System

Abstract

The system according to the embodiment aims to convert speech into sign language and provide a video of a sign language interpreter in real time.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a voice input. The analysis unit analyzes the voice accepted by the acceptance unit and converts the voice into sign language. The generation unit generates a video of the sign language interpreter based on the sign language generated by the analysis unit. The providing unit provides the image generated by the generating unit in real time.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to convert speech into sign language and provide video of a sign language interpreter in real time.

[0005] The system according to the embodiment aims to convert speech into sign language and provide a video of a sign language interpreter in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives voice input. The analysis unit analyzes the voice received by the reception unit and converts it into sign language. The generation unit generates a video of a sign language interpreter based on the sign language generated by the analysis unit. The provision unit provides the video generated by the generation unit in real time. [Effects of the Invention]

[0007] The system according to the embodiment can convert speech into sign language and provide a video of a sign language interpreter in real time. [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 uses a generation AI to create a video of a sign language interpreter in real time. This system accepts audio input, and the generation AI analyzes the audio, converts it into sign language, and generates a video of the sign language interpreter, which is then provided in real time. For example, in a meeting or lecture, the speaker's voice is picked up by a microphone and input into the system. This audio is input into the generation AI, which analyzes the audio data and converts it into sign language. For example, the AI ​​analyzes the audio of the word "hello" and generates the corresponding sign language. Based on the generated sign language, the generation AI generates a video of the sign language interpreter. For example, it generates a video of the sign language interpreter signing "hello." This video is created in real time and provided to the user. For example, in a meeting or lecture, the speaker's voice is converted into a video of the sign language interpreter in real time, and the video is provided to the hearing impaired. This allows hearing impaired people to receive information in real time.

[0029] A sign language interpretation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives audio input. For example, in a situation such as a meeting or a lecture, the speaker's voice can be picked up by a microphone and input into the system. The analysis unit uses a generation AI to analyze the voice received by the reception unit and convert it into sign language. For example, the generation AI can analyze audio data and convert the voice saying "hello" into sign language. The generation unit uses the generation AI to generate a video of a sign language interpreter based on the sign language generated by the analysis unit. For example, the generation AI can generate a video of the sign language interpreter signing "hello." The provision unit provides the video generated by the generation unit in real time. For example, in a situation such as a meeting or a lecture, the speaker's voice is converted into a video of the sign language interpreter in real time and provided to a hearing-impaired person. In this way, the sign language interpretation system according to an embodiment can convert voice into sign language and provide a video of the sign language interpreter in real time.

[0030] The reception unit can receive voice using a microphone or other voice input device. Voice input devices include, but are not limited to, for example, a microphone, a smartphone, a headset, etc. The reception unit can receive voice using, for example, a microphone. The reception unit can also receive voice using a microphone of a smartphone. The reception unit can also receive voice using a microphone of a headset. This makes it possible to receive voice from a variety of voice input devices.

[0031] The analysis unit can analyze speech using speech recognition technology or natural language processing technology and convert the speech into sign language. Examples of speech recognition technology include, but are not limited to, deep learning-based speech recognition and HMM-based speech recognition. The analysis unit can analyze speech using, for example, deep learning-based speech recognition technology. The analysis unit can also analyze speech using HMM-based speech recognition technology. Examples of natural language processing technology include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can analyze speech using, for example, morphological analysis. The analysis unit can also analyze speech using grammatical analysis. The analysis unit can also analyze speech using semantic analysis. This allows speech to be accurately analyzed and converted into sign language.

[0032] The generation unit may reproduce the movements of the sign language interpreter using 3D modeling technology or animation technology. 3D modeling technology includes, but is not limited to, polygon modeling, sculpting, etc. The generation unit may reproduce the movements of the sign language interpreter using, for example, polygon modeling technology. The generation unit may also reproduce the movements of the sign language interpreter using sculpting technology. Animation technology includes, but is not limited to, keyframe animation, motion capture, etc. The generation unit may reproduce the movements of the sign language interpreter using, for example, keyframe animation technology. The generation unit may also reproduce the movements of the sign language interpreter using motion capture technology. This allows the movements of the sign language interpreter to be reproduced realistically.

[0033] The providing unit can provide the generated video to the user in real time. Specific methods for providing the video in real time include, but are not limited to, for example, setting an acceptable range for delay time and selecting a communication technology to be used. For example, the providing unit can set an acceptable range for delay time and provide the video in real time. The providing unit can also select a communication technology to be used and provide the video in real time. This allows the generated video to be provided in real time.

[0034] The reception unit can add a filtering function that automatically removes background noise when voice is input. Specific types of background noise include, but are not limited to, environmental sounds and background noise. For example, the reception unit can analyze ambient noise in real time when voice is input and perform noise cancellation. The reception unit can also filter noise in a specific frequency band when voice is input to improve voice clarity. Furthermore, the reception unit can perform dynamic noise filtering when voice is input and adjust the strength of noise removal according to changes in the environment. This can remove background noise and improve voice clarity.

[0035] The reception unit can analyze the characteristics of the speaker's voice when inputting voice and select an optimal voice recognition model. Specific methods for analyzing the characteristics of the speaker's voice include, but are not limited to, for example, voice pitch, tone, and speed. The reception unit can analyze, for example, the pitch and tone of the speaker's voice when inputting voice and select an optimal voice recognition model. The reception unit can also analyze the accent and dialect of the speaker when inputting voice and select a corresponding voice recognition model. Furthermore, the reception unit can analyze the speed and rhythm of the speaker's voice when inputting voice and select an optimal voice recognition model. Specific types of voice recognition models include, but are not limited to, for example, deep learning models and HMM models. This makes it possible to select an optimal voice recognition model based on the characteristics of the speaker's voice.

[0036] The reception unit can improve the accuracy of the voice by simultaneously using multiple voice input devices when inputting voice. Examples of the multiple voice input devices include, but are not limited to, a microphone, a smartphone, a headset, etc. The reception unit can, for example, use multiple microphones to collect voice from different angles and improve the accuracy of the voice. The reception unit can also improve the clarity of the voice by simultaneously using a headset and a desktop microphone. Furthermore, the reception unit can also improve the accuracy of the voice by simultaneously using a smartphone and an external microphone. This allows the use of multiple voice input devices to improve the accuracy of the voice.

[0037] When a voice is input, the reception unit can preferentially accept highly relevant voices in consideration of the speaker's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data, location information services, etc. For example, if the speaker is in a specific area, the reception unit can preferentially accept voices corresponding to the dialect or accent of that area. Furthermore, if the speaker is moving, the reception unit can preferentially accept highly relevant voices based on the speaker's current geographical location information. Furthermore, if the speaker is at a specific event venue, the reception unit can preferentially accept voices related to the event. This makes it possible to preferentially accept highly relevant voices based on the speaker's geographical location information.

