system

Abstract

To provide a system that enables people with hearing impairments to better understand television content. [Solution] The system includes a voice analysis unit, a sign language generation unit, a visual information generation unit, and a display unit. The voice analysis unit analyzes voice data. The sign language generation unit generates a sign language interpretation based on text data analyzed by the voice analysis unit. The visual information generation unit generates visual information based on the text data analyzed by the voice analysis unit. The display unit displays the information generated by the sign language generation unit and the visual information generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for people with hearing impairments to fully understand television content, creating a barrier to information transmission.

[0005] The system according to the embodiment aims to make it easier for people with hearing impairments to understand television content. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice analysis unit, a sign language generation unit, a visual information generation unit, and a display unit. The voice analysis unit analyzes voice data. The sign language generation unit generates a sign language interpretation based on text data analyzed by the voice analysis unit. The visual information generation unit generates visual information based on the text data analyzed by the voice analysis unit. The display unit displays the information generated by the sign language generation unit and the visual information generation unit. [Effects of the Invention]

[0007] Systems according to embodiments can make television content easier to understand for people with hearing limitations. [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 system according to an embodiment of the present invention utilizes generative AI to provide sign language interpretation and visual information. This system analyzes audio data from television programs, generates sign language interpretation and visual information, and displays them on a television screen. This makes it easier for people with hearing impairments to understand television content and creates a barrier-free media environment. For example, a generative AI analyzes the audio data of a television program and generates sign language interpretation and visual information. The generative AI converts the audio data into text data and generates sign language interpretation and visual information based on the text data. For example, the system can analyze the audio data of a news program and display the news content as a sign language interpretation. The generated sign language interpretation and visual information are then displayed on a television screen. For example, the sign language interpretation can be displayed in a portion of the screen, and the visual information can be displayed as subtitles. This makes it easier for people with hearing impairments to understand television content. This system enables people with hearing impairments to enjoy realistic information transmission and entertainment experiences. For example, they can understand the content of movies and dramas through sign language interpretation and visual information, allowing them to enjoy entertainment. In addition, by understanding the content of news and educational programs through sign language interpretation and visual information, people can accurately grasp the information. Furthermore, this system contributes to the creation of a barrier-free media environment. By enabling people with hearing limitations to access information through television, inclusive communication is promoted throughout society. For example, by providing sign language interpretation and visual information, people with hearing limitations can more easily understand the content of news and educational programs, promoting their social participation. In this way, a system utilizing generative AI can make it easier for people with hearing limitations to understand television content, thereby creating a barrier-free media environment.

[0029] A sign language interpretation and visual information providing system according to an embodiment includes an audio analysis unit, a sign language generation unit, a visual information generation unit, and a display unit. The audio analysis unit analyzes audio data of a television program. The audio data includes, but is not limited to, audio file formats and real-time audio data. The audio analysis unit converts the audio data into text data using, for example, speech recognition technology or natural language processing technology. For example, the audio analysis unit analyzes audio data of a news program and converts the content into text data. The sign language generation unit uses a generation AI to generate a sign language interpreter based on the text data analyzed by the audio analysis unit. The sign language interpreter is generated based on, for example, a type of sign language and a generation algorithm, but is not limited to, for example. For example, the sign language generation unit generates a sign language interpreter using a deep learning model. The sign language generation unit can also generate a sign language interpreter using a generation algorithm. The visual information generation unit uses a generation AI to generate visual information based on the text data analyzed by the audio analysis unit. The visual information is generated in the form of, for example, an image, a video, an animation, or the like, but is not limited to, for example. For example, the visual information generation unit generates the visual information using a deep learning model. The visual information generation unit can also generate the visual information using a generation algorithm. The display unit displays the information generated by the sign language generation unit and the visual information generation unit on a television screen. The display unit, for example, displays the generated sign language interpretation in a portion of the screen. For example, the display unit displays the sign language interpretation in the lower right corner of the screen. The display unit also displays the generated visual information as subtitles. For example, the display unit displays the visual information as subtitles in the lower part of the screen. In this way, the sign language interpretation and visual information provision system according to the embodiment makes it easier for people with hearing limitations to understand television content, and can create a barrier-free media environment.

[0030] The sign language generation unit can generate a sign language interpreter using a generation AI. The sign language generation unit generates a sign language interpreter using, for example, a generation AI. The generation AI can generate a sign language interpreter using a deep learning model or a generation algorithm. For example, the sign language generation unit uses a deep learning model to generate a sign language interpreter based on text data analyzed by the voice analysis unit. The sign language generation unit can also use a generation algorithm to generate a sign language interpreter based on text data analyzed by the voice analysis unit. In this way, the use of the generation AI improves the accuracy of generating a sign language interpreter. Some or all of the above-mentioned processing in the sign language generation unit is performed using the generation AI. For example, the sign language generation unit can input text data analyzed by the voice analysis unit to the generation AI and cause the generation AI to generate a sign language interpreter.

[0031] The visual information generation unit can generate visual information using a generation AI. The visual information generation unit generates visual information using, for example, a generation AI. The generation AI can generate visual information using a deep learning model or a generation algorithm. For example, the visual information generation unit can use a deep learning model to generate visual information based on text data analyzed by the audio analysis unit. The visual information generation unit can also use a generation algorithm to generate visual information based on text data analyzed by the audio analysis unit. This improves the accuracy of visual information generation by using the generation AI. Some or all of the above-mentioned processing in the visual information generation unit can be performed using the generation AI. For example, the visual information generation unit can input text data analyzed by the audio analysis unit to the generation AI and cause the generation AI to generate visual information.

[0032] The display unit can display the generated sign language interpreter on a portion of the screen. For example, the display unit can display the generated sign language interpreter on a portion of the screen. The display unit can display the generated sign language interpreter on the lower right of the screen. The display unit can also display the generated sign language interpreter on the upper left of the screen. By displaying the sign language interpreter on a portion of the screen, people with hearing limitations can more easily understand the content of television. Some or all of the above-described processing on the display unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the display unit can input the generated sign language interpreter to the generation AI and have the generation AI adjust the display position.

[0033] The display unit can display the generated visual information as subtitles. For example, the display unit displays the generated visual information as subtitles. The display unit can display the generated visual information as subtitles at the bottom of the screen. The display unit can also display the generated visual information as subtitles at the top of the screen. By displaying the visual information as subtitles, people with hearing limitations can more easily understand the content of television. Some or all of the above-described processing in the display unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the display unit can input the generated visual information to a generation AI and cause the generation AI to adjust the subtitle display.

