System

Abstract

An object of a system according to an embodiment is to efficiently perform conversion between sign language and characters (speech).SOLUTION: A system includes a capture unit, an analysis unit, a generation unit, a display unit, and a conversion unit. The capture unit includes a camera or a sensor for capturing a sign language action. The analysis unit analyzes the action of the sign language captured by the capture unit. The generation unit generates a corresponding character on the basis of the sign language action analyzed by the analysis unit. The display unit displays the character generated by the generation unit. The conversion unit converts the character displayed by the display unit into a sign language action.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not efficiently convert between sign language and text (speech), and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently convert between sign language and text (speech). [Means for solving the problem]

[0006] A system according to an embodiment includes a capture unit, an analysis unit, a generation unit, a display unit, and a conversion unit. The capture unit includes a camera or sensor for capturing sign language actions. The analysis unit analyzes the sign language actions captured by the capture unit. The generation unit generates corresponding characters based on the sign language actions analyzed by the analysis unit. The display unit displays the characters generated by the generation unit. The conversion unit converts the characters displayed by the display unit into sign language actions. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently convert between sign language and text (speech). [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A sign language conversion system according to an embodiment of the present invention is a system that captures sign language gestures, analyzes them, converts them into text, displays them, and converts them back into sign language. The sign language conversion system captures sign language gestures using a camera or sensor, analyzes the gestures using AI, generates corresponding text, and displays it. Furthermore, the system converts the displayed text into sign language gestures. For example, the sign language conversion system captures sign language gestures using a camera, analyzes the gestures using AI, and converts them into text. Furthermore, when text or audio is input into the sign language conversion system, the AI ​​analyzes the content and generates corresponding sign language gestures. This enables bidirectional conversion between sign language and text (speech). This enables smooth communication between people who use sign language and people who use text or speech. For example, when a person who uses sign language converses with someone who uses text, they can convey the content by converting sign language into text. Furthermore, when a person who uses text wants to convey a message to another person who uses sign language, they can convey the content by converting text into sign language. Furthermore, the sign language conversion system is also useful for learning sign language. When learning sign language, users can check the sign actions and their meanings, which allows them to learn more efficiently. For example, by capturing sign actions with a camera and having AI determine whether the actions are correct, users can learn the correct sign actions. In this way, sign language conversion systems are extremely useful tools for people who use sign language, and are extremely helpful in facilitating communication and learning sign language.

[0029] A sign language conversion system according to an embodiment includes a capture unit, an analysis unit, a generation unit, a display unit, and a conversion unit. The capture unit includes a camera or sensor for capturing sign language movements. For example, the capture unit captures the sign language movements using a high-resolution camera. The capture unit can also capture the sign language movements in three dimensions using a depth sensor. The capture unit can simultaneously capture not only the user's hand movements but also their facial expressions and body movements. For example, the capture unit can capture facial expressions simultaneously with the hand movements to more accurately analyze the meaning of the sign language. The analysis unit analyzes the sign language movements captured by the capture unit. The analysis unit can analyze the sign language movements with high accuracy using, for example, deep learning. The analysis unit can also improve the accuracy of the analysis by taking into account the speed and rhythm of the sign language movements. For example, if the sign language movements are fast, the analysis algorithm can be adjusted to match the speed. The generation unit generates corresponding characters based on the sign language movements analyzed by the analysis unit. For example, the generation unit collects data related to sign language from the Internet and builds a sign language database. The generation unit can also estimate the user's emotions and adjust the expression method of the generated characters based on the estimated user's emotions. For example, if the user is nervous, the display unit generates simple, highly visible characters. The display unit displays the characters generated by the generation unit. The display unit determines, for example, whether the sign language actions are correct. The display unit can also estimate the user's emotions and adjust the format of the characters to be displayed based on the estimated user's emotions. For example, if the user is nervous, the display unit provides a simple, highly visible format. The conversion unit converts the characters displayed by the display unit into sign language actions. The conversion unit converts, for example, characters or speech into sign language actions. The conversion unit can also estimate the user's emotions and adjust the method of converting sign language actions based on the estimated user's emotions. For example, if the user is nervous, the conversion is to a simple, highly visible sign language action. As a result, the sign language conversion system according to the embodiment captures, analyzes, converts sign language actions into characters, displays them, and converts them back into sign language, thereby enabling bidirectional conversion between sign language and characters (speech).

[0030] The capture unit can capture sign language movements three-dimensionally using a depth sensor. Depth sensors include, but are not limited to, a ToF (Time of Flight) sensor and a LiDAR (Light Detection and Ranging) sensor, for example. The capture unit can capture sign language movements three-dimensionally using, for example, a ToF sensor. The capture unit can also capture sign language movements three-dimensionally using a LiDAR sensor. The capture unit can also capture sign language movements three-dimensionally using a stereo camera. For example, the capture unit can capture sign language movements three-dimensionally using a stereo camera and transmit the data to the analysis unit. In this way, the use of a depth sensor can more accurately capture sign language movements. Some or all of the above-described processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit can input data acquired by the depth sensor to a generation AI and cause the generation AI to analyze the three-dimensional data.

[0031] The analysis unit can analyze sign language movements with high accuracy using deep learning. Deep learning includes, but is not limited to, for example, a convolutional neural network (CNN) or a recurrent neural network (RNN). The analysis unit can analyze sign language movements with high accuracy using, for example, a CNN. The analysis unit can also analyze sign language movements with high accuracy using an RNN. The analysis unit can also analyze sign language movements with high accuracy using a Transformer model. For example, the analysis unit analyzes sign language movements using a Transformer model and transmits the results to the generation unit. This improves the accuracy of sign language movement analysis using deep learning. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input sign language movement data to a generation AI and cause the generation AI to perform movement analysis.

[0032] The generation unit can collect data related to sign language on the Internet and build a sign language database. The sign language database includes, for example, data related to sign language actions and their meanings, but is not limited to such examples. For example, the generation unit can automatically collect data related to sign language on the Internet and build a sign language database. The generation unit can also periodically update the sign language database and add new sign language actions and meanings. The generation unit can also enable users to customize the sign language database. For example, the generation unit can enable users to add new sign language actions and register their meanings. This improves the accuracy of conversion between sign language actions and characters by building a sign language database. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without AI. For example, the generation unit can input sign language data on the Internet into a generation AI and cause the generation AI to build a database.

[0033] The conversion unit can convert text or speech into sign language actions. For example, the conversion unit converts text into sign language actions. The conversion unit can also convert speech into sign language actions. The conversion unit can also estimate the user's emotions and adjust the sign language action conversion method based on the estimated user's emotions. For example, if the user is nervous, the conversion unit converts the sign language actions into simple, highly visible sign language actions. The conversion unit can also apply conversion algorithms to support different sign languages ​​or dialects. For example, the conversion unit applies conversion algorithms to support different sign languages. The conversion unit can also improve the accuracy of the conversion by referring to the user's past conversion results. For example, the conversion unit can improve the conversion accuracy of similar actions by referring to the user's past conversion results. This enables bidirectional conversion between sign language and text (speech) by converting text or speech into sign language actions. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or without AI. For example, the conversion unit can input text data into a generation AI and have the generation AI convert the text data into sign language actions.

[0034] The display unit can determine whether the sign language actions are correct. The display unit, for example, uses an algorithm to determine whether the sign language actions are correct. The display unit, for example, compares the sign language actions with reference data to determine whether the sign language actions are correct. The display unit can also use AI to determine whether the sign language actions are correct. For example, the display unit can input sign language action data to a generation AI and cause the generation AI to determine the accuracy. The display unit can also estimate the user's emotions and adjust the format of the characters to be displayed based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible format is provided. This helps with learning sign language by determining whether the sign language actions are correct. Some or all of the above-described processing in the display unit can be performed, for example, using AI or without AI.

[0035] When capturing sign language actions, the capture unit can simultaneously capture not only the user's hand movements but also their facial expressions and body movements. For example, the capture unit can capture the user's facial expressions simultaneously with the user's hand movements to more accurately analyze the meaning of the sign language. The capture unit can also capture the user's body movements to check whether the sign language actions are in line with the context. For example, the capture unit can capture the user's body movements to check whether the sign language actions are in line with the context. The capture unit can also simultaneously capture the user's hand movements, facial expressions, and body movements to comprehensively analyze the sign language actions. For example, the capture unit can simultaneously capture hand movements, facial expressions, and body movements and send the data to the analysis unit. This allows for more accurate analysis of the meaning of the sign language by capturing not only hand movements but also facial expressions and body movements. Some or all of the above-described processing in the capture unit may be performed using, for example, AI, or may be performed without AI. For example, the capture unit can input data on hand movements, facial expressions, and body movements into a generation AI and have the generation AI perform a comprehensive analysis.

