system

Abstract

The system according to this embodiment aims to analyze sign language and gestures in real time and translate them into speech and text. [Solution] The system according to the embodiment comprises an analysis unit, a translation unit, a text conversion unit, and a calling unit. The analysis unit analyzes sign language and gestures in real time via a camera. The translation unit translates the sign language and gestures analyzed by the analysis unit into speech and text. The text conversion unit converts the speech translated by the translation unit into text. The calling unit displays the text converted by the text conversion unit.

Description

Technical Field

[0006] , , ,

[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, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the real-time analysis of sign language and gestures and the translation into speech or text have not been sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze sign language and gestures in real time and translate them into speech or text.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a translation unit, a text conversion unit, and a calling unit. The analysis unit analyzes sign language and gestures in real time via a camera. The translation unit translates the sign language and gestures analyzed by the analysis unit into speech and text. The text conversion unit converts the speech translated by the translation unit into text. The calling unit displays the text converted by the text conversion unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze sign language and gestures in real time and translate them into speech and text. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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) An innovative application for automating sign language and gesture interpretation according to an embodiment of the present invention is a system that analyzes sign language and gestures in real time via a camera and translates them into speech or text. This system enables smooth communication with the hearing impaired and the elderly, improving inclusive experiences in society. Furthermore, in communication with the elderly, it is possible to avoid loud exchanges and communicate on a text basis, similar to written communication. Calls are also made via text display rather than loud voice, making it easier for the elderly to understand. For example, sign language and gestures are analyzed in real time via a camera. In this process, the camera captures and analyzes the user's hand movements and gestures. For example, if "good morning" is expressed in sign language, the camera captures and analyzes the hand movements. Next, the analyzed sign language and gestures are translated into speech or text. For example, if "good morning" is expressed in sign language, it will be played back as "good morning" or displayed as text. In this way, smooth communication is facilitated with the hearing impaired and the elderly. Furthermore, to avoid the need for loud voice communication with the elderly, the system includes a function that converts speech to text. For example, if a speaker says "hello," it will be displayed as text. This allows for communication using large, easy-to-read fonts for the elderly. Greetings can also be made via text rather than loud voice. For example, if a speaker says "good morning," it will be displayed in large letters. This makes it easier for the elderly to understand intuitively. This facilitates smooth communication with the hearing impaired and the elderly, improving inclusive experiences in society. For example, the content of a hearing-impaired person's sign language is translated into voice and text in real time, enabling smooth communication with those around them. Also, elderly people can communicate via text without having to speak loudly, making it easier to understand. Thus, innovative applications that automate sign language and gesture interpretation can facilitate smooth communication with the hearing impaired and the elderly, improving inclusive experiences in society.

[0029] The system for automating sign language interpretation and gesture interpretation according to this embodiment comprises an analysis unit, a translation unit, a text conversion unit, and a calling unit. The analysis unit analyzes sign language and gestures in real time via a camera. The analysis unit, for example, captures and analyzes the user's hand movements and gestures via a camera. For example, if the user expresses "good morning" in sign language, the camera captures and analyzes the hand movements. The translation unit translates the sign language and gestures analyzed by the analysis unit into speech or text. The translation unit, for example, translates the analyzed sign language and gestures into speech or text. For example, if the user expresses "good morning" in sign language, it is either played back as speech or displayed as text. The text conversion unit converts the speech translated by the translation unit into text. The text conversion unit, for example, converts speech into text. For example, if the speaker says "hello," it is displayed as text. The calling unit displays the text converted by the text conversion unit. The calling unit displays the text. For example, if the speaker says "Good morning," it will be displayed in large letters as "Good morning." This enables smooth communication with the hearing impaired and the elderly by analyzing sign language and gestures in real time and translating them into speech and text. Some or all of the above-described processes in the analysis unit, translation unit, text conversion unit, and calling unit may be performed using AI, or not using AI. For example, the analysis unit can input video data of sign language and gestures acquired by a camera into a generation AI and have the generation AI perform the analysis of sign language and gestures. The translation unit can input the sign language and gesture data analyzed by the analysis unit into a generation AI and have the generation AI perform the translation into speech and text. The text conversion unit can input the audio data translated by the translation unit into a generation AI and have the generation AI perform the conversion into text. The calling unit can input the text data converted by the text conversion unit into a generation AI and have the generation AI display the text.As a result, the system for automating sign language interpretation and gesture interpretation according to the embodiment can enable smooth communication with people with hearing impairments and the elderly.

[0030] The analysis unit analyzes sign language and gestures in real time via a camera. For example, the analysis unit captures and analyzes the user's hand movements and gestures through the camera. Specifically, the camera captures the user's hand movements in high resolution and processes the video data in real time. The analysis unit divides the video data frame by frame and executes algorithms to analyze the position, shape, and movement of the hand in each frame. Computer vision technology and deep learning models are used for this. For example, if the user expresses "good morning" in sign language, the camera captures and analyzes the hand movements. The analysis unit extracts the characteristics of the hand movements and identifies the meaning of the sign language by comparing them with an existing sign language database. Furthermore, the analysis unit can recognize sign language and gestures more accurately by analyzing not only hand movements but also facial expressions and body movements. As a result, the analysis unit can analyze the user's sign language and gestures with high accuracy and provide the data necessary for the next processing step.

[0031] The translation unit translates the sign language and gestures analyzed by the analysis unit into speech or text. Specifically, the translation unit receives the sign language and gesture data provided by the analysis unit and executes an algorithm to convert it into natural language. For example, if the sign language means "good morning," it will be played back as "good morning" or displayed as text. The translation unit also uses speech synthesis technology to output the meaning of the sign language and gestures as speech. A Text-to-Speech (TTS) engine is used for speech synthesis to convert text into speech. A display or screen is used to display the meaning of the analyzed sign language and gestures as text. The translation unit can provide both speech output and text display according to the user's needs. This allows the translation unit to translate sign language and gestures quickly and accurately, providing the user with appropriate information.

[0032] The text conversion unit converts the audio translated by the translation unit into text. For example, the text conversion unit converts audio to text. Specifically, it uses speech recognition technology to execute an algorithm for converting audio data into text data. For example, if a speaker says "hello," it will be displayed as "hello" in text. The text conversion unit uses a speech recognition engine to analyze the audio data in real time and generate the corresponding text. The speech recognition engine extracts features from the audio and identifies the content of the audio by matching them with an existing audio database. Furthermore, the text conversion unit formats the generated text data and displays it in a user-friendly format. This includes adjusting the font size, color, and display position of the text. This allows the text conversion unit to quickly and accurately convert audio data into text and provide users with visual information.