[0034] The voice analysis unit can perform filtering to remove background noise during voice analysis. For example, the voice analysis unit performs filtering to remove background noise during voice analysis. Background noise is removed using, for example, noise filtering technology or a noise reduction algorithm. For example, the voice analysis unit uses a generation AI to detect background noise in real time and perform filtering. The voice analysis unit can also use a generation AI to remove noise in a specific frequency band to improve voice clarity. The voice analysis unit can also use a generation AI to integrate voice data from multiple microphones and reduce noise. This removes background noise, improving the accuracy of voice analysis. Some or all of the above-mentioned processing in the voice analysis unit can be performed using the generation AI. For example, the voice analysis unit can input voice data to the generation AI and have the generation AI remove background noise.

[0035] The voice analysis unit can identify speakers during voice analysis and apply different analysis algorithms to each speaker. For example, the voice analysis unit can identify speakers during voice analysis and apply different analysis algorithms to each speaker. Speaker identification is performed using, for example, voice recognition technology or a speaker identification algorithm. For example, the voice analysis unit uses a generation AI to analyze the speaker's voiceprint and create an individual voice profile. After the generation AI identifies a speaker, the voice analysis unit can select an appropriate voice analysis algorithm. The voice analysis unit can also analyze multiple speakers simultaneously and apply the optimal algorithm to each speaker. This improves the accuracy of voice analysis by applying the optimal analysis algorithm to each speaker. Some or all of the above-described processing in the voice analysis unit can be performed using the generation AI. For example, the voice analysis unit can input voice data into the generation AI and have the generation AI identify the speaker and apply the analysis algorithm.

[0036] The voice analysis unit can improve the accuracy of the analysis by adjusting the voice speed during voice analysis. For example, the voice analysis unit can improve the accuracy of the analysis by adjusting the voice speed during voice analysis. The voice speed is adjusted, for example, using a speed adjustment algorithm or real-time adjustment technology. For example, the voice analysis unit can have the generation AI slow down the voice speed for analysis to improve accuracy. The voice analysis unit can also have the generation AI speed up the voice speed for analysis to maintain real-time performance. The voice analysis unit can also have the generation AI dynamically adjust the voice speed to provide optimal analysis results. In this way, adjusting the voice speed improves the accuracy of the analysis. Some or all of the above-mentioned processing in the voice analysis unit can be performed using the generation AI. For example, the voice analysis unit can input voice data to the generation AI and have the generation AI adjust the voice speed.

[0037] The voice analysis unit can analyze the tone and pitch of the voice during voice analysis to infer emotions and intentions. For example, the voice analysis unit can analyze the tone and pitch of the voice during voice analysis to infer emotions and intentions. The tone and pitch of the voice are analyzed using, for example, voice analysis technology or a pitch detection algorithm. For example, the voice analysis unit uses a generation AI to analyze the tone of the voice to infer the speaker's emotions. The voice analysis unit can also use a generation AI to analyze the pitch of the voice to infer the speaker's intentions. The voice analysis unit can also perform an integrated analysis of the tone and pitch of the voice to more accurately infer emotions and intentions. This allows the speaker's emotions and intentions to be accurately inferred by analyzing the tone and pitch of the voice. Some or all of the above-mentioned processing in the voice analysis unit is performed using the generation AI. For example, the voice analysis unit can input voice data to the generation AI and have the generation AI perform tone and pitch analysis.

[0038] The sign language generation unit can generate sign language taking into account regional differences in sign language. For example, the sign language generation unit generates sign language taking into account regional differences in sign language. Regional differences in sign language are considered using, for example, a regional sign language dictionary or sign language dialect. For example, the sign language generation unit generates appropriate sign language by having the generation AI refer to a regional sign language database. The sign language generation unit can also generate regionally specific sign language based on the user's regional information. The sign language generation unit can also integrate sign languages ​​from multiple regions to generate optimal sign language. This allows for more appropriate sign language interpretation by taking into account regional differences in sign language. Some or all of the above-mentioned processing in the sign language generation unit is performed using the generation AI. For example, the sign language generation unit can input regional information to the generation AI and cause the generation AI to generate regional sign language.

[0039] The sign language generation unit can adjust the speed and emphasis of the sign language according to the context when generating the sign language. For example, the sign language generation unit adjusts the speed and emphasis of the sign language according to the context when generating the sign language. The context is analyzed using, for example, a context analysis algorithm or a context information extraction method. For example, the sign language generation unit generates sign language that emphasizes important parts by using a generation AI to analyze the context. The sign language generation unit can also adjust the speed of the sign language according to the context to make it easier to understand. The sign language generation unit can also smooth the sign language movements based on the context to make them visually easier to understand. This makes it possible to provide sign language interpretation that is easier to understand by adjusting the speed and emphasis of the sign language according to the context. Some or all of the above-mentioned processing in the sign language generation unit can be performed using the generation AI. For example, the sign language generation unit can input context information to the generation AI and cause the generation AI to adjust the speed and emphasis of the sign language.

[0040] The sign language generation unit can adjust the difficulty of the sign language according to the user's age and educational level when generating the sign language. For example, the sign language generation unit adjusts the difficulty of the sign language according to the user's age and educational level when generating the sign language. The user's age and educational level are identified using, for example, user profile information or survey results. For example, the sign language generation unit generates sign language of an appropriate difficulty level by using a generation AI to consider the user's age. The sign language generation unit can also generate sign language that is easy to understand based on the user's educational level. The sign language generation unit can also generate optimal sign language by comprehensively considering the user's age and educational level. This makes it possible to provide sign language interpretation that is easier to understand by adjusting the difficulty of the sign language according to the user's age and educational level. Some or all of the above-mentioned processing in the sign language generation unit is performed using the generation AI. For example, the sign language generation unit can input the user's profile information into the generation AI and cause the generation AI to adjust the difficulty of the sign language.

[0041] The sign language generation unit can learn the styles of multiple sign language interpreters when generating sign language and generate the optimal sign language. For example, the sign language generation unit can learn the styles of multiple sign language interpreters when generating sign language and generate the optimal sign language. The sign language interpreter styles are learned, for example, using a sign language interpreter dataset or a style feature extraction method. For example, the sign language generation unit generates the optimal sign language by having a generation AI learn data from multiple sign language interpreters. The sign language generation unit can also analyze the styles of sign language interpreters and generate the optimal sign language for the user. The sign language generation unit can also integrate the styles of sign language interpreters and generate visually easy-to-understand sign language. In this way, by learning the styles of multiple sign language interpreters, it is possible to provide sign language interpretation that is more visually easy to understand. Some or all of the above-mentioned processing in the sign language generation unit can be performed using the generation AI. For example, the sign language generation unit can input data of sign language interpreters into the generation AI and have the generation AI generate sign language.

[0042] The visual information generation unit can use colors that take into consideration users with color vision deficiencies when generating visual information. For example, the visual information generation unit uses colors that take into consideration users with color vision deficiencies when generating visual information. Color vision deficiencies are taken into consideration, for example, by using the type of color vision deficiency and color usage guidelines. For example, the visual information generation unit adjusts color contrast for users with color vision deficiencies using the generation AI. The visual information generation unit can also generate visual information that avoids specific colors for users with color vision deficiencies using the generation AI. The visual information generation unit can also generate visual information that uses shapes or patterns instead of colors for users with color vision deficiencies. This improves the comprehension of visual information by using colors that take into consideration users with color vision deficiencies. Some or all of the above-mentioned processing in the visual information generation unit can be performed using the generation AI. For example, the visual information generation unit can input information about color vision deficiencies into the generation AI and have the generation AI adjust the color usage.