[0036] The capture unit can apply filtering technology to accommodate different lighting conditions and background conditions when capturing sign language actions. For example, the capture unit applies a filter that suppresses light reflection when capturing in a bright place. The capture unit can also apply a correction filter to clarify sign language actions when capturing in a dark place. For example, the capture unit applies a correction filter to clarify sign language actions when capturing in a dark place. The capture unit can also apply a background removal filter to emphasize sign language actions when the background is complex. For example, the capture unit applies a background removal filter to emphasize sign language actions when the background is complex. This allows for more accurate capture of sign language actions by adapting to different lighting conditions and background conditions. Some or all of the above-described processing in the capture unit may be performed using, or without, AI. For example, the capture unit can input data on lighting conditions and background conditions into the generation AI and have the generation AI apply filtering technology.

[0037] The capture unit can improve the accuracy of capturing sign language actions by referring to the user's past sign language action history. For example, the capture unit can improve the accuracy of capturing similar actions by referring to the user's past sign language action history. The capture unit can also make it easier to recognize specific action patterns based on the user's past sign language action history. For example, the capture unit can make it easier to recognize specific action patterns based on the user's past sign language action history. The capture unit can also reduce erroneous recognition during capture by utilizing the user's past sign language action history. For example, the capture unit can reduce erroneous recognition during capture by utilizing the user's past sign language action history. In this way, referencing the user's past sign language action history improves the accuracy of capture. Some or all of the above-described processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit can input past sign language action history data to a generation AI and cause the generation AI to improve the accuracy of capture.

[0038] When capturing sign language actions, the capture unit can prioritize capturing highly relevant sign language actions by taking into account the user's geographical location information. For example, if the user is in a specific area, the capture unit prioritizes capturing sign language actions commonly used in that area. Furthermore, if the user is traveling, the capture unit can also prioritize capturing sign language actions commonly used in tourist spots. For example, if the user is traveling, the capture unit prioritizes capturing sign language actions commonly used in tourist spots. Furthermore, if the user is participating in a specific event, the capture unit can also prioritize capturing sign language actions related to the event. For example, if the user is participating in a specific event, the capture unit prioritizes capturing sign language actions related to the event. In this way, highly relevant sign language actions can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the capture unit may be performed using AI, for example, or without AI. For example, the capture unit can input geographical location information data to a generation AI and cause the generation AI to prioritize capturing highly relevant sign language actions.

[0039] When capturing sign language actions, the capture unit can analyze the user's social media activities and capture related sign language actions. For example, the capture unit analyzes content posted by the user on social media and captures related sign language actions. The capture unit can also capture related sign language actions by referring to the activities of the user's friends on social media. For example, the capture unit captures related sign language actions by referring to the activities of the user's friends on social media. The capture unit can also capture related sign language actions based on the user's check-in information on social media. For example, the capture unit captures related sign language actions based on the user's check-in information on social media. In this way, related sign language actions can be captured by analyzing the user's social media activities. Some or all of the above-described processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit can input social media activity data to a generation AI and cause the generation AI to capture related sign language actions.

[0040] The capture unit can customize the capture method by reflecting the user's past feedback when capturing sign language actions. The capture unit adjusts the capture method based on, for example, feedback provided by the user in the past. The capture unit can also improve the capture accuracy of specific sign language actions by referring to the user's past feedback. For example, the capture unit improves the capture accuracy of specific sign language actions by referring to the user's past feedback. The capture unit can also reduce misrecognition during capture by reflecting the user's feedback. For example, the capture unit reduces misrecognition during capture by reflecting the user's feedback. In this way, the capture method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit can input past feedback data into a generation AI and cause the generation AI to customize the capture method.

[0041] When analyzing sign language movements, the analysis unit can improve the accuracy of the analysis by taking into account the speed and rhythm of the sign language movements. For example, if the sign language movements are fast, the analysis unit adjusts the analysis algorithm to match the speed. Furthermore, if the sign language movements are slow, the analysis unit can also adjust the analysis algorithm to match the rhythm. For example, if the sign language movements are slow, the analysis unit adjusts the analysis algorithm to match the rhythm. Furthermore, the analysis unit can improve the accuracy of the analysis by comprehensively considering the speed and rhythm of the sign language movements. For example, the analysis unit improves the accuracy of the analysis by comprehensively considering the speed and rhythm of the sign language movements. In this way, the accuracy of the analysis is improved by taking into account the speed and rhythm of the sign language movements. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input sign language movement data to a generation AI and cause the generation AI to perform analysis taking into account the speed and rhythm.

[0042] When analyzing sign language movements, the analysis unit can correct the analysis result by taking into account the context of the sign language movements. For example, the analysis unit checks whether the sign language movements are in line with the context and corrects the analysis result. The analysis unit can also reevaluate and correct the analysis result if the sign language movements do not match the context. For example, the analysis unit reevaluates and corrects the analysis result if the sign language movements do not match the context. The analysis unit can also correct the analysis result by taking into account the context before and after the sign language movements. For example, the analysis unit corrects the analysis result by taking into account the context before and after the sign language movements. In this way, by taking into account the context of the sign language movements, the accuracy of the analysis result is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input sign language movement data to a generation AI and cause the generation AI to correct the analysis result by taking the context into account.

[0043] When analyzing sign language movements, the analysis unit can improve the accuracy of the analysis by referring to the user's past sign language movement analysis results. For example, the analysis unit can improve the analysis accuracy of similar movements by referring to the user's past sign language movement analysis results. The analysis unit can also make it easier to recognize specific movement patterns based on the user's past sign language movement analysis results. For example, the analysis unit can make it easier to recognize specific movement patterns based on the user's past sign language movement analysis results. The analysis unit can also reduce misrecognition during analysis by utilizing the user's past sign language movement analysis results. For example, the analysis unit can reduce misrecognition during analysis by utilizing the user's past sign language movement analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past sign language movement analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past sign language movement analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0044] The analysis unit can apply an analysis algorithm to accommodate different sign languages ​​or dialects when analyzing sign language movements. The analysis unit, for example, applies an analysis algorithm to accommodate different sign languages. The analysis unit can also apply an analysis algorithm to accommodate a specific dialect. For example, the analysis unit applies an analysis algorithm to accommodate a specific dialect. The analysis unit can also adjust the analysis algorithm taking into account differences between sign languages ​​or dialects. For example, the analysis unit adjusts the analysis algorithm taking into account differences between sign languages ​​or dialects. This improves the accuracy of analysis by accommodating different sign languages ​​or dialects. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of a sign language or dialect to a generation AI and cause the generation AI to apply the analysis algorithm.

[0045] When analyzing sign language movements, the analysis unit can improve the accuracy of the analysis by referring to related literature on sign language movements. For example, the analysis unit refers to related literature on sign language movements to improve the accuracy of the analysis. The analysis unit can also refer to related literature to understand the meaning of the sign language movements. For example, the analysis unit refers to related literature to understand the meaning of the sign language movements. The analysis unit can also refer to related literature to understand the context of the sign language movements. For example, the analysis unit refers to related literature to understand the context of the sign language movements. By doing so, the accuracy of the analysis is improved by referring to related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0046] When analyzing sign language movements, the analysis unit can perform the analysis taking into account the cultural background of the sign language movements. For example, the analysis unit improves the accuracy of the analysis by taking into account the cultural background of the sign language movements. Furthermore, if the sign language movements are related to a specific culture, the analysis unit can also perform the analysis taking into account that background. For example, if the sign language movements are related to a specific culture, the analysis unit can perform the analysis taking into account that background. Furthermore, the analysis unit can refer to related information to understand the cultural background of the sign language movements. For example, the analysis unit refers to related information to understand the cultural background of the sign language movements. This improves the accuracy of the analysis by taking the cultural background into account. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input cultural background data into a generation AI and cause the generation AI to perform the analysis.

[0047] When generating characters, the generation unit can adjust the level of detail of the characters to be generated based on the importance of the sign language action. For example, the generation unit generates detailed characters for important sign language actions. The generation unit can also generate concise characters for general sign language actions. For example, the generation unit generates concise characters for general sign language actions. The generation unit can also adjust the level of detail of characters according to the importance of the sign language action. For example, the generation unit adjusts the level of detail of characters according to the importance of the sign language action. In this way, by adjusting the level of detail of characters based on the importance of the sign language action, more appropriate characters can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input sign language action data to the generation AI and cause the generation AI to generate characters based on the importance.