[0033] The calling unit displays the text converted by the text conversion unit. Specifically, it receives text data from the text conversion unit and executes an algorithm to display it on a display or screen. For example, if the speaker says "Good morning," it will be displayed in large letters as "Good morning." The calling unit can flexibly configure how the text is displayed. For example, by adjusting the font size, color, and display position of the text, it can provide information in a way that is easy for the user to read. The calling unit can also set the timing of text display and animation effects. This makes it easier for users to visually receive information, facilitating smoother communication. Furthermore, by linking multiple displays or screens, the calling unit can achieve wide-area information display. This allows the calling unit to provide users with visual information quickly and effectively, improving the quality of communication.

[0034] The analysis unit can capture and analyze the user's hand movements and gestures through the camera. For example, the analysis unit can capture and analyze the user's hand movements and gestures through the camera. For example, if the user expresses "good morning" in sign language, the camera will capture and analyze the hand movements. The analysis unit can also capture and analyze the user's hand movements and gestures through the camera. For example, the camera will capture the user's hand movements and analyze them. This improves the accuracy of sign language and gesture recognition by accurately capturing and analyzing the user's hand movements and gestures. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video data of sign language and gestures acquired by the camera into a generating AI and have the generating AI perform the analysis of sign language and gestures.

[0035] The translation unit can translate analyzed sign language and gestures into speech or text. For example, if the translation unit translates analyzed sign language and gestures into speech or text, it will either play "Good morning" as speech or display "Good morning" as text. The translation unit can also translate analyzed sign language and gestures into speech or text. For example, if the sign language expresses "Hello," it will either play "Hello" as speech or display "Hello" as text. This allows for smoother communication with the hearing impaired and the elderly by translating sign language and gestures into speech or text. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input the sign language and gesture data analyzed by the analysis unit into a generating AI, and have the generating AI perform the translation into speech or text.

[0036] The text conversion unit can convert speech into text. For example, if a speaker says "hello," it will be displayed as "hello" in text. The text conversion unit can also convert speech into text. For example, if a speaker says "good morning," it will be displayed as "good morning" in text. By converting speech into text, it becomes possible to communicate in large, easy-to-read font for the elderly. Some or all of the above processing in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input the speech data translated by the translation unit into a generating AI, and have the generating AI perform the conversion to text.

[0037] The calling unit can display text. For example, if the speaker says "Good morning," it will be displayed in large letters as "Good morning." The calling unit can also display text. For example, if the speaker says "Hello," it will be displayed in large letters as "Hello." This makes it easier for elderly people to understand intuitively by displaying text. Some or all of the above processing in the calling unit may be performed using AI, for example, or without AI. For example, the calling unit can input the text data converted by the text conversion unit into a generating AI, and have the generating AI perform the text display.

[0038] The analysis unit can optimize its analysis algorithm by referring to the user's past action history when analyzing sign language and gestures. For example, the analysis unit prioritizes analyzing sign language and gestures that the user has frequently used in the past. The analysis unit can also learn specific action patterns from the user's past action history to improve analysis accuracy. Furthermore, the analysis unit can apply individually customized analysis algorithms based on the user's past action history. This improves analysis accuracy and enables individually customized analysis by referring to the user's past action history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past action history data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0039] The analysis unit can perform analysis of sign language and gestures while taking into account the user's physical characteristics (hand size, speed of movement, etc.). For example, the analysis unit can adjust the parameters of the analysis algorithm according to the user's hand size. The analysis unit can also optimize the timing of the analysis according to the user's speed of movement. Furthermore, the analysis unit can pre-register the user's physical characteristics and improve the accuracy of the analysis based on that information. This improves the accuracy of the analysis by considering the user's physical characteristics, enabling more precise analysis. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's physical characteristic data into a generating AI and have the generating AI adjust the analysis algorithm.

[0040] The analysis unit can prioritize the analysis of region-specific sign language and gestures by considering the user's geographical location information when analyzing sign language and gestures. For example, if the user is in a specific region, the analysis unit will prioritize the analysis of region-specific sign language and gestures. The analysis unit can also learn region-specific movement patterns based on the user's geographical location information to improve analysis accuracy. Furthermore, if the user moves, the analysis unit can update the geographical location information in real time and adjust the analysis algorithm. This improves analysis accuracy and enables region-appropriate analysis by prioritizing the analysis of region-specific sign language and gestures. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information data into a generating AI and have the generating AI perform the analysis of region-specific sign language and gestures.

[0041] The analysis unit can analyze the user's social media activity when analyzing sign language and gestures, and prioritize the analysis of relevant actions. For example, the analysis unit can prioritize the analysis of sign language and gestures that the user frequently uses on social media. The analysis unit can also learn specific action patterns from the user's social media activity to improve analysis accuracy. Furthermore, the analysis unit can apply individually customized analysis algorithms based on the user's social media activity. This allows for the prioritization of relevant actions and improved analysis accuracy by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI perform the analysis of relevant actions.

[0042] The translation unit can adjust the level of detail in the translation based on the importance of sign language and gestures during the translation process. For example, the translation unit will provide a detailed translation for important sign language and gestures. It can also provide a concise translation for common sign language and gestures. Furthermore, the translation unit can dynamically adjust the level of detail in the translation depending on the context of the sign language and gestures. This ensures that important information is accurately conveyed by adjusting the level of detail in the translation based on the importance of sign language and gestures. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input importance data for sign language and gestures into a generating AI and have the generating AI perform the adjustment of the level of detail in the translation.

[0043] The translation unit can apply different translation algorithms depending on the category of sign language or gestures during translation. For example, the translation unit can apply a specific translation algorithm to sign language or gestures for greetings. It can also apply a different translation algorithm to sign language or gestures for questions. Furthermore, it can apply a translation algorithm that emphasizes emotion to sign language or gestures that express emotion. This improves translation accuracy by applying translation algorithms according to the category of sign language or gestures. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input category data of sign language and gestures into a generating AI and have the generating AI perform the application of translation algorithms.

[0044] The translation unit can determine translation priorities based on the timing of sign language and gestures during translation. For example, the translation unit may prioritize translating sign language and gestures related to important events. It can also postpone translating everyday sign language and gestures. Furthermore, the translation unit can dynamically adjust translation priorities according to the timing of sign language and gestures. This ensures that important information is translated preferentially by determining translation priorities based on the timing of sign language and gestures. Some or all of the above processing in the translation unit may be performed using AI, for example, or not. For example, the translation unit can input data on the timing of sign language and gestures into a generating AI and have the generating AI determine the translation priorities.

[0045] The translation unit can adjust the order of translations based on the relevance of sign language and gestures during the translation process. For example, the translation unit will prioritize translating highly relevant sign language and gestures. It can also postpone less relevant sign language and gestures. Furthermore, the translation unit can dynamically adjust the order of translations depending on the context of the sign language and gestures. This ensures that important information is translated preferentially by adjusting the order of translations based on the relevance of sign language and gestures. Some or all of the above processing in the translation unit may be performed using AI, for example, or not. For example, the translation unit can input relevance data of sign language and gestures into a generating AI and have the generating AI perform the adjustment of the translation order.