[0043] The visual information generation unit can adjust the size and position of the display according to the importance of the information when generating the visual information. For example, the visual information generation unit adjusts the size and position of the display according to the importance of the information when generating the visual information. The importance of the information is evaluated using, for example, an information prioritization algorithm or an importance evaluation criterion. For example, the visual information generation unit has the generation AI display important information larger to make it stand out. The visual information generation unit can also display less important information smaller, reducing visual strain. The visual information generation unit can also dynamically adjust the display position according to the importance of the information. This makes it possible to provide visually easy-to-understand information by adjusting the display size and position according to the importance of the information. Some or all of the above-mentioned processing in the visual information generation unit can be performed using the generation AI. For example, the visual information generation unit can input information importance data to the generation AI and have the generation AI adjust the display size and position.

[0044] The visual information generation unit can optimize the display method according to the screen size of the user's device when generating visual information. For example, the visual information generation unit optimizes the display method according to the screen size of the user's device when generating visual information. The screen size is identified using, for example, device resolution information or a screen size measurement method. For example, the visual information generation unit generates visual information that matches the screen size of a smartphone using a generation AI. The visual information generation unit can also generate visual information optimized for the screen size of a tablet using a generation AI. The visual information generation unit can also generate visual information optimized for a large display using a generation AI. This improves the visibility of the visual information by optimizing the display method according to the screen size of the user's device. Some or all of the above-mentioned processing in the visual information generation unit can be performed using a generation AI. For example, the visual information generation unit can input device screen size information to the generation AI and cause the generation AI to optimize the display method.

[0045] The visual information generation unit can provide information customized based on the user's visual preferences when generating visual information. For example, the visual information generation unit provides information customized based on the user's visual preferences when generating visual information. Visual preferences are identified using, for example, user profile information or survey results. For example, the visual information generation unit generates customized visual information by having the generation AI learn the user's visual preferences. The visual information generation unit can also provide optimal visual information based on the user's past visual information usage history. The visual information generation unit can also generate visual information with customized colors and fonts according to the user's visual preferences. This allows for more personalized visual information to be provided by providing customized information based on the user's visual preferences. Some or all of the above-described processing in the visual information generation unit can be performed using the generation AI. For example, the visual information generation unit can input user preference data into the generation AI and have the generation AI generate customized visual information.

[0046] The display unit can track the user's gaze during display and dynamically adjust the display position based on the gaze. For example, the display unit can track the user's gaze during display and dynamically adjust the display position based on the gaze. The gaze is tracked using, for example, gaze tracking technology or a gaze data analysis method. For example, the display unit can have the generation AI track the user's gaze and display important information at the gaze point. The display unit can also have the generation AI analyze the user's gaze movement and dynamically display information in accordance with the gaze movement. The display unit can also measure the concentration of the user's gaze and display important information at the focused area. This allows the display position to be dynamically adjusted based on the user's gaze, thereby displaying important information at the gaze point. Some or all of the above-mentioned processing in the display unit can be performed using the generation AI. For example, the display unit can input gaze data to the generation AI and have the generation AI adjust the display position.

[0047] The display unit can select the optimal display method by referring to the user's past viewing history when displaying. For example, the display unit selects the optimal display method by referring to the user's past viewing history when displaying. The viewing history is referenced, for example, using a viewing history database or a method of analyzing history data. For example, the display unit allows the generation AI to analyze the user's past viewing history and provide the optimal display layout. The display unit can also display using the user's preferred fonts and colors based on the user's viewing history. The display unit can also select a visually easy-to-understand display method by referring to the user's past viewing history. In this way, by referring to the user's past viewing history, a more visually easy-to-understand display method can be provided. Some or all of the above-mentioned processing in the display unit can be performed using the generation AI. For example, the display unit can input viewing history data to the generation AI and have the generation AI select the display method.

[0048] The display unit can optimize the display method according to the resolution of the user's device when displaying. For example, the display unit optimizes the display method according to the resolution of the user's device when displaying. The resolution is identified using, for example, device resolution information or a resolution measurement method. For example, the display unit allows the generation AI to detect the resolution of the user's device and provide an optimal display layout. The display unit can also allow the generation AI to display detailed information on a high-resolution device. The display unit can also allow the generation AI to display a simple, highly visible image on a low-resolution device. This improves visibility by optimizing the display method according to the resolution of the user's device. Some or all of the above-mentioned processing in the display unit is performed using the generation AI. For example, the display unit can input device resolution information to the generation AI and cause the generation AI to optimize the display method.

[0049] The display unit can adjust the brightness and contrast of the display according to the user's ambient light during display. For example, the display unit adjusts the brightness and contrast of the display according to the user's ambient light during display. Ambient light is identified using, for example, an ambient light sensor or a light measurement method. For example, the display unit has a generation AI that detects ambient light and automatically adjusts the brightness. The display unit can also dynamically adjust the contrast according to changes in ambient light. The display unit can also have the generation AI display a brighter image in a dark environment and a darker image in a bright environment. This improves visibility by adjusting the brightness and contrast of the display according to the user's ambient light. Some or all of the above-mentioned processing in the display unit is performed using the generation AI. For example, the display unit can input ambient light data to the generation AI and have the generation AI adjust the brightness and contrast.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] In addition to analyzing the audio data, the audio analysis unit can detect specific keywords contained in the audio data and provide additional related information based on those keywords. For example, if the audio data of a news program contains the keyword "earthquake," the audio analysis unit can provide the latest information on earthquakes and disaster prevention measures. Similarly, if the audio data of a music program contains the keyword "new song," the audio analysis unit can provide information on the lyrics and artist of the new song. Furthermore, if the audio data of an educational program contains the keyword "history," the audio analysis unit can provide information on related historical events and people. This allows for the provision of more information to users based on the results of the analysis of the audio data.

[0052] In addition to generating sign language interpretations, the sign language generation unit can track the user's learning progress and provide an individual learning plan. For example, the sign language generation unit can provide a practice plan for the user to master specific sign language movements. The sign language generation unit can also suggest the next sign language movement to learn based on the user's learning history. Furthermore, the sign language generation unit can provide real-time feedback and guidance on correct sign language movements as the user practices sign language. This allows the user to learn sign language efficiently.

[0053] In addition to generating visual information, the visual information generation unit can provide customized visual information based on the user's visual preferences. For example, the visual information generation unit can generate visual information using the user's preferred colors and fonts. The visual information generation unit can also provide optimal visual information based on the user's past visual information usage history. Furthermore, the visual information generation unit can adjust the layout and design of the visual information according to the user's visual preferences. This allows the user to use the visual information more comfortably.