[0048] When generating characters, the generation unit can apply different generation algorithms depending on the category of the sign language action. For example, if the sign language action is a greeting, the generation unit applies a specific generation algorithm. Furthermore, the generation unit can also apply a different generation algorithm if the sign language action is a question. For example, the generation unit applies a different generation algorithm if the sign language action is a question. Furthermore, the generation unit can also apply yet another generation algorithm if the sign language action is a thank you. For example, the generation unit applies yet another generation algorithm if the sign language action is a thank you. In this way, by applying a generation algorithm depending on the category of the sign language action, more appropriate characters can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input sign language action data into the generation AI and cause the generation AI to apply a generation algorithm depending on the category.

[0049] When generating characters, the generation unit can improve the accuracy of generation by referring to the user's past character generation results. For example, the generation unit can improve the accuracy of generating similar actions by referring to the user's past character generation results. The generation unit can also make it easier to recognize specific movement patterns based on the user's past character generation results. For example, the generation unit can make it easier to recognize specific movement patterns based on the user's past character generation results. The generation unit can also reduce misrecognition during generation by utilizing the user's past character generation results. For example, the generation unit can reduce misrecognition during generation by utilizing the user's past character generation results. In this way, by referring to the user's past character generation results, the accuracy of generation is improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past character generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0050] When generating characters, the generation unit can determine the priority of characters to be generated based on the time when the sign language action was submitted. For example, if a sign language action was submitted recently, the generation unit prioritizes generating characters corresponding to that action. Furthermore, if a sign language action was submitted in the past, the generation unit can postpone generating characters corresponding to that action. For example, if a sign language action was submitted in the past, the generation unit postpones generating characters corresponding to that action. Furthermore, the generation unit can adjust the priority of characters to be generated based on the time when the sign language action was submitted. For example, the generation unit adjusts the priority of characters to be generated based on the time when the sign language action was submitted. In this way, more appropriate characters can be generated by determining the priority of characters based on the time when the sign language action was submitted. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input sign language action data to a generation AI and cause the generation AI to generate characters based on the time of submission.

[0051] When generating characters, the generation unit can adjust the order of characters to be generated based on the relevance of sign language actions. For example, if a sign language action is highly relevant, the generation unit prioritizes generating characters corresponding to that action. Furthermore, if a sign language action is low in relevance, the generation unit can postpone generating characters corresponding to that action. For example, if a sign language action is low in relevance, the generation unit postpones generating characters corresponding to that action. Furthermore, the generation unit can adjust the order of characters to be generated according to the relevance of sign language actions. For example, the generation unit adjusts the order of characters to be generated according to the relevance of sign language actions. In this way, by adjusting the order of characters based on the relevance of sign language actions, more appropriate characters can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input sign language action data to a generation AI and cause the generation AI to generate characters based on the relevance.

[0052] When generating characters, the generation unit can adjust the use of technical terminology in the characters to be generated according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates characters that use a lot of technical terminology. Furthermore, if the user does not have technical expertise, the generation unit can also generate characters that use simple language. For example, if the user does not have technical expertise, the generation unit generates characters that use simple language. Furthermore, the generation unit can also adjust the use of technical terminology in the characters to be generated according to the user's level of expertise. For example, the generation unit adjusts the use of technical terminology in the characters to be generated according to the user's level of expertise. This allows for the generation of more appropriate characters by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to generate characters with adjusted use of technical terminology.

[0053] When displaying text, the display unit can select an optimal display method by referring to the user's past display history. For example, the display unit can select a display method with similar content by referring to the user's past display history. The display unit can also make it easier to recognize a specific display pattern based on the user's past display history. For example, the display unit can make it easier to recognize a specific display pattern based on the user's past display history. The display unit can also reduce erroneous recognition during display by utilizing the user's past display history. For example, the display unit can reduce erroneous recognition during display by utilizing the user's past display history. In this way, the optimal display method can be selected by referring to the user's past display history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input past display history data to a generation AI and cause the generation AI to select an optimal display method.

[0054] The display unit can customize the display content according to the user's current task when displaying text. For example, when the user is performing a specific task, the display unit prioritizes displaying information related to that task. Furthermore, when the user is performing multiple tasks, the display unit can also display information related to the most important task. For example, when the user is performing multiple tasks, the display unit displays information related to the most important task. Furthermore, the display unit can customize the display content according to the user's current task. By customizing the display content according to the user's current task, more appropriate information can be provided. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input current task data to a generation AI and have the generation AI customize the display content.

[0055] The display unit can improve the display method by reflecting user feedback when displaying characters. The display unit, for example, adjusts the display method based on feedback provided by the user. The display unit can also make a specific display pattern easier to recognize by referring to the user feedback. For example, the display unit can make a specific display pattern easier to recognize by referring to the user feedback. The display unit can also reduce erroneous recognition during display by reflecting the user feedback. For example, the display unit can reduce erroneous recognition during display by reflecting the user feedback. In this way, the display method can be improved by reflecting the user feedback. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input feedback data to a generation AI and cause the generation AI to improve the display method.

[0056] When displaying text, the display unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. For example, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. For example, if the user is using a smartwatch, the display unit can provide a simple and highly visible display method. This allows the optimal display method to be selected by taking into account the user's device information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input device information data to a generation AI and cause the generation AI to select the optimal display method.

[0057] When displaying text, the display unit can make the display content multilingual according to the user's language setting. The display unit automatically sets the display content based on, for example, the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. For example, the display unit provides a language switching function when the user uses multiple languages. The display unit can also provide display content in a specific language when the user selects that language. For example, the display unit provides display content in that language when the user selects a specific language. This makes it possible to provide more appropriate information by making the display content multilingual according to the user's language setting. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input language setting data to a generation AI and cause the generation AI to execute multilingual display content.

[0058] The display unit can customize the display format according to the user's visual preferences when displaying text. The display unit customizes the display format using, for example, a font or color preferred by the user. The display unit can also customize the display format using a layout preferred by the user. For example, the display unit customizes the display format using a layout preferred by the user. The display unit can also customize the display format according to the user's visual preferences. For example, the display unit customizes the display format according to the user's visual preferences. This allows more appropriate information to be provided by customizing the display format according to the user's visual preferences. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input visual preference data to a generation AI and cause the generation AI to customize the display format.

[0059] The conversion unit can apply a conversion algorithm to accommodate different sign languages ​​or dialects when converting sign language actions. The conversion unit, for example, applies a conversion algorithm to accommodate different sign languages. The conversion unit can also apply a conversion algorithm to accommodate a specific dialect. For example, the conversion unit applies a conversion algorithm to accommodate a specific dialect. The conversion unit can also adjust the conversion algorithm taking into account differences between sign languages ​​or dialects. For example, the conversion unit adjusts the conversion algorithm taking into account differences between sign languages ​​or dialects. This improves the accuracy of conversion by accommodating different sign languages ​​or dialects. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input data of a sign language or dialect to a generation AI and cause the generation AI to apply the conversion algorithm.

[0060] The conversion unit can improve the accuracy of conversion by referring to the user's past conversion results when converting sign language movements. For example, the conversion unit can improve the conversion accuracy of similar movements by referring to the user's past conversion results. The conversion unit can also make it easier to recognize specific movement patterns based on the user's past conversion results. For example, the conversion unit can make it easier to recognize specific movement patterns based on the user's past conversion results. The conversion unit can also reduce misrecognition during conversion by utilizing the user's past conversion results. For example, the conversion unit can reduce misrecognition during conversion by utilizing the user's past conversion results. In this way, the accuracy of conversion is improved by referring to the user's past conversion results. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input past conversion result data into a generation AI and cause the generation AI to improve the accuracy of conversion.

[0061] When converting sign language actions, the conversion unit can prioritize converting highly relevant sign language actions by taking into account the user's geographical location information. For example, if the user is in a specific area, the conversion unit can prioritize converting sign language actions commonly used in that area. Furthermore, if the user is traveling, the conversion unit can also prioritize converting sign language actions commonly used in tourist spots. For example, if the user is traveling, the conversion unit can prioritize converting sign language actions commonly used in tourist spots. Furthermore, if the user is participating in a specific event, the conversion unit can also prioritize converting sign language actions related to the event. For example, if the user is participating in a specific event, the conversion unit can prioritize converting sign language actions related to the event. In this way, highly relevant sign language actions can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the conversion unit may be performed using AI, for example, or may be performed without using AI. For example, the conversion unit can input geographical location information data to a generation AI and cause the generation AI to perform preferential conversion of highly relevant sign language actions.

[0062] When converting sign language actions, the conversion unit can analyze the user's social media activities and convert the related sign language actions. For example, the conversion unit analyzes content posted by the user on social media and converts the related sign language actions. The conversion unit can also convert the related sign language actions by referring to the activities of the user's friends on social media. For example, the conversion unit converts the related sign language actions by referring to the activities of the user's friends on social media. The conversion unit can also convert the related sign language actions based on the user's check-in information on social media. For example, the conversion unit converts the related sign language actions based on the user's check-in information on social media. In this way, the related sign language actions can be converted by analyzing the user's social media activities. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input social media activity data to a generation AI and cause the generation AI to convert the related sign language actions.