[0038] The reception unit can analyze the speaker's social media activity when inputting voice and preferentially accept related voice. Specific methods for analyzing social media activity include, but are not limited to, for example, the content of posts, the number of likes, and comments. The reception unit can, for example, preferentially accept words and phrases that the speaker frequently uses on social media. The reception unit can also analyze the content of the speaker's posts on social media and preferentially accept related voice. Furthermore, the reception unit can also preferentially accept related voice with reference to the activity of the speaker's friends on social media. This makes it possible to preferentially accept related voice based on the speaker's social media activity.

[0039] The reception unit can customize the voice input method by reflecting the speaker's past feedback when inputting voice. Specific methods for acquiring past feedback include, but are not limited to, user reviews, survey results, etc. For example, the reception unit can adjust the sensitivity and priority of the voice input based on the speaker's past feedback. The reception unit can also customize the voice input interface by referring to the speaker's past feedback. Furthermore, the reception unit can adjust noise filtering settings for the voice input based on the speaker's past feedback. This makes it possible to customize the voice input method based on the speaker's past feedback.

[0040] The analysis unit can add context analysis to understand the speaker's intention when analyzing the speech. Specific methods of context analysis include, but are not limited to, for example, the context before and after, related topics, etc. For example, the analysis unit can understand the speaker's intention by taking into account the speaker's past statements and context when analyzing the speech. The analysis unit can also understand the speaker's intention by taking into account the speaker's current situation and environment when analyzing the speech. Furthermore, the analysis unit can understand the meaning of specific phrases and words used by the speaker through context analysis when analyzing the speech. This makes it possible to add context analysis to understand the speaker's intention.

[0041] The analysis unit may add a multilingual support function to support different languages ​​and dialects during speech analysis. Specific implementation methods of the multilingual support function include, but are not limited to, a translation algorithm, a list of supported languages, and the like. For example, the analysis unit may automatically detect the language used by the speaker during speech analysis and use a corresponding language model. The analysis unit may also improve the accuracy of the analysis by taking into account the speaker's dialect or accent during speech analysis. Furthermore, the analysis unit may simultaneously analyze multiple languages ​​during speech analysis to provide optimal translation results. This allows the addition of a multilingual support function to support different languages ​​and dialects.

[0042] During speech analysis, the analysis unit can improve the accuracy of the analysis by referring to the speaker's past speech history. Specific methods for acquiring the past speech history include, but are not limited to, audio logs and text logs. For example, the analysis unit can analyze the meaning of specific phrases or words based on the speaker's past speech history. The analysis unit can also understand the context and improve the accuracy of the analysis by referring to the speaker's past speech history. Furthermore, the analysis unit can perform analysis related to a specific topic or theme based on the speaker's past speech history. This makes it possible to improve the accuracy of the analysis by referring to the speaker's past speech history.

[0043] During speech analysis, the analysis unit can improve the accuracy of the analysis by taking into account the speaker's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data, location information services, etc. For example, if the speaker is in a specific area, the analysis unit can perform speech analysis corresponding to the dialect or accent of that area. Furthermore, if the speaker is moving, the analysis unit can perform highly relevant speech analysis based on the speaker's current geographical location information. Furthermore, if the speaker is at a specific event venue, the analysis unit can perform speech analysis related to the event. This can improve the accuracy of the analysis based on the speaker's geographical location information.

[0044] During speech analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the speaker. Specific methods for acquiring related literature include, but are not limited to, academic papers, technical reports, etc. For example, when a speaker is talking about a specific topic, the analysis unit can analyze by referring to literature related to that topic. The analysis unit can also improve the accuracy of the analysis by comparing the speaker's past speech history with related literature. Furthermore, the analysis unit can analyze by referring to literature related to specific technical terms and phrases used by the speaker. In this way, the analysis accuracy can be improved by referring to literature related to the speaker.

[0045] During voice analysis, the analysis unit can determine the analysis priority by taking into account the market value of the speaker. Specific evaluation criteria for market value include, but are not limited to, revenue, influence, popularity, etc. For example, if a speaker has high market value, the analysis unit can increase the priority of voice analysis and perform analysis quickly. The analysis unit can also optimally allocate analysis resources based on the speaker's market value. Furthermore, the analysis unit can also invest additional resources to improve analysis accuracy by taking into account the speaker's market value. This makes it possible to determine the analysis priority based on the speaker's market value.

[0046] When generating the actions of a sign language interpreter, the generation unit can customize the actions by taking into account the cultural background of the sign language. Specific methods of considering the cultural background include, but are not limited to, differences in sign language between regions and cultural gestures. For example, when generating the actions of a sign language interpreter, the generation unit can customize the actions by taking into account the sign language culture of a specific region or country. The generation unit can also customize the actions by taking into account the sign language culture of a specific community or group. Furthermore, the generation unit can customize the actions by taking into account the sign language culture according to a specific event or situation. This makes it possible to customize the actions by taking into account the cultural background of the sign language.

[0047] The generation unit can add a customization function to accommodate different sign language styles when generating the actions of the sign language interpreter. Specific types of sign language styles include, but are not limited to, American Sign Language (ASL) and Japanese Sign Language (JSL). For example, the generation unit can accommodate different sign language styles, such as American Sign Language (ASL) and Japanese Sign Language (JSL), when generating the actions of the sign language interpreter. The generation unit can also customize the actions to match the sign language style of the speaker. Furthermore, the generation unit can provide a customization option to accommodate a specific sign language style. This makes it possible to add a customization function to accommodate different sign language styles.

[0048] When generating the actions of the sign language interpreter, the generation unit can improve the accuracy of the actions by reflecting the user's past feedback. Specific methods for acquiring past feedback include, but are not limited to, user reviews and survey results. For example, the generation unit can adjust the actions of the sign language interpreter based on feedback provided by the user in the past. The generation unit can also improve the accuracy of the actions of the sign language interpreter by referring to the user's past feedback. Furthermore, the generation unit can provide customization options for the actions of the sign language interpreter based on the user's past feedback. This allows the accuracy of the actions to be improved based on the user's past feedback.

[0049] When generating the actions of the sign language interpreter, the generation unit can customize the actions by taking into account the user's geographical location information. Specific methods for acquiring the geographical location information include, but are not limited to, GPS data, location information services, etc. For example, if the user is in a specific area, the generation unit can generate actions corresponding to the sign language style of that area. Furthermore, if the user is moving, the generation unit can generate highly relevant sign language actions based on the user's current geographical location information. Furthermore, if the user is at a specific event venue, the generation unit can generate sign language actions related to the event. This makes it possible to customize the actions based on the user's geographical location information.