[0046] The text conversion unit can optimize its conversion algorithm by referring to the user's past speech history when converting speech to text. For example, the text conversion unit can prioritize the conversion of words that the user has frequently used in the past. The text conversion unit can also learn specific speech patterns from the user's past speech history to improve conversion accuracy. Furthermore, the text conversion unit can apply a customized conversion algorithm based on the user's past speech history. This improves conversion accuracy and enables customized conversion by referring to the user's past speech history. Some or all of the above processes in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input the user's past speech history data into a generating AI and have the generating AI perform the optimization of the conversion algorithm.

[0047] The text conversion unit can perform the conversion of speech to text while taking into account the user's speaking speed and accent. For example, the text conversion unit can adjust the parameters of the conversion algorithm according to the user's speaking speed. The text conversion unit can also improve the accuracy of the conversion according to the user's accent. Furthermore, the text conversion unit can pre-register the user's speaking speed and accent and improve the conversion accuracy based on that information. This improves conversion accuracy and enables more precise conversion by taking the user's speaking speed and accent into consideration. Some or all of the above processing in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input the user's speaking speed and accent data into a generating AI and have the generating AI adjust the conversion algorithm.

[0048] The text conversion unit can prioritize the conversion of region-specific language by considering the user's geographical location when converting speech to text. For example, if the user is in a specific region, the text conversion unit will prioritize the conversion of region-specific language. The text conversion unit can also learn region-specific speech patterns based on the user's geographical location to improve conversion accuracy. Furthermore, if the user moves, the text conversion unit can update the geographical location information in real time and adjust the conversion algorithm. This improves conversion accuracy by prioritizing the conversion of region-specific language, enabling conversion that is appropriate for the region. Some or all of the above processing in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input the user's geographical location data into a generating AI and have the generating AI perform the conversion of region-specific language.

[0049] The text conversion unit can analyze the user's social media activity and prioritize the conversion of relevant words when converting speech to text. For example, the text conversion unit can prioritize the conversion of words that the user frequently uses on social media. The text conversion unit can also learn specific speech patterns from the user's social media activity to improve conversion accuracy. Furthermore, the text conversion unit can apply individually customized conversion algorithms based on the user's social media activity. This improves conversion accuracy by prioritizing the conversion of relevant words through analysis of the user's social media activity. Some or all of the above processing in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input the user's social media activity data into a generating AI and have the generating AI perform the conversion of relevant words.

[0050] The prompting unit can select the optimal display method when displaying text by referring to the user's past display history. For example, the prompting unit may prioritize the use of fonts and colors that the user has previously preferred. The prompting unit can also learn specific display patterns from the user's past display history and provide the optimal display method. Furthermore, the prompting unit can apply individually customized display methods based on the user's past display history. This improves readability by providing the optimal display method by referring to the user's past display history. Some or all of the above processing in the prompting unit may be performed using AI, for example, or without AI. For example, the prompting unit can input the user's past display history data into a generating AI and have the generating AI select the display method.

[0051] The call-to-user function can adjust font size and color when displaying text, taking into account the user's eyesight and readability. For example, the call-to-user function can automatically adjust the font size according to the user's eyesight. It can also use high-contrast colors to improve user readability. Furthermore, the call-to-user function can pre-register the user's eyesight information and optimize the display method based on that information. This improves visibility and makes the display easier for the user by considering the user's eyesight and readability. Some or all of the above processing in the call-to-user function may be performed using AI, for example, or without AI. For example, the call-to-user function can input the user's eyesight information data into a generating AI and have the generating AI perform adjustments to the font size and color.

[0052] The calling unit can select the optimal display method when displaying text, taking into account the user's device information. For example, if the user is using a smartphone, the calling unit can provide a display method that matches the screen size. It can also provide a display method optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a smartwatch, the calling unit can provide a concise and highly visible display method. This ensures that the optimal display method is provided and visibility is improved by considering the user's device information. Some or all of the above processing in the calling unit may be performed using AI, or without AI. For example, the calling unit can input user device information data into a generating AI and have the generating AI select the display method.

[0053] The call-to-action unit can analyze the user's social media activity when displaying text and prioritize the display of relevant information. For example, the call-to-action unit can prioritize the display of information that the user frequently shows interest in on social media. The call-to-action unit can also learn specific information patterns from the user's social media activity and provide the optimal display method. Furthermore, the call-to-action unit can apply individually customized display methods based on the user's social media activity. This improves visibility by prioritizing the display of relevant information through the analysis of the user's social media activity. Some or all of the above processing in the call-to-action unit may be performed using AI, for example, or without AI. For example, the call-to-action unit can input the user's social media activity data into a generating AI and have the generating AI perform the display of relevant information.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The analysis unit can optimize its analysis algorithm by referring to the user's past action history when analyzing sign language and gestures. For example, the analysis unit prioritizes analyzing sign language and gestures that the user has frequently used in the past. The analysis unit can also learn specific action patterns from the user's past action history to improve analysis accuracy. Furthermore, the analysis unit can apply individually customized analysis algorithms based on the user's past action history. This improves analysis accuracy and enables individually customized analysis by referring to the user's past action history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past action history data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0056] The analysis unit can perform analysis of sign language and gestures while taking into account the user's physical characteristics (hand size, speed of movement, etc.). For example, the analysis unit can adjust the parameters of the analysis algorithm according to the user's hand size. The analysis unit can also optimize the timing of the analysis according to the user's speed of movement. Furthermore, the analysis unit can pre-register the user's physical characteristics and improve the accuracy of the analysis based on that information. This improves the accuracy of the analysis by considering the user's physical characteristics, enabling more precise analysis. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's physical characteristic data into a generating AI and have the generating AI adjust the analysis algorithm.

[0057] The analysis unit can prioritize the analysis of region-specific sign language and gestures by considering the user's geographical location information when analyzing sign language and gestures. For example, if the user is in a specific region, the analysis unit will prioritize the analysis of region-specific sign language and gestures. The analysis unit can also learn region-specific movement patterns based on the user's geographical location information to improve analysis accuracy. Furthermore, if the user moves, the analysis unit can update the geographical location information in real time and adjust the analysis algorithm. This improves analysis accuracy and enables region-appropriate analysis by prioritizing the analysis of region-specific sign language and gestures. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information data into a generating AI and have the generating AI perform the analysis of region-specific sign language and gestures.

[0058] The translation unit can adjust the level of detail in the translation based on the importance of sign language and gestures during the translation process. For example, the translation unit will provide a detailed translation for important sign language and gestures. It can also provide a concise translation for common sign language and gestures. Furthermore, the translation unit can dynamically adjust the level of detail in the translation depending on the context of the sign language and gestures. This ensures that important information is accurately conveyed by adjusting the level of detail in the translation based on the importance of sign language and gestures. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input importance data for sign language and gestures into a generating AI and have the generating AI perform the adjustment of the level of detail in the translation.