[0063] The conversion unit can customize the conversion method by reflecting the user's past feedback when converting sign language actions. The conversion unit adjusts the conversion method based on, for example, feedback provided by the user in the past. The conversion unit can also improve the conversion accuracy of specific sign language actions by referring to the user's past feedback. For example, the conversion unit improves the conversion accuracy of specific sign language actions by referring to the user's past feedback. The conversion unit can also reflect the user's feedback to reduce misrecognition during conversion. For example, the conversion unit reflects the user's feedback to reduce misrecognition during conversion. In this way, the conversion method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input past feedback data into a generation AI and cause the generation AI to customize the conversion method.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The capture unit can simultaneously capture not only the user's hand movements but also their facial expressions and body movements. For example, the capture unit can capture facial expressions simultaneously with hand movements to more accurately analyze the meaning of sign language. The capture unit can also capture the user's body movements to check whether the sign language actions are in line with the context. This allows for more accurate analysis of the meaning of sign language by capturing not only hand movements but also facial expressions and body movements.

[0066] When analyzing sign language movements, the analysis unit can improve the accuracy of the analysis by taking into account the speed and rhythm of the sign language movements. For example, if the sign language movements are fast, the analysis algorithm can be adjusted to match that speed. Also, if the sign language movements are slow, the analysis algorithm can be adjusted to match that rhythm. In this way, by taking into account the speed and rhythm of the sign language movements, the accuracy of the analysis can be improved.

[0067] When generating characters, the generation unit can adjust the level of detail of the characters to be generated based on the importance of the sign language action. For example, detailed characters can be generated for important sign language actions. Also, simple characters can be generated for general sign language actions. In this way, by adjusting the level of detail of characters based on the importance of the sign language action, more appropriate characters can be generated.

[0068] The conversion unit can apply a conversion algorithm to support different sign languages ​​or dialects when converting sign language actions. For example, a conversion algorithm to support different sign languages ​​can be applied. Also, a conversion algorithm to support a specific dialect can be applied. This improves the accuracy of conversion by supporting different sign languages ​​and dialects.

[0069] When analyzing sign language movements, the analysis unit can correct the analysis results by taking into account the context of the sign language movements. For example, it can check whether the sign language movements are in line with the context and correct the analysis results. It can also reevaluate and correct the analysis results if the sign language movements do not match the context. In this way, the accuracy of the analysis results is improved by taking into account the context of the sign language movements.

[0070] When displaying text, the display unit can select the optimal display method by referring to the user's past display history. For example, the display unit can select a display method for similar content by referring to the user's past display history. It can also make it easier to recognize specific display patterns based on the user's past display history. This allows the optimal display method to be selected by referring to the user's past display history.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The capture unit includes a camera or sensor for capturing sign language actions. For example, the capture unit captures sign language actions with a high-resolution camera. The capture unit can also capture sign language actions in three dimensions using a depth sensor. The capture unit can also simultaneously capture not only the user's hand movements but also their facial expressions and body movements. For example, the capture unit can capture facial expressions simultaneously with hand movements to more accurately analyze the meaning of the sign language. Step 2: The analysis unit analyzes the sign language movements captured by the capture unit. The analysis unit analyzes the sign language movements with high accuracy, for example, using deep learning. The analysis unit can also improve the accuracy of the analysis by taking into account the speed and rhythm of the sign language movements. For example, if the sign language movements are fast, the analysis algorithm is adjusted to match that speed. Step 3: The generation unit generates corresponding characters based on the sign language actions analyzed by the analysis unit. The generation unit, for example, collects data on sign language from the Internet and builds a sign language database. The generation unit can also estimate the user's emotions and adjust the expression method of the generated characters based on the estimated user emotions. For example, if the user is nervous, the generation unit generates simple, highly visible characters. Step 4: The display unit displays the characters generated by the generation unit. The display unit, for example, determines whether the sign language movements are correct. The display unit can also estimate the user's emotions and adjust the format of the characters to be displayed based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible format is provided. Step 5: The conversion unit converts the characters displayed by the display unit into sign language actions. The conversion unit converts, for example, characters or voice into sign language actions. The conversion unit can also estimate the user's emotions and adjust the method of converting sign language actions based on the estimated user's emotions. For example, if the user is nervous, the conversion unit converts into simple, highly visible sign language actions.

[0073] (Example 2) A sign language conversion system according to an embodiment of the present invention is a system that captures sign language gestures, analyzes them, converts them into text, displays them, and converts them back into sign language. The sign language conversion system captures sign language gestures using a camera or sensor, analyzes the gestures using AI, generates corresponding text, and displays it. Furthermore, the system converts the displayed text into sign language gestures. For example, the sign language conversion system captures sign language gestures using a camera, analyzes the gestures using AI, and converts them into text. Furthermore, when text or audio is input into the sign language conversion system, the AI ​​analyzes the content and generates corresponding sign language gestures. This enables bidirectional conversion between sign language and text (speech). This enables smooth communication between people who use sign language and people who use text or speech. For example, when a person who uses sign language converses with someone who uses text, they can convey the content by converting sign language into text. Furthermore, when a person who uses text wants to convey a message to another person who uses sign language, they can convey the content by converting text into sign language. Furthermore, the sign language conversion system is also useful for learning sign language. When learning sign language, users can check the sign actions and their meanings, which allows them to learn more efficiently. For example, by capturing sign actions with a camera and having AI determine whether the actions are correct, users can learn the correct sign actions. In this way, sign language conversion systems are extremely useful tools for people who use sign language, and are extremely helpful in facilitating communication and learning sign language.

[0074] A sign language conversion system according to an embodiment includes a capture unit, an analysis unit, a generation unit, a display unit, and a conversion unit. The capture unit includes a camera or sensor for capturing sign language movements. For example, the capture unit captures the sign language movements using a high-resolution camera. The capture unit can also capture the sign language movements in three dimensions using a depth sensor. The capture unit can simultaneously capture not only the user's hand movements but also their facial expressions and body movements. For example, the capture unit can capture facial expressions simultaneously with the hand movements to more accurately analyze the meaning of the sign language. The analysis unit analyzes the sign language movements captured by the capture unit. The analysis unit can analyze the sign language movements with high accuracy using, for example, deep learning. The analysis unit can also improve the accuracy of the analysis by taking into account the speed and rhythm of the sign language movements. For example, if the sign language movements are fast, the analysis algorithm can be adjusted to match the speed. The generation unit generates corresponding characters based on the sign language movements analyzed by the analysis unit. For example, the generation unit collects data related to sign language from the Internet and builds a sign language database. The generation unit can also estimate the user's emotions and adjust the expression method of the generated characters based on the estimated user's emotions. For example, if the user is nervous, the display unit generates simple, highly visible characters. The display unit displays the characters generated by the generation unit. The display unit determines, for example, whether the sign language actions are correct. The display unit can also estimate the user's emotions and adjust the format of the characters to be displayed based on the estimated user's emotions. For example, if the user is nervous, the display unit provides a simple, highly visible format. The conversion unit converts the characters displayed by the display unit into sign language actions. The conversion unit converts, for example, characters or speech into sign language actions. The conversion unit can also estimate the user's emotions and adjust the method of converting sign language actions based on the estimated user's emotions. For example, if the user is nervous, the conversion is to a simple, highly visible sign language action. As a result, the sign language conversion system according to the embodiment captures, analyzes, converts sign language actions into characters, displays them, and converts them back into sign language, thereby enabling bidirectional conversion between sign language and characters (speech).

[0075] The capture unit can capture sign language movements three-dimensionally using a depth sensor. Depth sensors include, but are not limited to, a ToF (Time of Flight) sensor and a LiDAR (Light Detection and Ranging) sensor, for example. The capture unit can capture sign language movements three-dimensionally using, for example, a ToF sensor. The capture unit can also capture sign language movements three-dimensionally using a LiDAR sensor. The capture unit can also capture sign language movements three-dimensionally using a stereo camera. For example, the capture unit can capture sign language movements three-dimensionally using a stereo camera and transmit the data to the analysis unit. In this way, the use of a depth sensor can more accurately capture sign language movements. Some or all of the above-described processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit can input data acquired by the depth sensor to a generation AI and cause the generation AI to analyze the three-dimensional data.