[0050] When generating the sign language interpreter's actions, the generation unit can analyze the user's social media activity to customize the actions. Specific methods for analyzing social media activity include, but are not limited to, post content, the number of likes, and comments. For example, the generation unit can prioritize the generation of sign language styles that the user frequently uses on social media. The generation unit can also analyze the content of the user's posts on social media and generate related sign language actions. Furthermore, the generation unit can generate related sign language actions by referring to the activities of the user's friends on social media. This makes it possible to customize actions based on the user's social media activity.

[0051] When generating the actions of the sign language interpreter, the generation unit can improve the accuracy of the actions by reflecting the user's past feedback. Specific methods for acquiring past feedback include, but are not limited to, user reviews and survey results. For example, the generation unit can adjust the actions of the sign language interpreter based on feedback provided by the user in the past. The generation unit can also improve the accuracy of the actions of the sign language interpreter by referring to the user's past feedback. Furthermore, the generation unit can provide customization options for the actions of the sign language interpreter based on the user's past feedback. This allows the accuracy of the actions to be improved based on the user's past feedback.

[0052] When providing video, the providing unit can select the optimal video format by taking into consideration the user's device information. Specific methods for acquiring device information include, but are not limited to, the device type, performance, and screen size. For example, if the user is using a smartphone, the providing unit can provide a video format that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a video format optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide a simple and highly visible video format. Specific types of video formats include, but are not limited to, MP4, AVI, MKV, and the like. This allows the optimal video format to be selected based on the user's device information.

[0053] The providing unit may analyze the user's network status and adjust the quality of the video when providing the video. Specific methods for acquiring the network status include, but are not limited to, bandwidth, delay, packet loss, etc. For example, if the user's network speed is slow, the providing unit may lower the resolution of the video to provide smooth playback. Furthermore, if the user's network speed is fast, the providing unit may provide high-resolution video. Furthermore, the providing unit may dynamically adjust the bit rate of the video according to the user's network status. Specific methods for adjusting the quality of the video include, but are not limited to, resolution, bit rate, frame rate, etc. This allows the quality of the video to be adjusted based on the user's network status.

[0054] When providing video, the providing unit can customize the providing method by referring to the user's past viewing history. Specific methods for acquiring the past viewing history include, but are not limited to, a list of viewed videos and viewing time. The providing unit can provide, for example, a preferred video format based on the user's past viewing history. The providing unit can also refer to the user's past viewing history and provide related videos preferentially. Furthermore, the providing unit can customize the video providing method based on the user's past viewing history. This makes it possible to customize the providing method based on the user's past viewing history.

[0055] When providing video, the providing unit can provide optimal video by taking into consideration the user's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data, location information services, etc. For example, when the user is in a specific area, the providing unit can provide video related to that area. Furthermore, when the user is moving, the providing unit can provide highly relevant video based on the user's current geographical location information. Furthermore, when the user is at a specific event venue, the providing unit can provide video related to that event. This makes it possible to provide optimal video based on the user's geographical location information.

[0056] When providing videos, the providing unit can analyze the user's social media activity and provide related videos. Specific methods for analyzing social media activity include, but are not limited to, for example, the content of posts, the number of likes, and comments. For example, the providing unit can prioritize providing the style of videos that the user frequently watches on social media. The providing unit can also analyze the content of posts made by the user on social media and provide related videos. Furthermore, the providing unit can provide related videos by referring to the activities of the user's friends on social media. This makes it possible to provide related videos based on the user's social media activity.

[0057] The providing unit can customize the video providing method by reflecting the user's past feedback when providing the video. Specific methods for acquiring past feedback include, but are not limited to, user reviews and survey results. The providing unit can adjust the video providing method, for example, based on the user's past feedback. The providing unit can also customize the video providing order by referring to the user's past feedback. Furthermore, the providing unit can adjust the quality and format of the video based on the user's past feedback. This makes it possible to customize the video providing method based on the user's past feedback.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can analyze the characteristics of the speaker's voice when inputting voice and select an optimal voice recognition model. Specific methods for analyzing the characteristics of the speaker's voice include, but are not limited to, for example, voice pitch, tone, and speed. The reception unit can analyze, for example, the pitch and tone of the speaker's voice when inputting voice and select an optimal voice recognition model. The reception unit can also analyze the accent and dialect of the speaker when inputting voice and select a corresponding voice recognition model. Furthermore, the reception unit can analyze the speed and rhythm of the speaker's voice when inputting voice and select an optimal voice recognition model. Specific types of voice recognition models include, but are not limited to, for example, deep learning models and HMM models. This makes it possible to select an optimal voice recognition model based on the characteristics of the speaker's voice.

[0060] The analysis unit can add context analysis to understand the speaker's intention when analyzing the speech. Specific methods of context analysis include, but are not limited to, for example, the context before and after, related topics, etc. For example, the analysis unit can understand the speaker's intention by taking into account the speaker's past statements and context when analyzing the speech. The analysis unit can also understand the speaker's intention by taking into account the speaker's current situation and environment when analyzing the speech. Furthermore, the analysis unit can understand the meaning of specific phrases and words used by the speaker through context analysis when analyzing the speech. This makes it possible to add context analysis to understand the speaker's intention.

[0061] When generating the actions of a sign language interpreter, the generation unit can customize the actions by taking into account the cultural background of the sign language. Specific methods of considering the cultural background include, but are not limited to, differences in sign language between regions and cultural gestures. For example, when generating the actions of a sign language interpreter, the generation unit can customize the actions by taking into account the sign language culture of a specific region or country. The generation unit can also customize the actions by taking into account the sign language culture of a specific community or group. Furthermore, the generation unit can customize the actions by taking into account the sign language culture according to a specific event or situation. This makes it possible to customize the actions by taking into account the cultural background of the sign language.

[0062] When providing video, the providing unit can select the optimal video format by taking into consideration the user's device information. Specific methods for acquiring device information include, but are not limited to, the device type, performance, and screen size. For example, if the user is using a smartphone, the providing unit can provide a video format that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a video format optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide a simple and highly visible video format. Specific types of video formats include, but are not limited to, MP4, AVI, MKV, and the like. This allows the optimal video format to be selected based on the user's device information.

[0063] The providing unit may analyze the user's network status and adjust the quality of the video when providing the video. Specific methods for acquiring the network status include, but are not limited to, bandwidth, delay, packet loss, etc. For example, if the user's network speed is slow, the providing unit may lower the resolution of the video to provide smooth playback. Furthermore, if the user's network speed is fast, the providing unit may provide high-resolution video. Furthermore, the providing unit may dynamically adjust the bit rate of the video according to the user's network status. Specific methods for adjusting the quality of the video include, but are not limited to, resolution, bit rate, frame rate, etc. This allows the quality of the video to be adjusted based on the user's network status.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The reception unit receives voice input. For example, in a meeting or lecture, the speaker's voice can be picked up by a microphone and input into the system. Step 2: The analysis unit uses the generation AI to analyze the voice received by the reception unit and convert it into sign language. For example, the generation AI can analyze the voice data and convert the voice saying "hello" into sign language. Step 3: The generation unit uses the generation AI to generate a video of a sign language interpreter based on the sign language generated by the analysis unit. For example, the generation AI can generate a video of a sign language interpreter signing "hello." Step 4: The providing unit provides the video generated by the generating unit in real time. For example, in a meeting or lecture, the speaker's voice is converted in real time into a video of a sign language interpreter and provided to a hearing-impaired person.