[0059] The translation unit can apply different translation algorithms depending on the category of sign language or gestures during translation. For example, the translation unit can apply a specific translation algorithm to sign language or gestures for greetings. It can also apply a different translation algorithm to sign language or gestures for questions. Furthermore, it can apply a translation algorithm that emphasizes emotion to sign language or gestures that express emotion. This improves translation accuracy by applying translation algorithms according to the category of sign language or gestures. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input category data of sign language and gestures into a generating AI and have the generating AI perform the application of translation algorithms.

[0060] The translation unit can determine translation priorities based on the timing of sign language and gestures during translation. For example, the translation unit may prioritize translating sign language and gestures related to important events. It can also postpone translating everyday sign language and gestures. Furthermore, the translation unit can dynamically adjust translation priorities according to the timing of sign language and gestures. This ensures that important information is translated preferentially by determining translation priorities based on the timing of sign language and gestures. Some or all of the above processing in the translation unit may be performed using AI, for example, or not. For example, the translation unit can input data on the timing of sign language and gestures into a generating AI and have the generating AI determine the translation priorities.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The analysis unit analyzes sign language and gestures in real time via the camera. For example, it captures and analyzes the user's hand movements and gestures via the camera. If the user expresses "good morning" in sign language, the camera captures and analyzes the hand movements. Step 2: The translation unit translates the sign language and gestures analyzed by the analysis unit into speech or text. For example, if the analyzed sign language and gestures are translated into speech or text, then the sign language will either be played as "Good morning" or displayed as text. Step 3: The text conversion unit converts the audio translated by the translation unit into text. For example, if the speaker says "Hello," it will be displayed as "Hello" in text. Step 4: The greeting section displays the text converted by the text conversion section. For example, if the speaker says "Good morning," it will be displayed as "Good morning" in large letters.

[0063] (Example of form 2) An innovative application for automating sign language and gesture interpretation according to an embodiment of the present invention is a system that analyzes sign language and gestures in real time via a camera and translates them into speech or text. This system enables smooth communication with the hearing impaired and the elderly, improving inclusive experiences in society. Furthermore, in communication with the elderly, it is possible to avoid loud exchanges and communicate on a text basis, similar to written communication. Calls are also made via text display rather than loud voice, making it easier for the elderly to understand. For example, sign language and gestures are analyzed in real time via a camera. In this process, the camera captures and analyzes the user's hand movements and gestures. For example, if "good morning" is expressed in sign language, the camera captures and analyzes the hand movements. Next, the analyzed sign language and gestures are translated into speech or text. For example, if "good morning" is expressed in sign language, it will be played back as "good morning" or displayed as text. In this way, smooth communication is facilitated with the hearing impaired and the elderly. Furthermore, to avoid the need for loud voice communication with the elderly, the system includes a function that converts speech to text. For example, if a speaker says "hello," it will be displayed as text. This allows for communication using large, easy-to-read fonts for the elderly. Greetings can also be made via text rather than loud voice. For example, if a speaker says "good morning," it will be displayed in large letters. This makes it easier for the elderly to understand intuitively. This facilitates smooth communication with the hearing impaired and the elderly, improving inclusive experiences in society. For example, the content of a hearing-impaired person's sign language is translated into voice and text in real time, enabling smooth communication with those around them. Also, elderly people can communicate via text without having to speak loudly, making it easier to understand. Thus, innovative applications that automate sign language and gesture interpretation can facilitate smooth communication with the hearing impaired and the elderly, improving inclusive experiences in society.

[0064] The system for automating sign language interpretation and gesture interpretation according to this embodiment comprises an analysis unit, a translation unit, a text conversion unit, and a calling unit. The analysis unit analyzes sign language and gestures in real time via a camera. The analysis unit, for example, captures and analyzes the user's hand movements and gestures via a camera. For example, if the user expresses "good morning" in sign language, the camera captures and analyzes the hand movements. The translation unit translates the sign language and gestures analyzed by the analysis unit into speech or text. The translation unit, for example, translates the analyzed sign language and gestures into speech or text. For example, if the user expresses "good morning" in sign language, it is either played back as speech or displayed as text. The text conversion unit converts the speech translated by the translation unit into text. The text conversion unit, for example, converts speech into text. For example, if the speaker says "hello," it is displayed as text. The calling unit displays the text converted by the text conversion unit. The calling unit displays the text. For example, if the speaker says "Good morning," it will be displayed in large letters as "Good morning." This enables smooth communication with the hearing impaired and the elderly by analyzing sign language and gestures in real time and translating them into speech and text. Some or all of the above-described processes in the analysis unit, translation unit, text conversion unit, and calling unit may be performed using AI, or not using AI. For example, the analysis unit can input video data of sign language and gestures acquired by a camera into a generation AI and have the generation AI perform the analysis of sign language and gestures. The translation unit can input the sign language and gesture data analyzed by the analysis unit into a generation AI and have the generation AI perform the translation into speech and text. The text conversion unit can input the audio data translated by the translation unit into a generation AI and have the generation AI perform the conversion into text. The calling unit can input the text data converted by the text conversion unit into a generation AI and have the generation AI display the text.As a result, the system for automating sign language interpretation and gesture interpretation according to the embodiment can enable smooth communication with people with hearing impairments and the elderly.

[0065] The analysis unit analyzes sign language and gestures in real time via a camera. For example, the analysis unit captures and analyzes the user's hand movements and gestures through the camera. Specifically, the camera captures the user's hand movements in high resolution and processes the video data in real time. The analysis unit divides the video data frame by frame and executes algorithms to analyze the position, shape, and movement of the hand in each frame. Computer vision technology and deep learning models are used for this. For example, if the user expresses "good morning" in sign language, the camera captures and analyzes the hand movements. The analysis unit extracts the characteristics of the hand movements and identifies the meaning of the sign language by comparing them with an existing sign language database. Furthermore, the analysis unit can recognize sign language and gestures more accurately by analyzing not only hand movements but also facial expressions and body movements. As a result, the analysis unit can analyze the user's sign language and gestures with high accuracy and provide the data necessary for the next processing step.

[0066] The translation unit translates the sign language and gestures analyzed by the analysis unit into speech or text. Specifically, the translation unit receives the sign language and gesture data provided by the analysis unit and executes an algorithm to convert it into natural language. For example, if the sign language means "good morning," it will be played back as "good morning" or displayed as text. The translation unit also uses speech synthesis technology to output the meaning of the sign language and gestures as speech. A Text-to-Speech (TTS) engine is used for speech synthesis to convert text into speech. A display or screen is used to display the meaning of the analyzed sign language and gestures as text. The translation unit can provide both speech output and text display according to the user's needs. This allows the translation unit to translate sign language and gestures quickly and accurately, providing the user with appropriate information.