[0076] The analysis unit can analyze sign language movements with high accuracy using deep learning. Deep learning includes, but is not limited to, for example, a convolutional neural network (CNN) or a recurrent neural network (RNN). The analysis unit can analyze sign language movements with high accuracy using, for example, a CNN. The analysis unit can also analyze sign language movements with high accuracy using an RNN. The analysis unit can also analyze sign language movements with high accuracy using a Transformer model. For example, the analysis unit analyzes sign language movements using a Transformer model and transmits the results to the generation unit. This improves the accuracy of sign language movement analysis using deep learning. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input sign language movement data to a generation AI and cause the generation AI to perform movement analysis.

[0077] The generation unit can collect data related to sign language on the Internet and build a sign language database. The sign language database includes, for example, data related to sign language actions and their meanings, but is not limited to such examples. For example, the generation unit can automatically collect data related to sign language on the Internet and build a sign language database. The generation unit can also periodically update the sign language database and add new sign language actions and meanings. The generation unit can also enable users to customize the sign language database. For example, the generation unit can enable users to add new sign language actions and register their meanings. This improves the accuracy of conversion between sign language actions and characters by building a sign language database. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without AI. For example, the generation unit can input sign language data on the Internet into a generation AI and cause the generation AI to build a database.

[0078] The conversion unit can convert text or speech into sign language actions. For example, the conversion unit converts text into sign language actions. The conversion unit can also convert speech into sign language actions. The conversion unit can also estimate the user's emotions and adjust the sign language action conversion method based on the estimated user's emotions. For example, if the user is nervous, the conversion unit converts the sign language actions into simple, highly visible sign language actions. The conversion unit can also apply conversion algorithms to support different sign languages ​​or dialects. For example, the conversion unit applies conversion algorithms to support different sign languages. The conversion unit can also improve the accuracy of the conversion by referring to the user's past conversion results. For example, the conversion unit can improve the conversion accuracy of similar actions by referring to the user's past conversion results. This enables bidirectional conversion between sign language and text (speech) by converting text or speech into sign language actions. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or without AI. For example, the conversion unit can input text data into a generation AI and have the generation AI convert the text data into sign language actions.

[0079] The display unit can determine whether the sign language actions are correct. The display unit, for example, uses an algorithm to determine whether the sign language actions are correct. The display unit, for example, compares the sign language actions with reference data to determine whether the sign language actions are correct. The display unit can also use AI to determine whether the sign language actions are correct. For example, the display unit can input sign language action data to a generation AI and cause the generation AI to determine the accuracy. The display unit can also estimate the user's emotions and adjust the format of the characters to be displayed based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible format is provided. This helps with learning sign language by determining whether the sign language actions are correct. Some or all of the above-described processing in the display unit can be performed, for example, using AI or without AI.

[0080] The capture unit can estimate the user's emotion and adjust the timing of capturing sign language actions based on the estimated user emotion. The capture unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the capture unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The capture unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the capture unit analyzes the tone and speed of the voice and calculates an emotion score. The capture unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the capture unit calculates an emotion score based on heart rate fluctuations. This allows the timing of capture to be adjusted according to the user's emotion, thereby enabling more appropriate sign language actions to be captured. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit may input facial expression data of a user captured by a camera into the generation AI, and have the generation AI estimate the emotion.

[0081] When capturing sign language actions, the capture unit can simultaneously capture not only the user's hand movements but also their facial expressions and body movements. For example, the capture unit can capture the user's facial expressions simultaneously with the user's hand movements to more accurately analyze the meaning of the sign language. The capture unit can also capture the user's body movements to check whether the sign language actions are in line with the context. For example, the capture unit can capture the user's body movements to check whether the sign language actions are in line with the context. The capture unit can also simultaneously capture the user's hand movements, facial expressions, and body movements to comprehensively analyze the sign language actions. For example, the capture unit can simultaneously capture hand movements, facial expressions, and body movements and send the data to the analysis unit. This allows for more accurate analysis of the meaning of the sign language by capturing not only hand movements but also facial expressions and body movements. Some or all of the above-described processing in the capture unit may be performed using, for example, AI, or may be performed without AI. For example, the capture unit can input data on hand movements, facial expressions, and body movements into a generation AI and have the generation AI perform a comprehensive analysis.

[0082] The capture unit can apply filtering technology to accommodate different lighting conditions and background conditions when capturing sign language actions. For example, the capture unit applies a filter that suppresses light reflection when capturing in a bright place. The capture unit can also apply a correction filter to clarify sign language actions when capturing in a dark place. For example, the capture unit applies a correction filter to clarify sign language actions when capturing in a dark place. The capture unit can also apply a background removal filter to emphasize sign language actions when the background is complex. For example, the capture unit applies a background removal filter to emphasize sign language actions when the background is complex. This allows for more accurate capture of sign language actions by adapting to different lighting conditions and background conditions. Some or all of the above-described processing in the capture unit may be performed using, or without, AI. For example, the capture unit can input data on lighting conditions and background conditions into the generation AI and have the generation AI apply filtering technology.

[0083] The capture unit can improve the accuracy of capturing sign language actions by referring to the user's past sign language action history. For example, the capture unit can improve the accuracy of capturing similar actions by referring to the user's past sign language action history. The capture unit can also make it easier to recognize specific action patterns based on the user's past sign language action history. For example, the capture unit can make it easier to recognize specific action patterns based on the user's past sign language action history. The capture unit can also reduce erroneous recognition during capture by utilizing the user's past sign language action history. For example, the capture unit can reduce erroneous recognition during capture by utilizing the user's past sign language action history. In this way, referencing the user's past sign language action history improves the accuracy of capture. Some or all of the above-described processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit can input past sign language action history data to a generation AI and cause the generation AI to improve the accuracy of capture.

[0084] The capture unit can estimate the user's emotions and prioritize the sign language actions to be captured based on the estimated user emotions. The capture unit, for example, uses facial expression recognition technology to estimate the user's emotions. For example, the capture unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. The capture unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the capture unit can analyze the tone and speed of the voice and calculate an emotion score. The capture unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the capture unit can calculate an emotion score based on heart rate fluctuations. This allows the prioritization of sign language actions according to the user's emotions, thereby enabling more appropriate sign language actions to be captured. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit may input facial expression data of a user captured by a camera into the generation AI, and have the generation AI estimate the emotion.

[0085] When capturing sign language actions, the capture unit can prioritize capturing highly relevant sign language actions by taking into account the user's geographical location information. For example, if the user is in a specific area, the capture unit prioritizes capturing sign language actions commonly used in that area. Furthermore, if the user is traveling, the capture unit can also prioritize capturing sign language actions commonly used in tourist spots. For example, if the user is traveling, the capture unit prioritizes capturing sign language actions commonly used in tourist spots. Furthermore, if the user is participating in a specific event, the capture unit can also prioritize capturing sign language actions related to the event. For example, if the user is participating in a specific event, the capture unit prioritizes capturing sign language actions related to the event. In this way, highly relevant sign language actions can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the capture unit may be performed using AI, for example, or without AI. For example, the capture unit can input geographical location information data to a generation AI and cause the generation AI to prioritize capturing highly relevant sign language actions.

[0086] When capturing sign language actions, the capture unit can analyze the user's social media activities and capture related sign language actions. For example, the capture unit analyzes content posted by the user on social media and captures related sign language actions. The capture unit can also capture related sign language actions by referring to the activities of the user's friends on social media. For example, the capture unit captures related sign language actions by referring to the activities of the user's friends on social media. The capture unit can also capture related sign language actions based on the user's check-in information on social media. For example, the capture unit captures related sign language actions based on the user's check-in information on social media. In this way, related sign language actions can be captured by analyzing the user's social media activities. Some or all of the above-described processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit can input social media activity data to a generation AI and cause the generation AI to capture related sign language actions.

[0087] The capture unit can customize the capture method by reflecting the user's past feedback when capturing sign language actions. The capture unit adjusts the capture method based on, for example, feedback provided by the user in the past. The capture unit can also improve the capture accuracy of specific sign language actions by referring to the user's past feedback. For example, the capture unit improves the capture accuracy of specific sign language actions by referring to the user's past feedback. The capture unit can also reduce misrecognition during capture by reflecting the user's feedback. For example, the capture unit reduces misrecognition during capture by reflecting the user's feedback. In this way, the capture method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the capture unit may be performed using, for example, AI, or may be performed without using AI. For example, the capture unit can input past feedback data into a generation AI and cause the generation AI to customize the capture method.

[0088] The analysis unit can estimate the user's emotion and adjust the sign language movement analysis algorithm based on the estimated user's emotion. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on heart rate fluctuations. This improves analysis accuracy by adjusting the analysis algorithm according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input facial expression data of a user acquired by a camera into the generation AI and cause the generation AI to estimate emotions.