[0066] (Example 2) A system according to an embodiment of the present invention uses a generation AI to create a video of a sign language interpreter in real time. This system accepts audio input, and the generation AI analyzes the audio, converts it into sign language, and generates a video of the sign language interpreter, which is then provided in real time. For example, in a meeting or lecture, the speaker's voice is picked up by a microphone and input into the system. This audio is input into the generation AI, which analyzes the audio data and converts it into sign language. For example, the AI ​​analyzes the audio of the word "hello" and generates the corresponding sign language. Based on the generated sign language, the generation AI generates a video of the sign language interpreter. For example, it generates a video of the sign language interpreter signing "hello." This video is created in real time and provided to the user. For example, in a meeting or lecture, the speaker's voice is converted into a video of the sign language interpreter in real time, and the video is provided to the hearing impaired. This allows hearing impaired people to receive information in real time.

[0067] A sign language interpretation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives audio input. For example, in a situation such as a meeting or a lecture, the speaker's voice can be picked up by a microphone and input into the system. The analysis unit uses a generation AI to analyze the voice received by the reception unit and convert it into sign language. For example, the generation AI can analyze audio data and convert the voice saying "hello" into sign language. The generation unit uses the generation AI to generate a video of a sign language interpreter based on the sign language generated by the analysis unit. For example, the generation AI can generate a video of the sign language interpreter signing "hello." The provision unit provides the video generated by the generation unit in real time. For example, in a situation such as a meeting or a lecture, the speaker's voice is converted into a video of the sign language interpreter in real time and provided to a hearing-impaired person. In this way, the sign language interpretation system according to an embodiment can convert voice into sign language and provide a video of the sign language interpreter in real time.

[0068] The reception unit can receive voice using a microphone or other voice input device. Voice input devices include, but are not limited to, for example, a microphone, a smartphone, a headset, etc. The reception unit can receive voice using, for example, a microphone. The reception unit can also receive voice using a microphone of a smartphone. The reception unit can also receive voice using a microphone of a headset. This makes it possible to receive voice from a variety of voice input devices.

[0069] The analysis unit can analyze speech using speech recognition technology or natural language processing technology and convert the speech into sign language. Examples of speech recognition technology include, but are not limited to, deep learning-based speech recognition and HMM-based speech recognition. The analysis unit can analyze speech using, for example, deep learning-based speech recognition technology. The analysis unit can also analyze speech using HMM-based speech recognition technology. Examples of natural language processing technology include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can analyze speech using, for example, morphological analysis. The analysis unit can also analyze speech using grammatical analysis. The analysis unit can also analyze speech using semantic analysis. This allows speech to be accurately analyzed and converted into sign language.

[0070] The generation unit may reproduce the movements of the sign language interpreter using 3D modeling technology or animation technology. 3D modeling technology includes, but is not limited to, polygon modeling, sculpting, etc. The generation unit may reproduce the movements of the sign language interpreter using, for example, polygon modeling technology. The generation unit may also reproduce the movements of the sign language interpreter using sculpting technology. Animation technology includes, but is not limited to, keyframe animation, motion capture, etc. The generation unit may reproduce the movements of the sign language interpreter using, for example, keyframe animation technology. The generation unit may also reproduce the movements of the sign language interpreter using motion capture technology. This allows the movements of the sign language interpreter to be reproduced realistically.

[0071] The providing unit can provide the generated video to the user in real time. Specific methods for providing the video in real time include, but are not limited to, for example, setting an acceptable range for delay time and selecting a communication technology to be used. For example, the providing unit can set an acceptable range for delay time and provide the video in real time. The providing unit can also select a communication technology to be used and provide the video in real time. This allows the generated video to be provided in real time.

[0072] The reception unit can estimate the user's emotion and adjust the sensitivity of the voice input based on the estimated user's emotion. Specific methods for estimating the user's emotion include, but are not limited to, facial expression recognition and voice tone analysis. For example, the reception unit can estimate the user's emotion using facial expression recognition technology. The reception unit can also estimate the user's emotion using voice tone analysis technology. Specific methods for adjusting the sensitivity of the voice input include, but are not limited to, microphone sensitivity adjustment and noise filtering. For example, if the user is nervous, the reception unit can increase the sensitivity of the voice input to pick up the voice more clearly. If the user is relaxed, the reception unit can return the sensitivity of the voice input to normal to pick up natural voices. Furthermore, if the user is excited, the reception unit can adjust the sensitivity of the voice input to remove excessive noise and obtain clear voices. This makes it possible to adjust the sensitivity of the voice input according to the user's emotion.

[0073] The reception unit can add a filtering function that automatically removes background noise when voice is input. Specific types of background noise include, but are not limited to, environmental sounds and background noise. For example, the reception unit can analyze ambient noise in real time when voice is input and perform noise cancellation. The reception unit can also filter noise in a specific frequency band when voice is input to improve voice clarity. Furthermore, the reception unit can perform dynamic noise filtering when voice is input and adjust the strength of noise removal according to changes in the environment. This can remove background noise and improve voice clarity.

[0074] The reception unit can analyze the characteristics of the speaker's voice when inputting voice and select an optimal voice recognition model. Specific methods for analyzing the characteristics of the speaker's voice include, but are not limited to, for example, voice pitch, tone, and speed. The reception unit can analyze, for example, the pitch and tone of the speaker's voice when inputting voice and select an optimal voice recognition model. The reception unit can also analyze the accent and dialect of the speaker when inputting voice and select a corresponding voice recognition model. Furthermore, the reception unit can analyze the speed and rhythm of the speaker's voice when inputting voice and select an optimal voice recognition model. Specific types of voice recognition models include, but are not limited to, for example, deep learning models and HMM models. This makes it possible to select an optimal voice recognition model based on the characteristics of the speaker's voice.

[0075] The reception unit can improve the accuracy of the voice by simultaneously using multiple voice input devices when inputting voice. Examples of the multiple voice input devices include, but are not limited to, a microphone, a smartphone, a headset, etc. The reception unit can, for example, use multiple microphones to collect voice from different angles and improve the accuracy of the voice. The reception unit can also improve the clarity of the voice by simultaneously using a headset and a desktop microphone. Furthermore, the reception unit can also improve the accuracy of the voice by simultaneously using a smartphone and an external microphone. This allows the use of multiple voice input devices to improve the accuracy of the voice.