[0067] The text conversion unit converts the audio translated by the translation unit into text. For example, the text conversion unit converts audio to text. Specifically, it uses speech recognition technology to execute an algorithm for converting audio data into text data. For example, if a speaker says "hello," it will be displayed as "hello" in text. The text conversion unit uses a speech recognition engine to analyze the audio data in real time and generate the corresponding text. The speech recognition engine extracts features from the audio and identifies the content of the audio by matching them with an existing audio database. Furthermore, the text conversion unit formats the generated text data and displays it in a user-friendly format. This includes adjusting the font size, color, and display position of the text. This allows the text conversion unit to quickly and accurately convert audio data into text and provide users with visual information.

[0068] The calling unit displays the text converted by the text conversion unit. Specifically, it receives text data from the text conversion unit and executes an algorithm to display it on a display or screen. For example, if the speaker says "Good morning," it will be displayed in large letters as "Good morning." The calling unit can flexibly configure how the text is displayed. For example, by adjusting the font size, color, and display position of the text, it can provide information in a way that is easy for the user to read. The calling unit can also set the timing of text display and animation effects. This makes it easier for users to visually receive information, facilitating smoother communication. Furthermore, by linking multiple displays or screens, the calling unit can achieve wide-area information display. This allows the calling unit to provide users with visual information quickly and effectively, improving the quality of communication.

[0069] The analysis unit can capture and analyze the user's hand movements and gestures through the camera. For example, the analysis unit can capture and analyze the user's hand movements and gestures through the camera. For example, if the user expresses "good morning" in sign language, the camera will capture and analyze the hand movements. The analysis unit can also capture and analyze the user's hand movements and gestures through the camera. For example, the camera will capture the user's hand movements and analyze them. This improves the accuracy of sign language and gesture recognition by accurately capturing and analyzing the user's hand movements and gestures. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input video data of sign language and gestures acquired by the camera into a generating AI and have the generating AI perform the analysis of sign language and gestures.

[0070] The translation unit can translate analyzed sign language and gestures into speech or text. For example, if the translation unit translates analyzed sign language and gestures into speech or text, it will either play "Good morning" as speech or display "Good morning" as text. The translation unit can also translate analyzed sign language and gestures into speech or text. For example, if the sign language expresses "Hello," it will either play "Hello" as speech or display "Hello" as text. This allows for smoother communication with the hearing impaired and the elderly by translating sign language and gestures into speech or text. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input the sign language and gesture data analyzed by the analysis unit into a generating AI, and have the generating AI perform the translation into speech or text.

[0071] The text conversion unit can convert speech into text. For example, if a speaker says "hello," it will be displayed as "hello" in text. The text conversion unit can also convert speech into text. For example, if a speaker says "good morning," it will be displayed as "good morning" in text. By converting speech into text, it becomes possible to communicate in large, easy-to-read font for the elderly. Some or all of the above processing in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input the speech data translated by the translation unit into a generating AI, and have the generating AI perform the conversion to text.

[0072] The calling unit can display text. For example, if the speaker says "Good morning," it will be displayed in large letters as "Good morning." The calling unit can also display text. For example, if the speaker says "Hello," it will be displayed in large letters as "Hello." This makes it easier for elderly people to understand intuitively by displaying text. Some or all of the above processing in the calling unit may be performed using AI, for example, or without AI. For example, the calling unit can input the text data converted by the text conversion unit into a generating AI, and have the generating AI perform the text display.

[0073] The analysis unit can estimate the user's emotions and adjust the accuracy of sign language and gesture analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can increase the analysis accuracy to reduce misrecognition. If the user is relaxed, the analysis unit can maintain normal analysis accuracy and prioritize processing speed. If the user is in a hurry, the analysis unit can prioritize real-time performance even at the expense of slightly lower analysis accuracy. This allows for analysis that prioritizes real-time performance by adjusting the analysis accuracy according to the user's emotions, thereby reducing misrecognition. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user facial expression data acquired by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0074] The analysis unit can optimize its analysis algorithm by referring to the user's past action history when analyzing sign language and gestures. For example, the analysis unit prioritizes analyzing sign language and gestures that the user has frequently used in the past. The analysis unit can also learn specific action patterns from the user's past action history to improve analysis accuracy. Furthermore, the analysis unit can apply individually customized analysis algorithms based on the user's past action history. This improves analysis accuracy and enables individually customized analysis by referring to the user's past action history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past action history data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0075] The analysis unit can perform analysis of sign language and gestures while taking into account the user's physical characteristics (hand size, speed of movement, etc.). For example, the analysis unit can adjust the parameters of the analysis algorithm according to the user's hand size. The analysis unit can also optimize the timing of the analysis according to the user's speed of movement. Furthermore, the analysis unit can pre-register the user's physical characteristics and improve the accuracy of the analysis based on that information. This improves the accuracy of the analysis by considering the user's physical characteristics, enabling more precise analysis. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's physical characteristic data into a generating AI and have the generating AI adjust the analysis algorithm.

[0076] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, visibility is improved and a user-friendly display is made possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0077] The analysis unit can prioritize the analysis of region-specific sign language and gestures by considering the user's geographical location information when analyzing sign language and gestures. For example, if the user is in a specific region, the analysis unit will prioritize the analysis of region-specific sign language and gestures. The analysis unit can also learn region-specific movement patterns based on the user's geographical location information to improve analysis accuracy. Furthermore, if the user moves, the analysis unit can update the geographical location information in real time and adjust the analysis algorithm. This improves analysis accuracy and enables region-appropriate analysis by prioritizing the analysis of region-specific sign language and gestures. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information data into a generating AI and have the generating AI perform the analysis of region-specific sign language and gestures.

[0078] The analysis unit can analyze the user's social media activity when analyzing sign language and gestures, and prioritize the analysis of relevant actions. For example, the analysis unit can prioritize the analysis of sign language and gestures that the user frequently uses on social media. The analysis unit can also learn specific action patterns from the user's social media activity to improve analysis accuracy. Furthermore, the analysis unit can apply individually customized analysis algorithms based on the user's social media activity. This allows for the prioritization of relevant actions and improved analysis accuracy by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI perform the analysis of relevant actions.

[0079] The translation unit can estimate the user's emotions and adjust the translation's expression based on the estimated emotions. For example, if the user is relaxed, the translation unit will use softer language. If the user is tense, the translation unit can use concise and clear language. If the user is excited, the translation unit can use emotionally emphatic language. By adjusting the translation's expression according to the user's emotions, a more appropriate translation is provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI or not. For example, the translation unit can input user emotion data into a generative AI and have the generative AI adjust the translation's expression.