[0089] When analyzing sign language movements, the analysis unit can improve the accuracy of the analysis by taking into account the speed and rhythm of the sign language movements. For example, if the sign language movements are fast, the analysis unit adjusts the analysis algorithm to match the speed. Furthermore, if the sign language movements are slow, the analysis unit can also adjust the analysis algorithm to match the rhythm. For example, if the sign language movements are slow, the analysis unit adjusts the analysis algorithm to match the rhythm. Furthermore, the analysis unit can improve the accuracy of the analysis by comprehensively considering the speed and rhythm of the sign language movements. For example, the analysis unit improves the accuracy of the analysis by comprehensively considering the speed and rhythm of the sign language movements. In this way, the accuracy of the analysis is improved by taking into account the speed and rhythm of the sign language movements. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input sign language movement data to a generation AI and cause the generation AI to perform analysis taking into account the speed and rhythm.

[0090] When analyzing sign language movements, the analysis unit can correct the analysis result by taking into account the context of the sign language movements. For example, the analysis unit checks whether the sign language movements are in line with the context and corrects the analysis result. The analysis unit can also reevaluate and correct the analysis result if the sign language movements do not match the context. For example, the analysis unit reevaluates and corrects the analysis result if the sign language movements do not match the context. The analysis unit can also correct the analysis result by taking into account the context before and after the sign language movements. For example, the analysis unit corrects the analysis result by taking into account the context before and after the sign language movements. In this way, by taking into account the context of the sign language movements, the accuracy of the analysis result is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input sign language movement data to a generation AI and cause the generation AI to correct the analysis result by taking the context into account.

[0091] When analyzing sign language movements, the analysis unit can improve the accuracy of the analysis by referring to the user's past sign language movement analysis results. For example, the analysis unit can improve the analysis accuracy of similar movements by referring to the user's past sign language movement analysis results. The analysis unit can also make it easier to recognize specific movement patterns based on the user's past sign language movement analysis results. For example, the analysis unit can make it easier to recognize specific movement patterns based on the user's past sign language movement analysis results. The analysis unit can also reduce misrecognition during analysis by utilizing the user's past sign language movement analysis results. For example, the analysis unit can reduce misrecognition during analysis by utilizing the user's past sign language movement analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past sign language movement analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past sign language movement analysis result data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0092] The analysis unit can estimate the user's emotion and adjust the display method of the analysis results based on the estimated user's emotion. The analysis unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the voice and calculate an emotion score. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit can calculate an emotion score based on heart rate fluctuations. This enables a more appropriate display by adjusting the display method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input facial expression data of a user acquired by a camera into the generation AI and cause the generation AI to estimate emotions.

[0093] The analysis unit can apply an analysis algorithm to accommodate different sign languages ​​or dialects when analyzing sign language movements. The analysis unit, for example, applies an analysis algorithm to accommodate different sign languages. The analysis unit can also apply an analysis algorithm to accommodate a specific dialect. For example, the analysis unit applies an analysis algorithm to accommodate a specific dialect. The analysis unit can also adjust the analysis algorithm taking into account differences between sign languages ​​or dialects. For example, the analysis unit adjusts the analysis algorithm taking into account differences between sign languages ​​or dialects. This improves the accuracy of analysis by accommodating different sign languages ​​or dialects. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of a sign language or dialect to a generation AI and cause the generation AI to apply the analysis algorithm.

[0094] When analyzing sign language movements, the analysis unit can improve the accuracy of the analysis by referring to related literature on sign language movements. For example, the analysis unit refers to related literature on sign language movements to improve the accuracy of the analysis. The analysis unit can also refer to related literature to understand the meaning of the sign language movements. For example, the analysis unit refers to related literature to understand the meaning of the sign language movements. The analysis unit can also refer to related literature to understand the context of the sign language movements. For example, the analysis unit refers to related literature to understand the context of the sign language movements. By doing so, the accuracy of the analysis is improved by referring to related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0095] When analyzing sign language movements, the analysis unit can perform the analysis taking into account the cultural background of the sign language movements. For example, the analysis unit improves the accuracy of the analysis by taking into account the cultural background of the sign language movements. Furthermore, if the sign language movements are related to a specific culture, the analysis unit can also perform the analysis taking into account that background. For example, if the sign language movements are related to a specific culture, the analysis unit can perform the analysis taking into account that background. Furthermore, the analysis unit can refer to related information to understand the cultural background of the sign language movements. For example, the analysis unit refers to related information to understand the cultural background of the sign language movements. This improves the accuracy of the analysis by taking the cultural background into account. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input cultural background data into a generation AI and cause the generation AI to perform the analysis.

[0096] The generation unit can estimate the user's emotion and adjust the expression method of the generated characters based on the estimated user's emotion. The generation unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice and calculate an emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on heart rate fluctuations. This allows the generation of more appropriate characters by adjusting the expression method of the characters according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input facial expression data of a user acquired by a camera into the generation AI and cause the generation AI to estimate emotions.

[0097] When generating characters, the generation unit can adjust the level of detail of the characters to be generated based on the importance of the sign language action. For example, the generation unit generates detailed characters for important sign language actions. The generation unit can also generate concise characters for general sign language actions. For example, the generation unit generates concise characters for general sign language actions. The generation unit can also adjust the level of detail of characters according to the importance of the sign language action. For example, the generation unit adjusts the level of detail of characters according to the importance of the sign language action. In this way, by adjusting the level of detail of characters based on the importance of the sign language action, more appropriate characters can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input sign language action data to the generation AI and cause the generation AI to generate characters based on the importance.

[0098] When generating characters, the generation unit can apply different generation algorithms depending on the category of the sign language action. For example, if the sign language action is a greeting, the generation unit applies a specific generation algorithm. Furthermore, the generation unit can also apply a different generation algorithm if the sign language action is a question. For example, the generation unit applies a different generation algorithm if the sign language action is a question. Furthermore, the generation unit can also apply yet another generation algorithm if the sign language action is a thank you. For example, the generation unit applies yet another generation algorithm if the sign language action is a thank you. In this way, by applying a generation algorithm depending on the category of the sign language action, more appropriate characters can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input sign language action data into the generation AI and cause the generation AI to apply a generation algorithm depending on the category.

[0099] When generating characters, the generation unit can improve the accuracy of generation by referring to the user's past character generation results. For example, the generation unit can improve the accuracy of generating similar actions by referring to the user's past character generation results. The generation unit can also make it easier to recognize specific movement patterns based on the user's past character generation results. For example, the generation unit can make it easier to recognize specific movement patterns based on the user's past character generation results. The generation unit can also reduce misrecognition during generation by utilizing the user's past character generation results. For example, the generation unit can reduce misrecognition during generation by utilizing the user's past character generation results. In this way, by referring to the user's past character generation results, the accuracy of generation is improved. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input past character generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0100] The generation unit can estimate the user's emotion and adjust the length of the generated text based on the estimated user's emotion. The generation unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit can analyze the tone and speed of the voice and calculate an emotion score. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit can calculate an emotion score based on heart rate fluctuations. This allows the generation of more appropriate text by adjusting the length of the text according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input facial expression data of a user acquired by a camera into the generation AI and cause the generation AI to estimate emotions.

[0101] When generating characters, the generation unit can determine the priority of characters to be generated based on the time when the sign language action was submitted. For example, if a sign language action was submitted recently, the generation unit prioritizes generating characters corresponding to that action. Furthermore, if a sign language action was submitted in the past, the generation unit can postpone generating characters corresponding to that action. For example, if a sign language action was submitted in the past, the generation unit postpones generating characters corresponding to that action. Furthermore, the generation unit can adjust the priority of characters to be generated based on the time when the sign language action was submitted. For example, the generation unit adjusts the priority of characters to be generated based on the time when the sign language action was submitted. In this way, more appropriate characters can be generated by determining the priority of characters based on the time when the sign language action was submitted. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input sign language action data to a generation AI and cause the generation AI to generate characters based on the time of submission.

[0102] When generating characters, the generation unit can adjust the order of characters to be generated based on the relevance of sign language actions. For example, if a sign language action is highly relevant, the generation unit prioritizes generating characters corresponding to that action. Furthermore, if a sign language action is low in relevance, the generation unit can postpone generating characters corresponding to that action. For example, if a sign language action is low in relevance, the generation unit postpones generating characters corresponding to that action. Furthermore, the generation unit can adjust the order of characters to be generated according to the relevance of sign language actions. For example, the generation unit adjusts the order of characters to be generated according to the relevance of sign language actions. In this way, by adjusting the order of characters based on the relevance of sign language actions, more appropriate characters can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input sign language action data to a generation AI and cause the generation AI to generate characters based on the relevance.