[0076] The reception unit can estimate the user's emotion and determine the priority of voice inputs based on the estimated user's emotion. Specific methods for estimating the user's emotion include, but are not limited to, facial expression recognition and voice tone analysis. For example, the reception unit can estimate the user's emotion using facial expression recognition technology. The reception unit can also estimate the user's emotion using voice tone analysis technology. Specific methods for determining the priority of voice inputs include, but are not limited to, importance and urgency. For example, if the user is nervous, the reception unit can increase the priority of the voice input to process it quickly. Furthermore, if the user is relaxed, the reception unit can return the priority of the voice input to normal and process it in parallel with other tasks. Furthermore, if the user is excited, the reception unit can adjust the priority of the voice input to remove excessive noise and obtain clear voice. This makes it possible to determine the priority of the voice inputs according to the user's emotion.

[0077] When a voice is input, the reception unit can preferentially accept highly relevant voices in consideration of the speaker's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data, location information services, etc. For example, if the speaker is in a specific area, the reception unit can preferentially accept voices corresponding to the dialect or accent of that area. Furthermore, if the speaker is moving, the reception unit can preferentially accept highly relevant voices based on the speaker's current geographical location information. Furthermore, if the speaker is at a specific event venue, the reception unit can preferentially accept voices related to the event. This makes it possible to preferentially accept highly relevant voices based on the speaker's geographical location information.

[0078] The reception unit can analyze the speaker's social media activity when inputting voice and preferentially accept related voice. Specific methods for analyzing social media activity include, but are not limited to, for example, the content of posts, the number of likes, and comments. The reception unit can, for example, preferentially accept words and phrases that the speaker frequently uses on social media. The reception unit can also analyze the content of the speaker's posts on social media and preferentially accept related voice. Furthermore, the reception unit can also preferentially accept related voice with reference to the activity of the speaker's friends on social media. This makes it possible to preferentially accept related voice based on the speaker's social media activity.

[0079] The reception unit can customize the voice input method by reflecting the speaker's past feedback when inputting voice. Specific methods for acquiring past feedback include, but are not limited to, user reviews, survey results, etc. For example, the reception unit can adjust the sensitivity and priority of the voice input based on the speaker's past feedback. The reception unit can also customize the voice input interface by referring to the speaker's past feedback. Furthermore, the reception unit can adjust noise filtering settings for the voice input based on the speaker's past feedback. This makes it possible to customize the voice input method based on the speaker's past feedback.

[0080] The analysis unit can estimate the user's emotion and adjust the voice analysis algorithm based on the estimated user's emotion. Specific methods for estimating the user's emotion include, but are not limited to, facial expression recognition and voice tone analysis. For example, the analysis unit can estimate the user's emotion using facial expression recognition technology. The analysis unit can also estimate the user's emotion using voice tone analysis technology. Specific types of voice analysis algorithms include, but are not limited to, deep learning algorithms and HMM algorithms. For example, if the user is nervous, the analysis unit can set the voice analysis algorithm to high sensitivity and analyze clear voices. Furthermore, if the user is relaxed, the analysis unit can return the voice analysis algorithm to normal and analyze natural voices. Furthermore, if the user is excited, the analysis unit can adjust the voice analysis algorithm to remove excessive noise and analyze clear voices. This allows the voice analysis algorithm to be adjusted according to the user's emotion.

[0081] The analysis unit can add context analysis to understand the speaker's intention when analyzing the speech. Specific methods of context analysis include, but are not limited to, for example, the context before and after, related topics, etc. For example, the analysis unit can understand the speaker's intention by taking into account the speaker's past statements and context when analyzing the speech. The analysis unit can also understand the speaker's intention by taking into account the speaker's current situation and environment when analyzing the speech. Furthermore, the analysis unit can understand the meaning of specific phrases and words used by the speaker through context analysis when analyzing the speech. This makes it possible to add context analysis to understand the speaker's intention.

[0082] The analysis unit may add a multilingual support function to support different languages ​​and dialects during speech analysis. Specific implementation methods of the multilingual support function include, but are not limited to, a translation algorithm, a list of supported languages, and the like. For example, the analysis unit may automatically detect the language used by the speaker during speech analysis and use a corresponding language model. The analysis unit may also improve the accuracy of the analysis by taking into account the speaker's dialect or accent during speech analysis. Furthermore, the analysis unit may simultaneously analyze multiple languages ​​during speech analysis to provide optimal translation results. This allows the addition of a multilingual support function to support different languages ​​and dialects.

[0083] During speech analysis, the analysis unit can improve the accuracy of the analysis by referring to the speaker's past speech history. Specific methods for acquiring the past speech history include, but are not limited to, audio logs and text logs. For example, the analysis unit can analyze the meaning of specific phrases or words based on the speaker's past speech history. The analysis unit can also understand the context and improve the accuracy of the analysis by referring to the speaker's past speech history. Furthermore, the analysis unit can perform analysis related to a specific topic or theme based on the speaker's past speech history. This makes it possible to improve the accuracy of the analysis by referring to the speaker's past speech history.

[0084] The analysis unit can estimate the user's emotion and determine the priority of the voice analysis based on the estimated user's emotion. Specific methods for estimating the user's emotion include, but are not limited to, facial expression recognition and voice tone analysis. For example, the analysis unit can estimate the user's emotion using facial expression recognition technology. The analysis unit can also estimate the user's emotion using voice tone analysis technology. Specific methods for determining the priority of the voice analysis include, but are not limited to, importance and urgency. For example, if the user is nervous, the analysis unit can increase the priority of the voice analysis to perform analysis quickly. Furthermore, if the user is relaxed, the analysis unit can return the priority of the voice analysis to normal and perform analysis in parallel with other tasks. Furthermore, if the user is excited, the analysis unit can adjust the priority of the voice analysis to remove excessive noise and analyze clear voices. This makes it possible to determine the priority of the voice analysis according to the user's emotion.

[0085] During speech analysis, the analysis unit can improve the accuracy of the analysis by taking into account the speaker's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data, location information services, etc. For example, if the speaker is in a specific area, the analysis unit can perform speech analysis corresponding to the dialect or accent of that area. Furthermore, if the speaker is moving, the analysis unit can perform highly relevant speech analysis based on the speaker's current geographical location information. Furthermore, if the speaker is at a specific event venue, the analysis unit can perform speech analysis related to the event. This can improve the accuracy of the analysis based on the speaker's geographical location information.

[0086] During speech analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the speaker. Specific methods for acquiring related literature include, but are not limited to, academic papers, technical reports, etc. For example, when a speaker is talking about a specific topic, the analysis unit can analyze by referring to literature related to that topic. The analysis unit can also improve the accuracy of the analysis by comparing the speaker's past speech history with related literature. Furthermore, the analysis unit can analyze by referring to literature related to specific technical terms and phrases used by the speaker. In this way, the analysis accuracy can be improved by referring to literature related to the speaker.