[0054] In addition to providing sign language interpretation and displaying visual information, the display unit can track the user's gaze and dynamically adjust the display position based on the user's gaze. For example, if the user is gazing at a specific part of the screen, the display unit can display information related to that part. The display unit can also analyze the user's gaze movement and dynamically display information in accordance with the gaze movement. Furthermore, the display unit can measure the concentration of the user's gaze and display important information in the part where the user is concentrating. This allows the user to efficiently obtain information based on their gaze.

[0055] In addition to displaying visual information, the display unit can adjust the brightness and contrast of the display according to the user's ambient light. For example, the display unit can display brighter in dark environments and darker in bright environments. The display unit can also dynamically adjust the contrast according to changes in ambient light. Furthermore, the display unit can automatically select optimal display settings based on the user's ambient light data. This allows the user to enjoy a highly visible display in any environment.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The audio analysis unit analyzes the audio data of a television program. This audio data includes audio file formats and real-time audio data. The audio analysis unit converts the audio data into text data using speech recognition technology and natural language processing technology. For example, the audio data of a news program is analyzed and its contents are converted into text data. Step 2: The sign language generation unit uses a generation AI to generate a sign language interpreter based on the text data analyzed by the speech analysis unit. The sign language interpreter is generated based on the type of sign language and a generation algorithm. For example, a deep learning model is used to generate the sign language interpreter. Step 3: The visual information generation unit uses a generative AI to generate visual information based on the text data analyzed by the audio analysis unit. The visual information is generated in the form of images, videos, animations, etc. For example, the visual information is generated using a deep learning model. Step 4: The display unit displays the information generated by the sign language generation unit and the visual information generation unit on the television screen. For example, the generated sign language interpretation is displayed in one part of the screen, and the visual information is displayed as subtitles in the lower part of the screen.

[0058] (Example 2) A system according to an embodiment of the present invention utilizes generative AI to provide sign language interpretation and visual information. This system analyzes audio data from television programs, generates sign language interpretation and visual information, and displays them on a television screen. This makes it easier for people with hearing impairments to understand television content and creates a barrier-free media environment. For example, a generative AI analyzes the audio data of a television program and generates sign language interpretation and visual information. The generative AI converts the audio data into text data and generates sign language interpretation and visual information based on the text data. For example, the system can analyze the audio data of a news program and display the news content as a sign language interpretation. The generated sign language interpretation and visual information are then displayed on a television screen. For example, the sign language interpretation can be displayed in a portion of the screen, and the visual information can be displayed as subtitles. This makes it easier for people with hearing impairments to understand television content. This system enables people with hearing impairments to enjoy realistic information transmission and entertainment experiences. For example, they can understand the content of movies and dramas through sign language interpretation and visual information, allowing them to enjoy entertainment. In addition, by understanding the content of news and educational programs through sign language interpretation and visual information, people can accurately grasp the information. Furthermore, this system contributes to the creation of a barrier-free media environment. By enabling people with hearing limitations to access information through television, inclusive communication is promoted throughout society. For example, by providing sign language interpretation and visual information, people with hearing limitations can more easily understand the content of news and educational programs, promoting their social participation. In this way, a system utilizing generative AI can make it easier for people with hearing limitations to understand television content, thereby creating a barrier-free media environment.

[0059] A sign language interpretation and visual information providing system according to an embodiment includes an audio analysis unit, a sign language generation unit, a visual information generation unit, and a display unit. The audio analysis unit analyzes audio data of a television program. The audio data includes, but is not limited to, audio file formats and real-time audio data. The audio analysis unit converts the audio data into text data using, for example, speech recognition technology or natural language processing technology. For example, the audio analysis unit analyzes audio data of a news program and converts the content into text data. The sign language generation unit uses a generation AI to generate a sign language interpreter based on the text data analyzed by the audio analysis unit. The sign language interpreter is generated based on, for example, a type of sign language and a generation algorithm, but is not limited to, for example. For example, the sign language generation unit generates a sign language interpreter using a deep learning model. The sign language generation unit can also generate a sign language interpreter using a generation algorithm. The visual information generation unit uses a generation AI to generate visual information based on the text data analyzed by the audio analysis unit. The visual information is generated in the form of, for example, an image, a video, an animation, or the like, but is not limited to, for example. For example, the visual information generation unit generates the visual information using a deep learning model. The visual information generation unit can also generate the visual information using a generation algorithm. The display unit displays the information generated by the sign language generation unit and the visual information generation unit on a television screen. The display unit, for example, displays the generated sign language interpretation in a portion of the screen. For example, the display unit displays the sign language interpretation in the lower right corner of the screen. The display unit also displays the generated visual information as subtitles. For example, the display unit displays the visual information as subtitles in the lower part of the screen. In this way, the sign language interpretation and visual information provision system according to the embodiment makes it easier for people with hearing limitations to understand television content, and can create a barrier-free media environment.

[0060] The sign language generation unit can generate a sign language interpreter using a generation AI. The sign language generation unit generates a sign language interpreter using, for example, a generation AI. The generation AI can generate a sign language interpreter using a deep learning model or a generation algorithm. For example, the sign language generation unit uses a deep learning model to generate a sign language interpreter based on text data analyzed by the voice analysis unit. The sign language generation unit can also use a generation algorithm to generate a sign language interpreter based on text data analyzed by the voice analysis unit. In this way, the use of the generation AI improves the accuracy of generating a sign language interpreter. Some or all of the above-mentioned processing in the sign language generation unit is performed using the generation AI. For example, the sign language generation unit can input text data analyzed by the voice analysis unit to the generation AI and cause the generation AI to generate a sign language interpreter.

[0061] The visual information generation unit can generate visual information using a generation AI. The visual information generation unit generates visual information using, for example, a generation AI. The generation AI can generate visual information using a deep learning model or a generation algorithm. For example, the visual information generation unit can use a deep learning model to generate visual information based on text data analyzed by the audio analysis unit. The visual information generation unit can also use a generation algorithm to generate visual information based on text data analyzed by the audio analysis unit. This improves the accuracy of visual information generation by using the generation AI. Some or all of the above-mentioned processing in the visual information generation unit can be performed using the generation AI. For example, the visual information generation unit can input text data analyzed by the audio analysis unit to the generation AI and cause the generation AI to generate visual information.

[0062] The display unit can display the generated sign language interpreter on a portion of the screen. For example, the display unit can display the generated sign language interpreter on a portion of the screen. The display unit can display the generated sign language interpreter on the lower right of the screen. The display unit can also display the generated sign language interpreter on the upper left of the screen. By displaying the sign language interpreter on a portion of the screen, people with hearing limitations can more easily understand the content of television. Some or all of the above-described processing on the display unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the display unit can input the generated sign language interpreter to the generation AI and have the generation AI adjust the display position.