[0103] When generating characters, the generation unit can adjust the use of technical terminology in the characters to be generated according to the user's level of expertise. For example, if the user has technical expertise, the generation unit generates characters that use a lot of technical terminology. Furthermore, if the user does not have technical expertise, the generation unit can also generate characters that use simple language. For example, if the user does not have technical expertise, the generation unit generates characters that use simple language. Furthermore, the generation unit can also adjust the use of technical terminology in the characters to be generated according to the user's level of expertise. For example, the generation unit adjusts the use of technical terminology in the characters to be generated according to the user's level of expertise. This allows for the generation of more appropriate characters by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's level of expertise data into the generation AI and cause the generation AI to generate characters with adjusted use of technical terminology.

[0104] The display unit can estimate the user's emotion and adjust the format of the characters to be displayed based on the estimated user's emotion. The display unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the display unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The display unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the display unit can analyze the tone and speed of the voice and calculate an emotion score. The display unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the display unit can calculate an emotion score based on heart rate fluctuations. This enables a more appropriate display by adjusting the display format according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may input facial expression data of the user acquired by a camera into the generation AI and cause the generation AI to estimate emotions.

[0105] When displaying text, the display unit can select an optimal display method by referring to the user's past display history. For example, the display unit can select a display method with similar content by referring to the user's past display history. The display unit can also make it easier to recognize a specific display pattern based on the user's past display history. For example, the display unit can make it easier to recognize a specific display pattern based on the user's past display history. The display unit can also reduce erroneous recognition during display by utilizing the user's past display history. For example, the display unit can reduce erroneous recognition during display by utilizing the user's past display history. In this way, the optimal display method can be selected by referring to the user's past display history. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input past display history data to a generation AI and cause the generation AI to select an optimal display method.

[0106] The display unit can customize the display content according to the user's current task when displaying text. For example, when the user is performing a specific task, the display unit prioritizes displaying information related to that task. Furthermore, when the user is performing multiple tasks, the display unit can also display information related to the most important task. For example, when the user is performing multiple tasks, the display unit displays information related to the most important task. Furthermore, the display unit can customize the display content according to the user's current task. By customizing the display content according to the user's current task, more appropriate information can be provided. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit can input current task data to a generation AI and have the generation AI customize the display content.

[0107] The display unit can improve the display method by reflecting user feedback when displaying characters. The display unit, for example, adjusts the display method based on feedback provided by the user. The display unit can also make a specific display pattern easier to recognize by referring to the user feedback. For example, the display unit can make a specific display pattern easier to recognize by referring to the user feedback. The display unit can also reduce erroneous recognition during display by reflecting the user feedback. For example, the display unit can reduce erroneous recognition during display by reflecting the user feedback. In this way, the display method can be improved by reflecting the user feedback. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input feedback data to a generation AI and cause the generation AI to improve the display method.

[0108] The display unit can estimate the user's emotion and determine the priority of characters to display based on the estimated user's emotion. The display unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the display unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The display unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the display unit can analyze the tone and speed of the voice and calculate an emotion score. The display unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the display unit can calculate an emotion score based on heart rate fluctuations. This allows the display unit to determine the priority of characters according to the user's emotion, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generative AI. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit may input facial expression data of the user acquired by a camera into the generation AI and cause the generation AI to estimate emotions.

[0109] When displaying text, the display unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the display unit can also provide a display method optimized for a large screen. For example, if the user is using a tablet, the display unit can provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the display unit can also provide a simple and highly visible display method. For example, if the user is using a smartwatch, the display unit can provide a simple and highly visible display method. This allows the optimal display method to be selected by taking into account the user's device information. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input device information data to a generation AI and cause the generation AI to select the optimal display method.

[0110] When displaying text, the display unit can make the display content multilingual according to the user's language setting. The display unit automatically sets the display content based on, for example, the language setting of the user's device. The display unit can also provide a language switching function when the user uses multiple languages. For example, the display unit provides a language switching function when the user uses multiple languages. The display unit can also provide display content in a specific language when the user selects that language. For example, the display unit provides display content in that language when the user selects a specific language. This makes it possible to provide more appropriate information by making the display content multilingual according to the user's language setting. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input language setting data to a generation AI and cause the generation AI to execute multilingual display content.

[0111] The display unit can customize the display format according to the user's visual preferences when displaying text. The display unit customizes the display format using, for example, a font or color preferred by the user. The display unit can also customize the display format using a layout preferred by the user. For example, the display unit customizes the display format using a layout preferred by the user. The display unit can also customize the display format according to the user's visual preferences. For example, the display unit customizes the display format according to the user's visual preferences. This allows more appropriate information to be provided by customizing the display format according to the user's visual preferences. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input visual preference data to a generation AI and cause the generation AI to customize the display format.

[0112] The conversion unit can estimate the user's emotion and adjust the sign language action conversion method based on the estimated user emotion. The conversion unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the conversion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The conversion unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the conversion unit can analyze the tone and speed of the voice and calculate an emotion score. The conversion unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the conversion unit can calculate an emotion score based on heart rate fluctuations. This allows the sign language action conversion method to be adjusted according to the user's emotion, thereby converting the sign language into more appropriate actions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit may input facial expression data of a user acquired by a camera to the generation AI and cause the generation AI to estimate emotions.

[0113] The conversion unit can apply a conversion algorithm to accommodate different sign languages ​​or dialects when converting sign language actions. The conversion unit, for example, applies a conversion algorithm to accommodate different sign languages. The conversion unit can also apply a conversion algorithm to accommodate a specific dialect. For example, the conversion unit applies a conversion algorithm to accommodate a specific dialect. The conversion unit can also adjust the conversion algorithm taking into account differences between sign languages ​​or dialects. For example, the conversion unit adjusts the conversion algorithm taking into account differences between sign languages ​​or dialects. This improves the accuracy of conversion by accommodating different sign languages ​​or dialects. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input data of a sign language or dialect to a generation AI and cause the generation AI to apply the conversion algorithm.

[0114] The conversion unit can improve the accuracy of conversion by referring to the user's past conversion results when converting sign language movements. For example, the conversion unit can improve the conversion accuracy of similar movements by referring to the user's past conversion results. The conversion unit can also make it easier to recognize specific movement patterns based on the user's past conversion results. For example, the conversion unit can make it easier to recognize specific movement patterns based on the user's past conversion results. The conversion unit can also reduce misrecognition during conversion by utilizing the user's past conversion results. For example, the conversion unit can reduce misrecognition during conversion by utilizing the user's past conversion results. In this way, the accuracy of conversion is improved by referring to the user's past conversion results. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input past conversion result data into a generation AI and cause the generation AI to improve the accuracy of conversion.

[0115] The conversion unit can estimate the user's emotion and determine the priority of sign language actions to be converted based on the estimated user emotion. The conversion unit, for example, uses facial expression recognition technology to estimate the user's emotion. For example, the conversion unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The conversion unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the conversion unit can analyze the tone and speed of the voice and calculate an emotion score. The conversion unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the conversion unit can calculate an emotion score based on heart rate fluctuations. This allows the user's emotion to be prioritized and converted into more appropriate sign language actions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit may input facial expression data of a user acquired by a camera to the generation AI and cause the generation AI to estimate emotions.

[0116] When converting sign language actions, the conversion unit can prioritize converting highly relevant sign language actions by taking into account the user's geographical location information. For example, if the user is in a specific area, the conversion unit can prioritize converting sign language actions commonly used in that area. Furthermore, if the user is traveling, the conversion unit can also prioritize converting sign language actions commonly used in tourist spots. For example, if the user is traveling, the conversion unit can prioritize converting sign language actions commonly used in tourist spots. Furthermore, if the user is participating in a specific event, the conversion unit can also prioritize converting sign language actions related to the event. For example, if the user is participating in a specific event, the conversion unit can prioritize converting sign language actions related to the event. In this way, highly relevant sign language actions can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the conversion unit may be performed using AI, for example, or may be performed without using AI. For example, the conversion unit can input geographical location information data to a generation AI and cause the generation AI to perform preferential conversion of highly relevant sign language actions.

[0117] When converting sign language actions, the conversion unit can analyze the user's social media activities and convert the related sign language actions. For example, the conversion unit analyzes content posted by the user on social media and converts the related sign language actions. The conversion unit can also convert the related sign language actions by referring to the activities of the user's friends on social media. For example, the conversion unit converts the related sign language actions by referring to the activities of the user's friends on social media. The conversion unit can also convert the related sign language actions based on the user's check-in information on social media. For example, the conversion unit converts the related sign language actions based on the user's check-in information on social media. In this way, the related sign language actions can be converted by analyzing the user's social media activities. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input social media activity data to a generation AI and cause the generation AI to convert the related sign language actions.