[0080] The translation unit can adjust the level of detail in the translation based on the importance of sign language and gestures during the translation process. For example, the translation unit will provide a detailed translation for important sign language and gestures. It can also provide a concise translation for common sign language and gestures. Furthermore, the translation unit can dynamically adjust the level of detail in the translation depending on the context of the sign language and gestures. This ensures that important information is accurately conveyed by adjusting the level of detail in the translation based on the importance of sign language and gestures. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input importance data for sign language and gestures into a generating AI and have the generating AI perform the adjustment of the level of detail in the translation.

[0081] The translation unit can apply different translation algorithms depending on the category of sign language or gestures during translation. For example, the translation unit can apply a specific translation algorithm to sign language or gestures for greetings. It can also apply a different translation algorithm to sign language or gestures for questions. Furthermore, it can apply a translation algorithm that emphasizes emotion to sign language or gestures that express emotion. This improves translation accuracy by applying translation algorithms according to the category of sign language or gestures. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input category data of sign language and gestures into a generating AI and have the generating AI perform the application of translation algorithms.

[0082] The translation unit can estimate the user's emotions and adjust the translation length based on the estimated emotions. For example, if the user is in a hurry, the translation unit will provide a short, concise translation. If the user is relaxed, the translation unit may provide a longer translation with more detailed explanations. If the user is excited, the translation unit may provide a longer translation that emphasizes emotions. By adjusting the translation length according to the user's emotions, a more appropriate translation is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI or not. For example, the translation unit can input user emotion data into the generative AI and have the generative AI adjust the translation length.

[0083] The translation unit can determine translation priorities based on the timing of sign language and gestures during translation. For example, the translation unit may prioritize translating sign language and gestures related to important events. It can also postpone translating everyday sign language and gestures. Furthermore, the translation unit can dynamically adjust translation priorities according to the timing of sign language and gestures. This ensures that important information is translated preferentially by determining translation priorities based on the timing of sign language and gestures. Some or all of the above processing in the translation unit may be performed using AI, for example, or not. For example, the translation unit can input data on the timing of sign language and gestures into a generating AI and have the generating AI determine the translation priorities.

[0084] The translation unit can adjust the order of translations based on the relevance of sign language and gestures during the translation process. For example, the translation unit will prioritize translating highly relevant sign language and gestures. It can also postpone less relevant sign language and gestures. Furthermore, the translation unit can dynamically adjust the order of translations depending on the context of the sign language and gestures. This ensures that important information is translated preferentially by adjusting the order of translations based on the relevance of sign language and gestures. Some or all of the above processing in the translation unit may be performed using AI, for example, or not. For example, the translation unit can input relevance data of sign language and gestures into a generating AI and have the generating AI perform the adjustment of the translation order.

[0085] The text conversion unit can estimate the user's emotions and adjust the accuracy of speech-to-text conversion based on the estimated emotions. For example, if the user is nervous, the text conversion unit can increase conversion accuracy to reduce misrecognition. If the user is relaxed, the text conversion unit can maintain normal conversion accuracy and prioritize processing speed. If the user is in a hurry, the text conversion unit can prioritize real-time performance even at the expense of slightly lower conversion accuracy. This allows for conversion that reduces misrecognition and prioritizes real-time performance by adjusting conversion accuracy according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the text conversion unit may be performed using AI, or not using AI. For example, the text conversion unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of conversion accuracy.

[0086] The text conversion unit can optimize its conversion algorithm by referring to the user's past speech history when converting speech to text. For example, the text conversion unit can prioritize the conversion of words that the user has frequently used in the past. The text conversion unit can also learn specific speech patterns from the user's past speech history to improve conversion accuracy. Furthermore, the text conversion unit can apply a customized conversion algorithm based on the user's past speech history. This improves conversion accuracy and enables customized conversion by referring to the user's past speech history. Some or all of the above processes in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input the user's past speech history data into a generating AI and have the generating AI perform the optimization of the conversion algorithm.

[0087] The text conversion unit can perform the conversion of speech to text while taking into account the user's speaking speed and accent. For example, the text conversion unit can adjust the parameters of the conversion algorithm according to the user's speaking speed. The text conversion unit can also improve the accuracy of the conversion according to the user's accent. Furthermore, the text conversion unit can pre-register the user's speaking speed and accent and improve the conversion accuracy based on that information. This improves conversion accuracy and enables more precise conversion by taking the user's speaking speed and accent into consideration. Some or all of the above processing in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input the user's speaking speed and accent data into a generating AI and have the generating AI adjust the conversion algorithm.

[0088] The text conversion unit can estimate the user's emotions and adjust the display method of the converted text based on the estimated emotions. For example, if the user is nervous, the text conversion unit can provide a simple and highly legible display method. If the user is relaxed, it can also provide a display method that includes detailed information. If the user is in a hurry, it can also provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, legibility is improved, and a user-friendly display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0089] The text conversion unit can prioritize the conversion of region-specific language by considering the user's geographical location when converting speech to text. For example, if the user is in a specific region, the text conversion unit will prioritize the conversion of region-specific language. The text conversion unit can also learn region-specific speech patterns based on the user's geographical location to improve conversion accuracy. Furthermore, if the user moves, the text conversion unit can update the geographical location information in real time and adjust the conversion algorithm. This improves conversion accuracy by prioritizing the conversion of region-specific language, enabling conversion that is appropriate for the region. Some or all of the above processing in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input the user's geographical location data into a generating AI and have the generating AI perform the conversion of region-specific language.

[0090] The text conversion unit can analyze the user's social media activity and prioritize the conversion of relevant words when converting speech to text. For example, the text conversion unit can prioritize the conversion of words that the user frequently uses on social media. The text conversion unit can also learn specific speech patterns from the user's social media activity to improve conversion accuracy. Furthermore, the text conversion unit can apply individually customized conversion algorithms based on the user's social media activity. This improves conversion accuracy by prioritizing the conversion of relevant words through analysis of the user's social media activity. Some or all of the above processing in the text conversion unit may be performed using AI, for example, or without AI. For example, the text conversion unit can input the user's social media activity data into a generating AI and have the generating AI perform the conversion of relevant words.

[0091] The caller can estimate the user's emotions and adjust the text display method based on the estimated emotions. For example, if the user is tense, the caller can provide text in calm colors. If the user is relaxed, it can also provide text in bright colors. If the user is in a hurry, it can use highly legible fonts and colors for the text display. By adjusting the display method according to the user's emotions, visibility is improved, and a user-friendly display is made possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the caller may be performed using AI, or not using AI. For example, the caller can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0092] The prompting unit can select the optimal display method when displaying text by referring to the user's past display history. For example, the prompting unit may prioritize the use of fonts and colors that the user has previously preferred. The prompting unit can also learn specific display patterns from the user's past display history and provide the optimal display method. Furthermore, the prompting unit can apply individually customized display methods based on the user's past display history. This improves readability by providing the optimal display method by referring to the user's past display history. Some or all of the above processing in the prompting unit may be performed using AI, for example, or without AI. For example, the prompting unit can input the user's past display history data into a generating AI and have the generating AI select the display method.

