts the accuracy of speech conversion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned voice conversion unit is When converting speech, the accuracy of the conversion is improved by taking into account the speaker's accent and dialect. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned voice conversion unit is Remove background noise during speech conversion to improve conversion accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned voice conversion unit is It estimates the user's emotions and adjusts how the translated text is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned voice conversion unit is When converting speech, the accuracy of the conversion is improved by taking into account the speaker's age and gender. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned voice conversion unit is During speech conversion, the conversion speed is adjusted according to the speaker's speaking speed. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned sign language input unit is It estimates the user's emotions and adjusts the sensitivity of sign language input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned sign language input unit is When inputting sign language, the input accuracy is improved by taking into account the speed of sign language movements. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned sign language input unit is It estimates the user's emotions and determines the priority of sign language input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned sign language input unit is When inputting sign language, the input accuracy is improved by taking into account the range of movement in sign language. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned sign language analysis unit is The system estimates the user's emotions and adjusts the accuracy of sign language analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned sign language analysis unit is When analyzing sign language, consider the context of the sign language to improve analysis accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned sign language analysis unit is During sign language analysis, we detect subtle differences in sign language movements to improve analysis accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned sign language analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned sign language analysis unit is When analyzing sign language, we improve the accuracy of the analysis by taking into account regional differences in sign language. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned sign language analysis unit is When analyzing sign language, we improve the accuracy of the analysis by taking into account the speed of the sign language movements. The system described in Appendix 1, characterized by the features described herein. (Note 24) The conversion unit is It estimates the user's emotions and adjusts the way the conversion results are represented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The conversion unit is During conversion, the meaning of sign language movements is taken into consideration to improve conversion accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 26) The conversion unit is During conversion, the accuracy of the conversion is improved by considering the continuity of sign language movements. The system described in Appendix 1, characterized by the features described herein. (Note 27) The conversion unit is It estimates the user's emotions and adjusts the display order of the conversion results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The conversion unit is During conversion, the frequency of sign language movements is taken into consideration to improve conversion accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 29) The conversion unit is During conversion, the accuracy of the conversion is improved by taking into account the intensity of the sign language movements. The system described in Appendix 1, characterized by the features described herein. (Note 30) The character provisioning unit is, It estimates the user's emotions and adjusts the font and size of the text provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The character provisioning unit is, When providing text, customize the display method based on the user's visual preferences. The system described in Appendix 1, characterized by the features described herein. (Note 32) The character provisioning unit is, It estimates the user's emotions and adjusts the color of the text provided based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The character provisioning unit is, When providing text, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned display unit is It estimates the user's emotions and adjusts the layout of the displayed content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned display unit is When displaying content, the system selects the optimal display method by referring to the user's past viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned display unit is It estimates the user's emotions and determines the priority of displayed content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned display unit is When displaying content, the system selects the optimal display method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0198] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A speech conversion unit that converts speech into text, A sign language input unit that captures sign language movements with a camera, A sign language analysis unit analyzes the sign language movements captured by the sign language input unit, A conversion unit that converts the information analyzed by the sign language analysis unit into speech or text, A character provisioning unit that provides character information converted by the voice conversion unit, The system includes a display unit that displays the information converted by the conversion unit. A system characterized by the following features.

2. The aforementioned voice conversion unit is AI-generated speech instantly converts it into text. The system according to feature 1.

3. The aforementioned sign language input unit is Capture sign language movements using a camera. The system according to feature 1.

4. The aforementioned sign language analysis unit is Generative AI analyzes sign language movements. The system according to feature 1.

5. The conversion unit is Generative AI converts sign language gestures into speech and text. The system according to feature 1.

6. The character provisioning unit is, Provides text information converted by generation AI. The system according to feature 1.

7. The aforementioned display unit is Displaying information converted by the generating AI. The system according to feature 1.

8. The aforementioned voice conversion unit is It estimates the user's emotions and adjusts the accuracy of speech conversion based on the estimated emotions. The system according to feature 1.

9. The aforementioned voice conversion unit is When converting speech, the accuracy of the conversion is improved by taking into account the speaker's accent and dialect. The system according to feature 1.

10. The aforementioned voice conversion unit is Remove background noise during speech conversion to improve conversion accuracy. The system according to feature 1.

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