[0063] The display unit can display the generated visual information as subtitles. For example, the display unit displays the generated visual information as subtitles. The display unit can display the generated visual information as subtitles at the bottom of the screen. The display unit can also display the generated visual information as subtitles at the top of the screen. By displaying the visual information as subtitles, people with hearing limitations can more easily understand the content of television. Some or all of the above-described processing in the display unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the display unit can input the generated visual information to a generation AI and cause the generation AI to adjust the subtitle display.

[0064] The voice analysis unit can estimate the user's emotions and adjust the accuracy of the voice analysis based on the estimated user's emotions. The voice analysis unit, for example, estimates the user's emotions and adjusts the accuracy of the voice analysis based on the estimated user's emotions. The user's emotions are estimated using technologies such as voice tone analysis and facial expression recognition. For example, if the user is nervous, the voice analysis unit can cause the generation AI to increase the accuracy of the voice analysis and reduce misrecognition. Furthermore, if the user is relaxed, the voice analysis unit can cause the generation AI to maintain the accuracy of the voice analysis at a normal level. Furthermore, if the user is in a hurry, the voice analysis unit can prioritize the speed of the voice analysis and provide analysis results quickly. This allows for more accurate analysis results to be provided by adjusting the accuracy of the voice analysis according to the user's emotions. Some or all of the above-mentioned processing in the voice analysis unit can be performed using the generation AI. For example, the voice analysis unit can input user emotion data into the generation AI and have the generation AI adjust the accuracy of the voice analysis.

[0065] The voice analysis unit can perform filtering to remove background noise during voice analysis. For example, the voice analysis unit performs filtering to remove background noise during voice analysis. Background noise is removed using, for example, noise filtering technology or a noise reduction algorithm. For example, the voice analysis unit uses a generation AI to detect background noise in real time and perform filtering. The voice analysis unit can also use a generation AI to remove noise in a specific frequency band to improve voice clarity. The voice analysis unit can also use a generation AI to integrate voice data from multiple microphones and reduce noise. This removes background noise, improving the accuracy of voice analysis. Some or all of the above-mentioned processing in the voice analysis unit can be performed using the generation AI. For example, the voice analysis unit can input voice data to the generation AI and have the generation AI remove background noise.

[0066] The voice analysis unit can identify speakers during voice analysis and apply different analysis algorithms to each speaker. For example, the voice analysis unit can identify speakers during voice analysis and apply different analysis algorithms to each speaker. Speaker identification is performed using, for example, voice recognition technology or a speaker identification algorithm. For example, the voice analysis unit uses a generation AI to analyze the speaker's voiceprint and create an individual voice profile. After the generation AI identifies a speaker, the voice analysis unit can select an appropriate voice analysis algorithm. The voice analysis unit can also analyze multiple speakers simultaneously and apply the optimal algorithm to each speaker. This improves the accuracy of voice analysis by applying the optimal analysis algorithm to each speaker. Some or all of the above-described processing in the voice analysis unit can be performed using the generation AI. For example, the voice analysis unit can input voice data into the generation AI and have the generation AI identify the speaker and apply the analysis algorithm.

[0067] The voice analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. The voice analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated user emotions. The user's emotions can be estimated using technologies such as voice tone analysis and facial expression recognition. For example, if the user is excited, the voice analysis unit can cause the generation AI to prioritize analysis and display of important information. Furthermore, if the user is relaxed, the voice analysis unit can cause the generation AI to analyze and display overall information evenly. Furthermore, if the user is in a hurry, the voice analysis unit can prioritize analysis of key points and display them quickly. This allows important information to be provided preferentially by prioritizing the analysis results according to the user's emotions. Some or all of the above-described processing in the voice analysis unit can be performed using the generation AI. For example, the voice analysis unit can input the user's emotional data into the generation AI and have the generation AI prioritize the analysis results.

[0068] The voice analysis unit can improve the accuracy of the analysis by adjusting the voice speed during voice analysis. For example, the voice analysis unit can improve the accuracy of the analysis by adjusting the voice speed during voice analysis. The voice speed is adjusted, for example, using a speed adjustment algorithm or real-time adjustment technology. For example, the voice analysis unit can have the generation AI slow down the voice speed for analysis to improve accuracy. The voice analysis unit can also have the generation AI speed up the voice speed for analysis to maintain real-time performance. The voice analysis unit can also have the generation AI dynamically adjust the voice speed to provide optimal analysis results. In this way, adjusting the voice speed improves the accuracy of the analysis. Some or all of the above-mentioned processing in the voice analysis unit can be performed using the generation AI. For example, the voice analysis unit can input voice data to the generation AI and have the generation AI adjust the voice speed.

[0069] The voice analysis unit can analyze the tone and pitch of the voice during voice analysis to infer emotions and intentions. For example, the voice analysis unit can analyze the tone and pitch of the voice during voice analysis to infer emotions and intentions. The tone and pitch of the voice are analyzed using, for example, voice analysis technology or a pitch detection algorithm. For example, the voice analysis unit uses a generation AI to analyze the tone of the voice to infer the speaker's emotions. The voice analysis unit can also use a generation AI to analyze the pitch of the voice to infer the speaker's intentions. The voice analysis unit can also perform an integrated analysis of the tone and pitch of the voice to more accurately infer emotions and intentions. This allows the speaker's emotions and intentions to be accurately inferred by analyzing the tone and pitch of the voice. Some or all of the above-mentioned processing in the voice analysis unit is performed using the generation AI. For example, the voice analysis unit can input voice data to the generation AI and have the generation AI perform tone and pitch analysis.

[0070] The sign language generation unit can estimate the user's emotions and adjust the sign language expression method based on the estimated user emotions. The sign language generation unit, for example, estimates the user's emotions and adjusts the sign language expression method based on the estimated user emotions. The user's emotions are estimated using technologies such as voice tone analysis and facial expression recognition. For example, if the user is nervous, the sign language generation unit causes the generation AI to slow down the sign language movements to make them easier to understand. Also, if the user is relaxed, the sign language generation unit causes the generation AI to perform the sign language movements at a normal speed. Also, if the user is excited, the sign language generation unit can emphasize the sign language movements to make them visually easier to understand. This allows the sign language expression method to be adjusted according to the user's emotions, making it possible to provide sign language interpretation that is easier to understand. Some or all of the above-mentioned processing in the sign language generation unit is performed using the generation AI. For example, the sign language generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the sign language expression method.

[0071] The sign language generation unit can generate sign language taking into account regional differences in sign language. For example, the sign language generation unit generates sign language taking into account regional differences in sign language. Regional differences in sign language are considered using, for example, a regional sign language dictionary or sign language dialect. For example, the sign language generation unit generates appropriate sign language by having the generation AI refer to a regional sign language database. The sign language generation unit can also generate regionally specific sign language based on the user's regional information. The sign language generation unit can also integrate sign languages ​​from multiple regions to generate optimal sign language. This allows for more appropriate sign language interpretation by taking into account regional differences in sign language. Some or all of the above-mentioned processing in the sign language generation unit is performed using the generation AI. For example, the sign language generation unit can input regional information to the generation AI and cause the generation AI to generate regional sign language.