[0093] The call-to-user function can adjust font size and color when displaying text, taking into account the user's eyesight and readability. For example, the call-to-user function can automatically adjust the font size according to the user's eyesight. It can also use high-contrast colors to improve user readability. Furthermore, the call-to-user function can pre-register the user's eyesight information and optimize the display method based on that information. This improves visibility and makes the display easier for the user by considering the user's eyesight and readability. Some or all of the above processing in the call-to-user function may be performed using AI, for example, or without AI. For example, the call-to-user function can input the user's eyesight information data into a generating AI and have the generating AI perform adjustments to the font size and color.

[0094] The caller can estimate the user's emotions and determine the priority of text display based on the estimated emotions. For example, if the user is nervous, the caller can prioritize displaying important information. If the user is relaxed, the caller can also display text containing detailed information. If the user is in a hurry, the caller can also prioritize displaying concise text. In this way, important information is displayed preferentially by determining the display priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the caller may be performed using AI or not using AI. For example, the caller can input user emotion data into a generative AI and have the generative AI determine the display priority.

[0095] The calling unit can select the optimal display method when displaying text, taking into account the user's device information. For example, if the user is using a smartphone, the calling unit can provide a display method that matches the screen size. It can also provide a display method optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a smartwatch, the calling unit can provide a concise and highly visible display method. This ensures that the optimal display method is provided and visibility is improved by considering the user's device information. Some or all of the above processing in the calling unit may be performed using AI, or without AI. For example, the calling unit can input user device information data into a generating AI and have the generating AI select the display method.

[0096] The call-to-action unit can analyze the user's social media activity when displaying text and prioritize the display of relevant information. For example, the call-to-action unit can prioritize the display of information that the user frequently shows interest in on social media. The call-to-action unit can also learn specific information patterns from the user's social media activity and provide the optimal display method. Furthermore, the call-to-action unit can apply individually customized display methods based on the user's social media activity. This improves visibility by prioritizing the display of relevant information through the analysis of the user's social media activity. Some or all of the above processing in the call-to-action unit may be performed using AI, for example, or without AI. For example, the call-to-action unit can input the user's social media activity data into a generating AI and have the generating AI perform the display of relevant information.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The analysis unit can estimate the user's emotions and adjust the accuracy of sign language and gesture analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can increase the analysis accuracy to reduce misrecognition. If the user is relaxed, the analysis unit can maintain normal analysis accuracy and prioritize processing speed. If the user is in a hurry, the analysis unit can prioritize real-time performance even at the expense of slightly lower analysis accuracy. This allows for analysis that prioritizes real-time performance by adjusting the analysis accuracy according to the user's emotions, thereby reducing misrecognition. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user facial expression data acquired by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0099] The analysis unit can optimize its analysis algorithm by referring to the user's past action history when analyzing sign language and gestures. For example, the analysis unit prioritizes analyzing sign language and gestures that the user has frequently used in the past. The analysis unit can also learn specific action patterns from the user's past action history to improve analysis accuracy. Furthermore, the analysis unit can apply individually customized analysis algorithms based on the user's past action history. This improves analysis accuracy and enables individually customized analysis by referring to the user's past action history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past action history data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0100] The analysis unit can perform analysis of sign language and gestures while taking into account the user's physical characteristics (hand size, speed of movement, etc.). For example, the analysis unit can adjust the parameters of the analysis algorithm according to the user's hand size. The analysis unit can also optimize the timing of the analysis according to the user's speed of movement. Furthermore, the analysis unit can pre-register the user's physical characteristics and improve the accuracy of the analysis based on that information. This improves the accuracy of the analysis by considering the user's physical characteristics, enabling more precise analysis. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's physical characteristic data into a generating AI and have the generating AI adjust the analysis algorithm.

[0101] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, visibility is improved and a user-friendly display is made possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the display method.

[0102] The analysis unit can prioritize the analysis of region-specific sign language and gestures by considering the user's geographical location information when analyzing sign language and gestures. For example, if the user is in a specific region, the analysis unit will prioritize the analysis of region-specific sign language and gestures. The analysis unit can also learn region-specific movement patterns based on the user's geographical location information to improve analysis accuracy. Furthermore, if the user moves, the analysis unit can update the geographical location information in real time and adjust the analysis algorithm. This improves analysis accuracy and enables region-appropriate analysis by prioritizing the analysis of region-specific sign language and gestures. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information data into a generating AI and have the generating AI perform the analysis of region-specific sign language and gestures.

[0103] The translation unit can estimate the user's emotions and adjust the translation's expression based on the estimated emotions. For example, if the user is relaxed, the translation unit will use softer language. If the user is tense, the translation unit can use concise and clear language. If the user is excited, the translation unit can use emotionally emphatic language. By adjusting the translation's expression according to the user's emotions, a more appropriate translation is provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI or not. For example, the translation unit can input user emotion data into a generative AI and have the generative AI adjust the translation's expression.

[0104] The translation unit can adjust the level of detail in the translation based on the importance of sign language and gestures during the translation process. For example, the translation unit will provide a detailed translation for important sign language and gestures. It can also provide a concise translation for common sign language and gestures. Furthermore, the translation unit can dynamically adjust the level of detail in the translation depending on the context of the sign language and gestures. This ensures that important information is accurately conveyed by adjusting the level of detail in the translation based on the importance of sign language and gestures. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input importance data for sign language and gestures into a generating AI and have the generating AI perform the adjustment of the level of detail in the translation.

[0105] The translation unit can apply different translation algorithms depending on the category of sign language or gestures during translation. For example, the translation unit can apply a specific translation algorithm to sign language or gestures for greetings. It can also apply a different translation algorithm to sign language or gestures for questions. Furthermore, it can apply a translation algorithm that emphasizes emotion to sign language or gestures that express emotion. This improves translation accuracy by applying translation algorithms according to the category of sign language or gestures. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input category data of sign language and gestures into a generating AI and have the generating AI perform the application of translation algorithms.

[0106] The translation unit can estimate the user's emotions and adjust the translation length based on the estimated emotions. For example, if the user is in a hurry, the translation unit will provide a short, concise translation. If the user is relaxed, the translation unit may provide a longer translation with more detailed explanations. If the user is excited, the translation unit may provide a longer translation that emphasizes emotions. By adjusting the translation length according to the user's emotions, a more appropriate translation is provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI or not. For example, the translation unit can input user emotion data into the generative AI and have the generative AI adjust the translation length.