[0118] The conversion unit can customize the conversion method by reflecting the user's past feedback when converting sign language actions. The conversion unit adjusts the conversion method based on, for example, feedback provided by the user in the past. The conversion unit can also improve the conversion accuracy of specific sign language actions by referring to the user's past feedback. For example, the conversion unit improves the conversion accuracy of specific sign language actions by referring to the user's past feedback. The conversion unit can also reflect the user's feedback to reduce misrecognition during conversion. For example, the conversion unit reflects the user's feedback to reduce misrecognition during conversion. In this way, the conversion method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversion unit can input past feedback data into a generation AI and cause the generation AI to customize the conversion method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned capture unit, analysis unit, generation unit, display unit, and conversion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the capture unit captures sign language movements using the camera 42 or a sensor of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the captured sign language movements. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates corresponding characters based on the analysis results. The display unit displays the generated characters on the display 40A of the smart device 14. The conversion unit is realized by the specific processing unit 290 of the data processing device 12 and converts the displayed characters into sign language movements. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned capture unit, analysis unit, generation unit, display unit, and conversion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the capture unit captures sign language actions using the camera 42 or a sensor of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the captured sign language actions. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates corresponding characters based on the analysis results. The display unit displays the generated characters on the display of the smart glasses 214. The conversion unit is realized by the specific processing unit 290 of the data processing device 12 and converts the displayed characters into sign language actions. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned capture unit, analysis unit, generation unit, display unit, and conversion unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the capture unit captures sign language movements using the camera 42 or a sensor of the headset type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the captured sign language movements. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates corresponding characters based on the analysis results. The display unit displays the generated characters on the display 343 of the headset type terminal 314. The conversion unit is realized by the specific processing unit 290 of the data processing device 12 and converts the displayed characters into sign language movements. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned capture unit, analysis unit, generation unit, display unit, and conversion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the capture unit captures sign language movements using the camera 42 or a sensor of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the captured sign language movements. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates corresponding characters based on the analysis results. The display unit displays the generated characters on the display of the robot 414. The conversion unit is realized by the specific processing unit 290 of the data processing device 12 and converts the displayed characters into sign language movements.

[0119] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0120] The capture unit can simultaneously capture not only the user's hand movements but also their facial expressions and body movements. For example, the capture unit can capture facial expressions simultaneously with hand movements to more accurately analyze the meaning of sign language. The capture unit can also capture the user's body movements to check whether the sign language actions are in line with the context. This allows for more accurate analysis of the meaning of sign language by capturing not only hand movements but also facial expressions and body movements.

[0121] When analyzing sign language movements, the analysis unit can improve the accuracy of the analysis by taking into account the speed and rhythm of the sign language movements. For example, if the sign language movements are fast, the analysis algorithm can be adjusted to match that speed. Also, if the sign language movements are slow, the analysis algorithm can be adjusted to match that rhythm. In this way, by taking into account the speed and rhythm of the sign language movements, the accuracy of the analysis can be improved.

[0122] When generating characters, the generation unit can adjust the level of detail of the characters to be generated based on the importance of the sign language action. For example, detailed characters can be generated for important sign language actions. Also, simple characters can be generated for general sign language actions. In this way, by adjusting the level of detail of characters based on the importance of the sign language action, more appropriate characters can be generated.

[0123] The display unit can estimate the user's emotions and adjust the format of the text to be displayed based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible format can be provided. Alternatively, if the user is relaxed, a format containing more detailed information can be provided. This allows for more appropriate display by adjusting the display format according to the user's emotions.

[0124] The conversion unit can apply a conversion algorithm to support different sign languages ​​or dialects when converting sign language actions. For example, a conversion algorithm to support different sign languages ​​can be applied. Also, a conversion algorithm to support a specific dialect can be applied. This improves the accuracy of conversion by supporting different sign languages ​​and dialects.

[0125] The capture unit can estimate the user's emotions and adjust the timing of sign language action capture based on the estimated user emotions. For example, if the user is nervous, the capture timing can be delayed to capture more accurate actions. On the other hand, if the user is relaxed, the capture timing can be advanced to capture smoother actions. In this way, by adjusting the capture timing according to the user's emotions, more appropriate sign language actions can be captured.

[0126] When analyzing sign language movements, the analysis unit can correct the analysis results by taking into account the context of the sign language movements. For example, it can check whether the sign language movements are in line with the context and correct the analysis results. It can also reevaluate and correct the analysis results if the sign language movements do not match the context. In this way, the accuracy of the analysis results is improved by taking into account the context of the sign language movements.

[0127] The generation unit can estimate the user's emotions and adjust the expression method of the generated characters based on the estimated user emotions. For example, if the user is nervous, simple, highly visible characters can be generated. On the other hand, if the user is relaxed, characters containing more detailed information can be generated. In this way, by adjusting the expression method of the characters according to the user's emotions, more appropriate characters can be generated.

[0128] When displaying text, the display unit can select the optimal display method by referring to the user's past display history. For example, the display unit can select a display method for similar content by referring to the user's past display history. It can also make it easier to recognize specific display patterns based on the user's past display history. This allows the optimal display method to be selected by referring to the user's past display history.

[0129] The conversion unit can estimate the user's emotions and adjust the method of converting sign language actions based on the estimated user emotions. For example, if the user is nervous, the conversion unit can convert the sign language actions into simpler, more visible sign language actions. Alternatively, if the user is relaxed, the conversion unit can convert the sign language actions into more detailed actions. In this way, by adjusting the method of converting sign language actions according to the user's emotions, the conversion unit can convert the sign language actions into more appropriate sign language actions.

[0130] The processing flow of the second embodiment will be briefly explained below.

[0131] Step 1: The capture unit includes a camera or sensor for capturing sign language actions. For example, the capture unit captures sign language actions with a high-resolution camera. The capture unit can also capture sign language actions in three dimensions using a depth sensor. The capture unit can also simultaneously capture not only the user's hand movements but also their facial expressions and body movements. For example, the capture unit can capture facial expressions simultaneously with hand movements to more accurately analyze the meaning of the sign language. Step 2: The analysis unit analyzes the sign language movements captured by the capture unit. The analysis unit analyzes the sign language movements with high accuracy, for example, using deep learning. The analysis unit can also improve the accuracy of the analysis by taking into account the speed and rhythm of the sign language movements. For example, if the sign language movements are fast, the analysis algorithm is adjusted to match that speed. Step 3: The generation unit generates corresponding characters based on the sign language actions analyzed by the analysis unit. The generation unit, for example, collects data on sign language from the Internet and builds a sign language database. The generation unit can also estimate the user's emotions and adjust the expression method of the generated characters based on the estimated user emotions. For example, if the user is nervous, the generation unit generates simple, highly visible characters. Step 4: The display unit displays the characters generated by the generation unit. The display unit, for example, determines whether the sign language movements are correct. The display unit can also estimate the user's emotions and adjust the format of the characters to be displayed based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible format is provided. Step 5: The conversion unit converts the characters displayed by the display unit into sign language actions. The conversion unit converts, for example, characters or voice into sign language actions. The conversion unit can also estimate the user's emotions and adjust the method of converting sign language actions based on the estimated user's emotions. For example, if the user is nervous, the conversion unit converts into simple, highly visible sign language actions.

[0132] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0136] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0152] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0153] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0154] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0155] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0156] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0158] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0159] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0160] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0161] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0162] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0163] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0164] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0166] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0167] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0168] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0169] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0170] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0171] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0172] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0173] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0174] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0175] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0176] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0177] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0178] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0179] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0180] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0181] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0182] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0183] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0184] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0185] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0186] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0187] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0188] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0189] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0190] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0191] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0192] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0193] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0194] 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.

[0195] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0196] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0197] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0198] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0199] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0200] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0201] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0202] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0203] [Explanation of symbols]

[0204] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a capture unit having a camera or a sensor for capturing sign language actions; an analysis unit that analyzes the sign language gestures captured by the capture unit; a generation unit that generates a corresponding character based on the sign language gesture analyzed by the analysis unit; a display unit that displays the characters generated by the generation unit; a conversion unit that converts the characters displayed by the display unit into sign language actions. A system characterized by:

2. The capture unit Using a depth sensor to capture sign language movements in three dimensions 2. The system of claim 1.

3. The analysis unit Using deep learning to accurately analyze sign language movements 2. The system of claim 1.

4. The generation unit Collecting data on sign language on the Internet and building a sign language database 2. The system of claim 1.

5. The conversion unit Converting text or speech into sign language actions 2. The system of claim 1.

6. The display unit Determine whether the sign language actions are correct or not 2. The system of claim 1.

7. The capture unit Estimate the user's emotions and adjust the timing of sign language action capture based on the estimated user emotions.

2. The system of claim 1.

8. The capture unit When capturing sign language actions, not only the user's hand movements but also their facial expressions and body movements are captured at the same time.

2. The system of claim 1.

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