[0072] The sign language generation unit can adjust the speed and emphasis of the sign language according to the context when generating the sign language. For example, the sign language generation unit adjusts the speed and emphasis of the sign language according to the context when generating the sign language. The context is analyzed using, for example, a context analysis algorithm or a context information extraction method. For example, the sign language generation unit generates sign language that emphasizes important parts by using a generation AI to analyze the context. The sign language generation unit can also adjust the speed of the sign language according to the context to make it easier to understand. The sign language generation unit can also smooth the sign language movements based on the context to make them visually easier to understand. This makes it possible to provide sign language interpretation that is easier to understand by adjusting the speed and emphasis of the sign language according to the context. Some or all of the above-mentioned processing in the sign language generation unit can be performed using the generation AI. For example, the sign language generation unit can input context information to the generation AI and cause the generation AI to adjust the speed and emphasis of the sign language.

[0073] The sign language generation unit can estimate the user's emotions and adjust the display order of the sign language based on the estimated user's emotions. The sign language generation unit, for example, estimates the user's emotions and adjusts the display order of the sign language based on the estimated user's emotions. The user's emotions are estimated using technologies such as voice tone analysis and facial expression recognition. For example, if the user is nervous, the sign language generation unit causes the generation AI to display important information first. Also, if the user is relaxed, the sign language generation unit can cause the generation AI to display information in order. Also, if the user is in a hurry, the sign language generation unit can cause the generation AI to display the main points first. In this way, by adjusting the display order of the sign language according to the user's emotions, important information can be provided preferentially. Some or all of the above-mentioned processing in the sign language generation unit can be performed using the generation AI. For example, the sign language generation unit can input user's emotion data into the generation AI and cause the generation AI to adjust the display order of the sign language.

[0074] The sign language generation unit can adjust the difficulty of the sign language according to the user's age and educational level when generating the sign language. For example, the sign language generation unit adjusts the difficulty of the sign language according to the user's age and educational level when generating the sign language. The user's age and educational level are identified using, for example, user profile information or survey results. For example, the sign language generation unit generates sign language of an appropriate difficulty level by using a generation AI to consider the user's age. The sign language generation unit can also generate sign language that is easy to understand based on the user's educational level. The sign language generation unit can also generate optimal sign language by comprehensively considering the user's age and educational level. This makes it possible to provide sign language interpretation that is easier to understand by adjusting the difficulty of the sign language according to the user's age and educational level. Some or all of the above-mentioned processing in the sign language generation unit is performed using the generation AI. For example, the sign language generation unit can input the user's profile information into the generation AI and cause the generation AI to adjust the difficulty of the sign language.

[0075] The sign language generation unit can learn the styles of multiple sign language interpreters when generating sign language and generate the optimal sign language. For example, the sign language generation unit can learn the styles of multiple sign language interpreters when generating sign language and generate the optimal sign language. The sign language interpreter styles are learned, for example, using a sign language interpreter dataset or a style feature extraction method. For example, the sign language generation unit generates the optimal sign language by having a generation AI learn data from multiple sign language interpreters. The sign language generation unit can also analyze the styles of sign language interpreters and generate the optimal sign language for the user. The sign language generation unit can also integrate the styles of sign language interpreters and generate visually easy-to-understand sign language. In this way, by learning the styles of multiple sign language interpreters, it is possible to provide sign language interpretation that is more visually easy to understand. Some or all of the above-mentioned processing in the sign language generation unit can be performed using the generation AI. For example, the sign language generation unit can input data of sign language interpreters into the generation AI and have the generation AI generate sign language.

[0076] The visual information generation unit can estimate the user's emotions and adjust the display method of visual information based on the estimated user emotions. The visual information generation unit, for example, estimates the user's emotions and adjusts the display method of visual information based on the estimated user emotions. The user's emotions are estimated using technologies such as voice tone analysis and facial expression recognition. For example, if the user is nervous, the visual information generation unit generates simple, highly visible visual information using a generation AI. If the user is relaxed, the visual information generation unit can generate detailed visual information. If the user is in a hurry, the visual information generation unit can generate visual information that focuses on the main points. This allows the display method of visual information to be adjusted according to the user's emotions, thereby providing visual information that is easier to understand. Some or all of the above-described processing in the visual information generation unit can be performed using a generation AI. For example, the visual information generation unit can input user emotion data into the generation AI and have the generation AI adjust the display method of visual information.

[0077] The visual information generation unit can use colors that take into consideration users with color vision deficiencies when generating visual information. For example, the visual information generation unit uses colors that take into consideration users with color vision deficiencies when generating visual information. Color vision deficiencies are taken into consideration, for example, by using the type of color vision deficiency and color usage guidelines. For example, the visual information generation unit adjusts color contrast for users with color vision deficiencies using the generation AI. The visual information generation unit can also generate visual information that avoids specific colors for users with color vision deficiencies using the generation AI. The visual information generation unit can also generate visual information that uses shapes or patterns instead of colors for users with color vision deficiencies. This improves the comprehension of visual information by using colors that take into consideration users with color vision deficiencies. Some or all of the above-mentioned processing in the visual information generation unit can be performed using the generation AI. For example, the visual information generation unit can input information about color vision deficiencies into the generation AI and have the generation AI adjust the color usage.

[0078] The visual information generation unit can adjust the size and position of the display according to the importance of the information when generating the visual information. For example, the visual information generation unit adjusts the size and position of the display according to the importance of the information when generating the visual information. The importance of the information is evaluated using, for example, an information prioritization algorithm or an importance evaluation criterion. For example, the visual information generation unit has the generation AI display important information larger to make it stand out. The visual information generation unit can also display less important information smaller, reducing visual strain. The visual information generation unit can also dynamically adjust the display position according to the importance of the information. This makes it possible to provide visually easy-to-understand information by adjusting the display size and position according to the importance of the information. Some or all of the above-mentioned processing in the visual information generation unit can be performed using the generation AI. For example, the visual information generation unit can input information importance data to the generation AI and have the generation AI adjust the display size and position.

[0079] The visual information generation unit can estimate the user's emotions and adjust the display order of visual information based on the estimated user emotions. The visual information generation unit, for example, estimates the user's emotions and adjusts the display order of visual information based on the estimated user emotions. The user's emotions are estimated using technologies such as voice tone analysis and facial expression recognition. For example, if the user is nervous, the visual information generation unit causes the generation AI to display important information first. Also, if the user is relaxed, the visual information generation unit can cause the generation AI to display information in order. Also, if the user is in a hurry, the visual information generation unit can cause the generation AI to display the main points first. This allows important information to be provided preferentially by adjusting the display order of visual information according to the user's emotions. Some or all of the above-mentioned processing in the visual information generation unit can be performed using the generation AI. For example, the visual information generation unit can input user emotion data into the generation AI and cause the generation AI to adjust the display order of visual information.