[0107] The translation unit can determine translation priorities based on the timing of sign language and gestures during translation. For example, the translation unit may prioritize translating sign language and gestures related to important events. It can also postpone translating everyday sign language and gestures. Furthermore, the translation unit can dynamically adjust translation priorities according to the timing of sign language and gestures. This ensures that important information is translated preferentially by determining translation priorities based on the timing of sign language and gestures. Some or all of the above processing in the translation unit may be performed using AI, for example, or not. For example, the translation unit can input data on the timing of sign language and gestures into a generating AI and have the generating AI determine the translation priorities.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The analysis unit analyzes sign language and gestures in real time via the camera. For example, it captures and analyzes the user's hand movements and gestures via the camera. If the user expresses "good morning" in sign language, the camera captures and analyzes the hand movements. Step 2: The translation unit translates the sign language and gestures analyzed by the analysis unit into speech or text. For example, if the analyzed sign language and gestures are translated into speech or text, then the sign language will either be played as "Good morning" or displayed as text. Step 3: The text conversion unit converts the audio translated by the translation unit into text. For example, if the speaker says "Hello," it will be displayed as "Hello" in text. Step 4: The greeting section displays the text converted by the text conversion section. For example, if the speaker says "Good morning," it will be displayed as "Good morning" in large letters.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the analysis unit, translation unit, text conversion unit, and calling unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit captures sign language or gestures using the camera 42 of the smart device 14 and analyzes them using the control unit 46A. The translation unit translates the analyzed sign language or gestures into speech or text using the specific processing unit 290 of the data processing unit 12. The text conversion unit converts speech into text using the specific processing unit 290 of the data processing unit 12. The calling unit displays the text 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.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the analysis unit, translation unit, text conversion unit, and calling unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit captures sign language or gestures using the camera 42 of the smart glasses 214 and analyzes them by the control unit 46A. The translation unit translates the analyzed sign language or gestures into speech or text by the specific processing unit 290 of the data processing unit 12. The text conversion unit converts speech into text by the specific processing unit 290 of the data processing unit 12. The calling unit displays the text by 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.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0145] Each of the multiple elements described above, including the analysis unit, translation unit, text conversion unit, and calling unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit captures sign language or gestures using the camera 42 of the headset terminal 314 and analyzes them using the control unit 46A. The translation unit translates the analyzed sign language or gestures into speech or text using the specific processing unit 290 of the data processing unit 12. The text conversion unit converts speech into text using the specific processing unit 290 of the data processing unit 12. The calling unit displays the text 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.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[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 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.

[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 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).

[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] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the analysis unit, translation unit, text conversion unit, and calling unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit captures sign language or gestures using the camera 42 of the robot 414 and analyzes them using the control unit 46A. The translation unit translates the analyzed sign language or gestures into speech or text using the specific processing unit 290 of the data processing unit 12. The text conversion unit converts speech into text using the specific processing unit 290 of the data processing unit 12. The calling unit displays the text 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) An analysis unit that analyzes sign language and gestures in real time via a camera, A translation unit that translates the sign language and gestures analyzed by the aforementioned analysis unit into speech and text, A text conversion unit converts the audio translated by the translation unit into text, The system includes a calling unit that displays the text converted by the text conversion unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, The camera captures and analyzes the user's hand movements and gestures. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned translation department, Translate analyzed sign language and gestures into speech and text. The system described in Appendix 1, characterized by the features described herein. (Note 4) The text conversion unit, Convert speech to text The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned calling section is, Display text The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the accuracy of sign language and gesture analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, When analyzing sign language or gestures, the analysis algorithm is optimized by referring to the user's past action history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing sign language and gestures, the analysis takes into account the user's physical characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, 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 10) The aforementioned analysis unit, When analyzing sign language and gestures, the system prioritizes analyzing region-specific sign language and gestures by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing sign language and gestures, the system analyzes the user's social media activity and prioritizes analyzing related actions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned translation department, It estimates the user's emotions and adjusts the translation's expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned translation department, During translation, adjust the level of detail based on the importance of sign language and gestures. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned translation department, During translation, different translation algorithms are applied depending on the category of sign language or gestures. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned translation department, It estimates the user's sentiment and adjusts the translation length based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned translation department, During translation, translation priorities are determined based on the timing of sign language and gestures. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned translation department, During translation, the order of translations is adjusted based on the relevance of sign language and gestures. The system described in Appendix 1, characterized by the features described herein. (Note 18) The text conversion unit, It estimates the user's emotions and adjusts the accuracy of speech-to-text conversion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The text conversion unit, When converting speech to text, the conversion algorithm is optimized by referring to the user's past speech history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The text conversion unit, When converting speech to text, the system takes into account the user's speaking speed and accent. The system described in Appendix 1, characterized by the features described herein. (Note 21) The text conversion unit, It estimates the user's emotions and adjusts how the translated text is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The text conversion unit, When converting speech to text, the system prioritizes region-specific language by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The text conversion unit, When converting speech to text, the system analyzes the user's social media activity and prioritizes relevant words. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned calling section is, It estimates the user's emotions and adjusts the way text is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned calling section is, When displaying text, the system selects the optimal display method by referring to the user's past display history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned calling section is, When displaying text, the font size and color are adjusted to take into account the user's eyesight and readability. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned calling section is, It estimates the user's emotions and determines the priority of text display based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned calling section is, When displaying text, the system selects the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned calling section is, When displaying text, the system analyzes the user's social media activity and prioritizes displaying relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 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. An analysis unit that analyzes sign language and gestures in real time via a camera, A translation unit that translates the sign language and gestures analyzed by the aforementioned analysis unit into speech and text, A text conversion unit converts the audio translated by the translation unit into text, The system includes a calling unit that displays the text converted by the text conversion unit. A system characterized by the following features.

2. The aforementioned analysis unit, The camera captures and analyzes the user's hand movements and gestures. The system according to feature 1.

3. The aforementioned translation department, Translate analyzed sign language and gestures into speech and text. The system according to feature 1.

4. The text conversion unit, Convert speech to text The system according to feature 1.

5. The aforementioned calling section is, Display text The system according to feature 1.

6. The aforementioned analysis unit, The system estimates the user's emotions and adjusts the accuracy of sign language and gesture analysis based on the estimated emotions. The system according to feature 1.

7. The aforementioned analysis unit, When analyzing sign language or gestures, the analysis algorithm is optimized by referring to the user's past action history. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing sign language and gestures, the analysis takes into account the user's physical characteristics. The system according to feature 1.

9. The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system according to feature 1.

10. The aforementioned analysis unit, When analyzing sign language and gestures, the system prioritizes analyzing region-specific sign language and gestures by considering the user's geographical location. The system according to feature 1.

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