[0087] During voice analysis, the analysis unit can determine the analysis priority by taking into account the market value of the speaker. Specific evaluation criteria for market value include, but are not limited to, revenue, influence, popularity, etc. For example, if a speaker has high market value, the analysis unit can increase the priority of voice analysis and perform analysis quickly. The analysis unit can also optimally allocate analysis resources based on the speaker's market value. Furthermore, the analysis unit can also invest additional resources to improve analysis accuracy by taking into account the speaker's market value. This makes it possible to determine the analysis priority based on the speaker's market value.

[0088] The generation unit can estimate the user's emotion and adjust the sign language interpreter's actions based on the estimated user's emotion. Specific methods for estimating the user's emotion include, but are not limited to, facial expression recognition and voice tone analysis. For example, the generation unit can estimate the user's emotion using facial expression recognition technology. The generation unit can also estimate the user's emotion using voice tone analysis technology. Specific methods for adjusting the sign language interpreter's actions include, but are not limited to, the speed of the action and the parts to emphasize. For example, if the user is nervous, the generation unit can slow down the sign language interpreter's actions. Also, if the user is relaxed, the generation unit can perform the sign language interpreter's actions at a normal pace. Furthermore, if the user is excited, the generation unit can speed up the sign language interpreter's actions to quickly convey information. This allows the sign language interpreter's actions to be adjusted according to the user's emotion.

[0089] When generating the actions of a sign language interpreter, the generation unit can customize the actions by taking into account the cultural background of the sign language. Specific methods of considering the cultural background include, but are not limited to, differences in sign language between regions and cultural gestures. For example, when generating the actions of a sign language interpreter, the generation unit can customize the actions by taking into account the sign language culture of a specific region or country. The generation unit can also customize the actions by taking into account the sign language culture of a specific community or group. Furthermore, the generation unit can customize the actions by taking into account the sign language culture according to a specific event or situation. This makes it possible to customize the actions by taking into account the cultural background of the sign language.

[0090] The generation unit can add a customization function to accommodate different sign language styles when generating the actions of the sign language interpreter. Specific types of sign language styles include, but are not limited to, American Sign Language (ASL) and Japanese Sign Language (JSL). For example, the generation unit can accommodate different sign language styles, such as American Sign Language (ASL) and Japanese Sign Language (JSL), when generating the actions of the sign language interpreter. The generation unit can also customize the actions to match the sign language style of the speaker. Furthermore, the generation unit can provide a customization option to accommodate a specific sign language style. This makes it possible to add a customization function to accommodate different sign language styles.

[0091] When generating the actions of the sign language interpreter, the generation unit can improve the accuracy of the actions by reflecting the user's past feedback. Specific methods for acquiring past feedback include, but are not limited to, user reviews and survey results. For example, the generation unit can adjust the actions of the sign language interpreter based on feedback provided by the user in the past. The generation unit can also improve the accuracy of the actions of the sign language interpreter by referring to the user's past feedback. Furthermore, the generation unit can provide customization options for the actions of the sign language interpreter based on the user's past feedback. This allows the accuracy of the actions to be improved based on the user's past feedback.

[0092] The generation unit can estimate the user's emotions and determine the priority of the sign language interpreter's actions based on the estimated user's emotions. Specific methods for estimating the user's emotions include, but are not limited to, facial expression recognition and voice tone analysis. For example, the generation unit can estimate the user's emotions using facial expression recognition technology. The generation unit can also estimate the user's emotions using voice tone analysis technology. Specific methods for determining the priority of the sign language interpreter's actions include, but are not limited to, importance and urgency. For example, if the user is nervous, the generation unit can increase the priority of the sign language interpreter's actions to quickly convey information. Furthermore, if the user is relaxed, the generation unit can return the priority of the sign language interpreter's actions to normal and generate actions in parallel with other tasks. Furthermore, if the user is excited, the generation unit can adjust the priority of the sign language interpreter's actions to remove excessive actions and convey clear information. This makes it possible to determine the priority of the sign language interpreter's actions according to the user's emotions.

[0093] When generating the actions of the sign language interpreter, the generation unit can customize the actions by taking into account the user's geographical location information. Specific methods for acquiring the geographical location information include, but are not limited to, GPS data, location information services, etc. For example, if the user is in a specific area, the generation unit can generate actions corresponding to the sign language style of that area. Furthermore, if the user is moving, the generation unit can generate highly relevant sign language actions based on the user's current geographical location information. Furthermore, if the user is at a specific event venue, the generation unit can generate sign language actions related to the event. This makes it possible to customize the actions based on the user's geographical location information.

[0094] When generating the sign language interpreter's actions, the generation unit can analyze the user's social media activity to customize the actions. Specific methods for analyzing social media activity include, but are not limited to, post content, the number of likes, and comments. For example, the generation unit can prioritize the generation of sign language styles that the user frequently uses on social media. The generation unit can also analyze the content of the user's posts on social media and generate related sign language actions. Furthermore, the generation unit can generate related sign language actions by referring to the activities of the user's friends on social media. This makes it possible to customize actions based on the user's social media activity.

[0095] When generating the actions of the sign language interpreter, the generation unit can improve the accuracy of the actions by reflecting the user's past feedback. Specific methods for acquiring past feedback include, but are not limited to, user reviews and survey results. For example, the generation unit can adjust the actions of the sign language interpreter based on feedback provided by the user in the past. The generation unit can also improve the accuracy of the actions of the sign language interpreter by referring to the user's past feedback. Furthermore, the generation unit can provide customization options for the actions of the sign language interpreter based on the user's past feedback. This allows the accuracy of the actions to be improved based on the user's past feedback.

[0096] The providing unit can estimate the user's emotions and adjust the video presentation method based on the estimated user's emotions. Specific methods for estimating the user's emotions include, but are not limited to, facial expression recognition and audio tone analysis. For example, the providing unit can estimate the user's emotions using facial expression recognition technology. The providing unit can also estimate the user's emotions using audio tone analysis technology. Specific methods for adjusting the video presentation method include, but are not limited to, video resolution and frame rate. For example, if the user is nervous, the providing unit can simplify the video presentation method to reduce visual stress. Furthermore, if the user is relaxed, the providing unit can return the video presentation method to normal and provide natural videos. Furthermore, if the user is excited, the providing unit can adjust the video presentation method to remove excessive effects and provide clear videos. This makes it possible to adjust the video presentation method according to the user's emotions.

[0097] When providing video, the providing unit can select the optimal video format by taking into consideration the user's device information. Specific methods for acquiring device information include, but are not limited to, the device type, performance, and screen size. For example, if the user is using a smartphone, the providing unit can provide a video format that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a video format optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide a simple and highly visible video format. Specific types of video formats include, but are not limited to, MP4, AVI, MKV, and the like. This allows the optimal video format to be selected based on the user's device information.