[0080] The visual information generation unit can optimize the display method according to the screen size of the user's device when generating visual information. For example, the visual information generation unit optimizes the display method according to the screen size of the user's device when generating visual information. The screen size is identified using, for example, device resolution information or a screen size measurement method. For example, the visual information generation unit generates visual information that matches the screen size of a smartphone using a generation AI. The visual information generation unit can also generate visual information optimized for the screen size of a tablet using a generation AI. The visual information generation unit can also generate visual information optimized for a large display using a generation AI. This improves the visibility of the visual information by optimizing the display method according to the screen size of the user's device. Some or all of the above-mentioned processing in the visual information generation unit can be performed using a generation AI. For example, the visual information generation unit can input device screen size information to the generation AI and cause the generation AI to optimize the display method.

[0081] The visual information generation unit can provide information customized based on the user's visual preferences when generating visual information. For example, the visual information generation unit provides information customized based on the user's visual preferences when generating visual information. Visual preferences are identified using, for example, user profile information or survey results. For example, the visual information generation unit generates customized visual information by having the generation AI learn the user's visual preferences. The visual information generation unit can also provide optimal visual information based on the user's past visual information usage history. The visual information generation unit can also generate visual information with customized colors and fonts according to the user's visual preferences. This allows for more personalized visual information to be provided by providing customized information based on the user's visual preferences. Some or all of the above-described processing in the visual information generation unit can be performed using the generation AI. For example, the visual information generation unit can input user preference data into the generation AI and have the generation AI generate customized visual information.

[0082] The display unit can estimate the user's emotions and adjust the display layout based on the estimated user emotions. The display unit, for example, estimates the user's emotions and adjusts the display layout based on the estimated user emotions. The user's emotions are estimated using technologies such as voice tone analysis and facial expression recognition. For example, if the user is nervous, the generation AI can provide a simple, highly visible layout. If the user is relaxed, the generation AI can provide a layout including detailed information. If the user is in a hurry, the generation AI can provide a layout that focuses on the main points. This improves visibility by adjusting the display layout according to the user's emotions. Some or all of the above-described processing in the display unit can be performed using the generation AI. For example, the display unit can input the user's emotion data into the generation AI and have the generation AI adjust the display layout.

[0083] The display unit can track the user's gaze during display and dynamically adjust the display position based on the gaze. For example, the display unit can track the user's gaze during display and dynamically adjust the display position based on the gaze. The gaze is tracked using, for example, gaze tracking technology or a gaze data analysis method. For example, the display unit can have the generation AI track the user's gaze and display important information at the gaze point. The display unit can also have the generation AI analyze the user's gaze movement and dynamically display information in accordance with the gaze movement. The display unit can also measure the concentration of the user's gaze and display important information at the focused area. This allows the display position to be dynamically adjusted based on the user's gaze, thereby displaying important information at the gaze point. Some or all of the above-mentioned processing in the display unit can be performed using the generation AI. For example, the display unit can input gaze data to the generation AI and have the generation AI adjust the display position.

[0084] The display unit can select the optimal display method by referring to the user's past viewing history when displaying. For example, the display unit selects the optimal display method by referring to the user's past viewing history when displaying. The viewing history is referenced, for example, using a viewing history database or a method of analyzing history data. For example, the display unit allows the generation AI to analyze the user's past viewing history and provide the optimal display layout. The display unit can also display using the user's preferred fonts and colors based on the user's viewing history. The display unit can also select a visually easy-to-understand display method by referring to the user's past viewing history. In this way, by referring to the user's past viewing history, a more visually easy-to-understand display method can be provided. Some or all of the above-mentioned processing in the display unit can be performed using the generation AI. For example, the display unit can input viewing history data to the generation AI and have the generation AI select the display method.

[0085] The display unit can estimate the user's emotions and determine display priorities based on the estimated user emotions. The display unit, for example, estimates the user's emotions and determines display priorities based on the estimated user emotions. The user's emotions are estimated using technologies such as voice tone analysis and facial expression recognition. For example, if the user is nervous, the generation AI can prioritize displaying important information. Also, if the user is relaxed, the generation AI can evenly display overall information. Also, if the user is in a hurry, the generation AI can prioritize displaying key points. This allows important information to be provided preferentially by determining display priorities according to the user's emotions. Some or all of the above-described processing in the display unit can be performed using the generation AI. For example, the display unit can input user emotion data into the generation AI and have the generation AI determine the display priorities.

[0086] The display unit can optimize the display method according to the resolution of the user's device when displaying. For example, the display unit optimizes the display method according to the resolution of the user's device when displaying. The resolution is identified using, for example, device resolution information or a resolution measurement method. For example, the display unit allows the generation AI to detect the resolution of the user's device and provide an optimal display layout. The display unit can also allow the generation AI to display detailed information on a high-resolution device. The display unit can also allow the generation AI to display a simple, highly visible image on a low-resolution device. This improves visibility by optimizing the display method according to the resolution of the user's device. Some or all of the above-mentioned processing in the display unit is performed using the generation AI. For example, the display unit can input device resolution information to the generation AI and cause the generation AI to optimize the display method.

[0087] The display unit can adjust the brightness and contrast of the display according to the user's ambient light during display. For example, the display unit adjusts the brightness and contrast of the display according to the user's ambient light during display. Ambient light is identified using, for example, an ambient light sensor or a light measurement method. For example, the display unit has a generation AI that detects ambient light and automatically adjusts the brightness. The display unit can also dynamically adjust the contrast according to changes in ambient light. The display unit can also have the generation AI display a brighter image in a dark environment and a darker image in a bright environment. This improves visibility by adjusting the brightness and contrast of the display according to the user's ambient light. Some or all of the above-mentioned processing in the display unit is performed using the generation AI. For example, the display unit can input ambient light data to the generation AI and have the generation AI adjust the brightness and contrast. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned voice analysis unit, sign language generation unit, visual information generation unit, and display unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the voice analysis unit is realized by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing device 12. The sign language generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The visual information generation unit is realized, for example, by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing device 12. The display unit is realized, for example, by the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned voice analysis unit, sign language generation unit, visual information generation unit, and display unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the voice analysis unit is realized by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The sign language generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The visual information generation unit is realized, for example, by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The display unit is realized, for example, by the speaker 240 and display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned voice analysis unit, sign language generation unit, visual information generation unit, and display unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the voice analysis unit is realized by the processor 46 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The sign language generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The visual information generation unit is realized, for example, by the processor 46 of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The display unit is realized, for example, by the display 343 and speaker 240 of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned voice analysis unit, sign language generation unit, visual information generation unit, and display unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the voice analysis unit is realized by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing device 12. The sign language generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The visual information generation unit is realized, for example, by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing device 12. The display unit is realized, for example, by the display and speaker 240 of the robot 414.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] In addition to analyzing the audio data, the audio analysis unit can detect specific keywords contained in the audio data and provide additional related information based on those keywords. For example, if the audio data of a news program contains the keyword "earthquake," the audio analysis unit can provide the latest information on earthquakes and disaster prevention measures. Similarly, if the audio data of a music program contains the keyword "new song," the audio analysis unit can provide information on the lyrics and artist of the new song. Furthermore, if the audio data of an educational program contains the keyword "history," the audio analysis unit can provide information on related historical events and people. This allows for the provision of more information to users based on the results of the analysis of the audio data.