[0098] The providing unit may analyze the user's network status and adjust the quality of the video when providing the video. Specific methods for acquiring the network status include, but are not limited to, bandwidth, delay, packet loss, etc. For example, if the user's network speed is slow, the providing unit may lower the resolution of the video to provide smooth playback. Furthermore, if the user's network speed is fast, the providing unit may provide high-resolution video. Furthermore, the providing unit may dynamically adjust the bit rate of the video according to the user's network status. Specific methods for adjusting the quality of the video include, but are not limited to, resolution, bit rate, frame rate, etc. This allows the quality of the video to be adjusted based on the user's network status.

[0099] When providing video, the providing unit can customize the providing method by referring to the user's past viewing history. Specific methods for acquiring the past viewing history include, but are not limited to, a list of viewed videos and viewing time. The providing unit can provide, for example, a preferred video format based on the user's past viewing history. The providing unit can also refer to the user's past viewing history and provide related videos preferentially. Furthermore, the providing unit can customize the video providing method based on the user's past viewing history. This makes it possible to customize the providing method based on the user's past viewing history.

[0100] The providing unit can estimate the user's emotion and determine the order in which the videos are presented based on the estimated user's emotion. Specific methods for estimating the user's emotion include, but are not limited to, facial expression recognition and audio tone analysis. For example, the providing unit can estimate the user's emotion using facial expression recognition technology. The providing unit can also estimate the user's emotion using audio tone analysis technology. Specific methods for determining the order in which the videos are presented include, but are not limited to, importance and urgency. For example, if the user is nervous, the providing unit can prioritize providing important information. Furthermore, if the user is relaxed, the providing unit can return the order in which the videos are presented to the normal order and present the videos in a natural order. Furthermore, if the user is excited, the providing unit can adjust the order in which the videos are presented to remove excessive information and provide clear information. This makes it possible to determine the order in which the videos are presented according to the user's emotion.

[0101] When providing video, the providing unit can provide optimal video by taking into consideration the user's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data, location information services, etc. For example, when the user is in a specific area, the providing unit can provide video related to that area. Furthermore, when the user is moving, the providing unit can provide highly relevant video based on the user's current geographical location information. Furthermore, when the user is at a specific event venue, the providing unit can provide video related to that event. This makes it possible to provide optimal video based on the user's geographical location information.

[0102] When providing videos, the providing unit can analyze the user's social media activity and provide related videos. Specific methods for analyzing social media activity include, but are not limited to, for example, the content of posts, the number of likes, and comments. For example, the providing unit can prioritize providing the style of videos that the user frequently watches on social media. The providing unit can also analyze the content of posts made by the user on social media and provide related videos. Furthermore, the providing unit can provide related videos by referring to the activities of the user's friends on social media. This makes it possible to provide related videos based on the user's social media activity.

[0103] The providing unit can customize the video providing method by reflecting the user's past feedback when providing the video. Specific methods for acquiring past feedback include, but are not limited to, user reviews and survey results. The providing unit can adjust the video providing method, for example, based on the user's past feedback. The providing unit can also customize the video providing order by referring to the user's past feedback. Furthermore, the providing unit can adjust the quality and format of the video based on the user's past feedback. This makes it possible to customize the video providing method based on the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives voice input using the microphone 38B of the smart device 14. The analysis unit analyzes the voice using a generation AI by the specific processing unit 290 of the data processing device 12 and converts it into sign language. The generation unit generates a video of a sign language interpreter using a generation AI by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated video in real time using the display 40A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives voice input using the microphone 238 of the smart glasses 214. The analysis unit analyzes the voice using a generation AI by the specific processing unit 290 of the data processing device 12 and converts it into sign language. The generation unit generates a video of a sign language interpreter using a generation AI by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated video in real time using the display of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives voice input using the microphone 238 of the headset-type terminal 314. The analysis unit analyzes the voice using a generation AI by the specific processing unit 290 of the data processing device 12 and converts it into sign language. The generation unit generates a video of a sign language interpreter using a generation AI by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated video in real time using the display 343 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives voice input using the microphone 238 of the robot 414. The analysis unit analyzes the voice using a generation AI by the specific processing unit 290 of the data processing device 12 and converts it into sign language. The generation unit generates a video of a sign language interpreter using a generation AI by the specific processing unit 290 of the data processing device 12. The provision unit provides the generated video in real time using a display of the robot 414.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The reception unit can analyze the characteristics of the speaker's voice when inputting voice and select an optimal voice recognition model. Specific methods for analyzing the characteristics of the speaker's voice include, but are not limited to, for example, voice pitch, tone, and speed. The reception unit can analyze, for example, the pitch and tone of the speaker's voice when inputting voice and select an optimal voice recognition model. The reception unit can also analyze the accent and dialect of the speaker when inputting voice and select a corresponding voice recognition model. Furthermore, the reception unit can analyze the speed and rhythm of the speaker's voice when inputting voice and select an optimal voice recognition model. Specific types of voice recognition models include, but are not limited to, for example, deep learning models and HMM models. This makes it possible to select an optimal voice recognition model based on the characteristics of the speaker's voice.

[0106] The analysis unit can add context analysis to understand the speaker's intention when analyzing the speech. Specific methods of context analysis include, but are not limited to, for example, the context before and after, related topics, etc. For example, the analysis unit can understand the speaker's intention by taking into account the speaker's past statements and context when analyzing the speech. The analysis unit can also understand the speaker's intention by taking into account the speaker's current situation and environment when analyzing the speech. Furthermore, the analysis unit can understand the meaning of specific phrases and words used by the speaker through context analysis when analyzing the speech. This makes it possible to add context analysis to understand the speaker's intention.

[0107] When generating the actions of a sign language interpreter, the generation unit can customize the actions by taking into account the cultural background of the sign language. Specific methods of considering the cultural background include, but are not limited to, differences in sign language between regions and cultural gestures. For example, when generating the actions of a sign language interpreter, the generation unit can customize the actions by taking into account the sign language culture of a specific region or country. The generation unit can also customize the actions by taking into account the sign language culture of a specific community or group. Furthermore, the generation unit can customize the actions by taking into account the sign language culture according to a specific event or situation. This makes it possible to customize the actions by taking into account the cultural background of the sign language.

[0108] When providing video, the providing unit can select the optimal video format by taking into consideration the user's device information. Specific methods for acquiring device information include, but are not limited to, the device type, performance, and screen size. For example, if the user is using a smartphone, the providing unit can provide a video format that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can provide a video format optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can provide a simple and highly visible video format. Specific types of video formats include, but are not limited to, MP4, AVI, MKV, and the like. This allows the optimal video format to be selected based on the user's device information.

[0109] The providing unit may analyze the user's network status and adjust the quality of the video when providing the video. Specific methods for acquiring the network status include, but are not limited to, bandwidth, delay, packet loss, etc. For example, if the user's network speed is slow, the providing unit may lower the resolution of the video to provide smooth playback. Furthermore, if the user's network speed is fast, the providing unit may provide high-resolution video. Furthermore, the providing unit may dynamically adjust the bit rate of the video according to the user's network status. Specific methods for adjusting the quality of the video include, but are not limited to, resolution, bit rate, frame rate, etc. This allows the quality of the video to be adjusted based on the user's network status.

[0110] The reception unit can estimate the user's emotion and adjust the sensitivity of the voice input based on the estimated user's emotion. Specific methods for estimating the user's emotion include, but are not limited to, facial expression recognition and voice tone analysis. For example, the reception unit can estimate the user's emotion using facial expression recognition technology. The reception unit can also estimate the user's emotion using voice tone analysis technology. Specific methods for adjusting the sensitivity of the voice input include, but are not limited to, microphone sensitivity adjustment and noise filtering. For example, if the user is nervous, the reception unit can increase the sensitivity of the voice input to pick up the voice more clearly. If the user is relaxed, the reception unit can return the sensitivity of the voice input to normal to pick up natural voices. Furthermore, if the user is excited, the reception unit can adjust the sensitivity of the voice input to remove excessive noise and obtain clear voices. This makes it possible to adjust the sensitivity of the voice input according to the user's emotion.

[0111] The analysis unit can estimate the user's emotion and adjust the voice analysis algorithm based on the estimated user's emotion. Specific methods for estimating the user's emotion include, but are not limited to, facial expression recognition and voice tone analysis. For example, the analysis unit can estimate the user's emotion using facial expression recognition technology. The analysis unit can also estimate the user's emotion using voice tone analysis technology. Specific types of voice analysis algorithms include, but are not limited to, deep learning algorithms and HMM algorithms. For example, if the user is nervous, the analysis unit can set the voice analysis algorithm to high sensitivity and analyze clear voices. Furthermore, if the user is relaxed, the analysis unit can return the voice analysis algorithm to normal and analyze natural voices. Furthermore, if the user is excited, the analysis unit can adjust the voice analysis algorithm to remove excessive noise and analyze clear voices. This allows the voice analysis algorithm to be adjusted according to the user's emotion.

[0112] The generation unit can estimate the user's emotion and adjust the sign language interpreter's actions based on the estimated user's emotion. Specific methods for estimating the user's emotion include, but are not limited to, facial expression recognition and voice tone analysis. For example, the generation unit can estimate the user's emotion using facial expression recognition technology. The generation unit can also estimate the user's emotion using voice tone analysis technology. Specific methods for adjusting the sign language interpreter's actions include, but are not limited to, the speed of the action and the parts to emphasize. For example, if the user is nervous, the generation unit can slow down the sign language interpreter's actions. Also, if the user is relaxed, the generation unit can perform the sign language interpreter's actions at a normal pace. Furthermore, if the user is excited, the generation unit can speed up the sign language interpreter's actions to quickly convey information. This allows the sign language interpreter's actions to be adjusted according to the user's emotion.

[0113] The providing unit can estimate the user's emotions and adjust the video presentation method based on the estimated user's emotions. Specific methods for estimating the user's emotions include, but are not limited to, facial expression recognition and audio tone analysis. For example, the providing unit can estimate the user's emotions using facial expression recognition technology. The providing unit can also estimate the user's emotions using audio tone analysis technology. Specific methods for adjusting the video presentation method include, but are not limited to, video resolution and frame rate. For example, if the user is nervous, the providing unit can simplify the video presentation method to reduce visual stress. Furthermore, if the user is relaxed, the providing unit can return the video presentation method to normal and provide natural videos. Furthermore, if the user is excited, the providing unit can adjust the video presentation method to remove excessive effects and provide clear videos. This makes it possible to adjust the video presentation method according to the user's emotions.

[0114] The providing unit can estimate the user's emotion and determine the order in which the videos are presented based on the estimated user's emotion. Specific methods for estimating the user's emotion include, but are not limited to, facial expression recognition and audio tone analysis. For example, the providing unit can estimate the user's emotion using facial expression recognition technology. The providing unit can also estimate the user's emotion using audio tone analysis technology. Specific methods for determining the order in which the videos are presented include, but are not limited to, importance and urgency. For example, if the user is nervous, the providing unit can prioritize providing important information. Furthermore, if the user is relaxed, the providing unit can return the order in which the videos are presented to the normal order and present the videos in a natural order. Furthermore, if the user is excited, the providing unit can adjust the order in which the videos are presented to remove excessive information and provide clear information. This makes it possible to determine the order in which the videos are presented according to the user's emotion.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The reception unit receives voice input. For example, in a meeting or lecture, the speaker's voice can be picked up by a microphone and input into the system. Step 2: The analysis unit uses the generation AI to analyze the voice received by the reception unit and convert it into sign language. For example, the generation AI can analyze the voice data and convert the voice saying "hello" into sign language. Step 3: The generation unit uses the generation AI to generate a video of a sign language interpreter based on the sign language generated by the analysis unit. For example, the generation AI can generate a video of a sign language interpreter signing "hello." Step 4: The providing unit provides the video generated by the generating unit in real time. For example, in a meeting or lecture, the speaker's voice is converted in real time into a video of a sign language interpreter and provided to a hearing-impaired person.

[0117] 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.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

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

[0122] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[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 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.

[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. 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.

[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 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.

[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 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.

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

[0135] 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.

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

[0138] 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.

[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 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.

[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 (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).

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

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 reception unit that receives voice input; an analysis unit that analyzes the voice received by the reception unit and converts it into sign language; a generation unit that generates an image of a sign language interpreter based on the sign language generated by the analysis unit; a providing unit that provides the video generated by the generating unit in real time. A system characterized by:

2. The reception unit Accepts audio using a microphone or other audio input device 2. The system of claim 1.

3. The analysis unit Use speech recognition or natural language processing technology to analyze speech and convert it into sign language 2. The system of claim 1.

4. The generation unit Recreate the movements of a sign language interpreter using 3D modeling or animation techniques 2. The system of claim 1.

5. The providing unit The generated video is provided to the user in real time 2. The system of claim 1.

6. The reception unit Estimate the user's emotion and adjust the sensitivity of the voice input based on the estimated user's emotion.

2. The system of claim 1.

7. The reception unit Add a filtering function to automatically remove background noise when inputting voice.

2. The system of claim 1.

8. The reception unit When voice input is performed, the speaker's voice characteristics are analyzed and a voice recognition model is selected.

2. The system of claim 1.

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