[0090] In addition to generating sign language interpretations, the sign language generation unit can track the user's learning progress and provide an individual learning plan. For example, the sign language generation unit can provide a practice plan for the user to master specific sign language movements. The sign language generation unit can also suggest the next sign language movement to learn based on the user's learning history. Furthermore, the sign language generation unit can provide real-time feedback and guidance on correct sign language movements as the user practices sign language. This allows the user to learn sign language efficiently.

[0091] In addition to generating visual information, the visual information generation unit can provide customized visual information based on the user's visual preferences. For example, the visual information generation unit can generate visual information using the user's preferred colors and fonts. The visual information generation unit can also provide optimal visual information based on the user's past visual information usage history. Furthermore, the visual information generation unit can adjust the layout and design of the visual information according to the user's visual preferences. This allows the user to use the visual information more comfortably.

[0092] In addition to providing sign language interpretation and displaying visual information, the display unit can track the user's gaze and dynamically adjust the display position based on the user's gaze. For example, if the user is gazing at a specific part of the screen, the display unit can display information related to that part. The display unit can also analyze the user's gaze movement and dynamically display information in accordance with the gaze movement. Furthermore, the display unit can measure the concentration of the user's gaze and display important information in the part where the user is concentrating. This allows the user to efficiently obtain information based on their gaze.

[0093] In addition to displaying visual information, the display unit can adjust the brightness and contrast of the display according to the user's ambient light. For example, the display unit can display brighter in dark environments and darker in bright environments. The display unit can also dynamically adjust the contrast according to changes in ambient light. Furthermore, the display unit can automatically select optimal display settings based on the user's ambient light data. This allows the user to enjoy a highly visible display in any environment.

[0094] The voice analysis unit can estimate the user's emotions and adjust the accuracy of voice analysis based on the estimated user emotions. For example, if the user is nervous, the voice analysis unit can cause the generation AI to increase the accuracy of voice analysis and reduce misrecognition. Also, if the user is relaxed, the voice analysis unit can cause the generation AI to maintain the accuracy of voice analysis at a normal level. Furthermore, if the user is in a hurry, the voice analysis unit can cause the generation AI to prioritize the speed of voice analysis and provide analysis results quickly. This allows the voice analysis accuracy to be adjusted according to the user's emotions, making it possible to provide more accurate analysis results.

[0095] The sign language generation unit can estimate the user's emotions and adjust the sign language expression method based on the estimated user's emotions. For example, if the user is nervous, the sign language generation unit can cause the generation AI to slow down the sign language movements to make them easier to understand. Also, if the user is relaxed, the sign language generation unit can cause the generation AI to perform the sign language movements at a normal speed. Furthermore, if the user is excited, the sign language generation unit can cause the generation AI to emphasize the sign language movements to make them visually easier to understand. In this way, by adjusting the sign language expression method according to the user's emotions, it is possible to provide sign language interpretation that is easier to understand.

[0096] The visual information generation unit can estimate the user's emotions and adjust the display method of visual information based on the estimated user's emotions. For example, if the user is nervous, the visual information generation unit can generate simple, highly visible visual information using the generation AI. If the user is relaxed, the visual information generation unit can generate detailed visual information. If the user is in a hurry, the visual information generation unit can generate visual information that focuses on the main points. This makes it possible to provide visual information that is easier to understand by adjusting the display method of visual information according to the user's emotions.

[0097] The display unit can estimate the user's emotions and adjust the display layout based on the estimated user's emotions. For example, if the user is nervous, the generation AI can provide a simple, highly visible layout. If the user is relaxed, the generation AI can provide a layout including detailed information. If the user is in a hurry, the generation AI can provide a layout that focuses on the main points. This improves visibility by adjusting the display layout according to the user's emotions.

[0098] The display unit can estimate the user's emotions and determine display priorities based on the estimated user's emotions. For example, if the user is nervous, the generation AI can prioritize displaying important information. Also, if the user is relaxed, the generation AI can display overall information evenly. Furthermore, if the user is in a hurry, the generation AI can prioritize displaying key points. In this way, by determining display priorities according to the user's emotions, important information can be provided preferentially.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The audio analysis unit analyzes the audio data of a television program. This audio data includes audio file formats and real-time audio data. The audio analysis unit converts the audio data into text data using speech recognition technology and natural language processing technology. For example, the audio data of a news program is analyzed and its contents are converted into text data. Step 2: The sign language generation unit uses a generation AI to generate a sign language interpreter based on the text data analyzed by the speech analysis unit. The sign language interpreter is generated based on the type of sign language and a generation algorithm. For example, a deep learning model is used to generate the sign language interpreter. Step 3: The visual information generation unit uses a generative AI to generate visual information based on the text data analyzed by the audio analysis unit. The visual information is generated in the form of images, videos, animations, etc. For example, the visual information is generated using a deep learning model. Step 4: The display unit displays the information generated by the sign language generation unit and the visual information generation unit on the television screen. For example, the generated sign language interpretation is displayed in one part of the screen, and the visual information is displayed as subtitles in the lower part of the screen.

[0101] 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.

[0102] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 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 voice analysis unit that analyzes voice data; a sign language generation unit that generates a sign language interpretation based on the text data analyzed by the voice analysis unit; a visual information generation unit that generates visual information based on the text data analyzed by the audio analysis unit; a display unit that displays the information generated by the sign language generation unit and the visual information generation unit. A system characterized by:

2. The sign language generation unit Generating sign language interpreters using generative AI 2. The system of claim 1.

3. The visual information generation unit Generate visual information using generative AI 2. The system of claim 1.

4. The display unit Display the generated sign language interpreter on a portion of the screen 2. The system of claim 1.

5. The display unit Display the generated visual information as subtitles 2. The system of claim 1.

6. The voice analysis unit Estimate the user's emotions and adjust the accuracy of voice analysis based on the estimated user emotions.

2. The system of claim 1.

7. The voice analysis unit When analyzing audio, filtering is performed to remove background noise.

2. The system of claim 1.

8. The voice analysis unit During voice analysis, speakers are identified and different analysis algorithms are applied to each speaker.

2. The system of claim 1.

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