System

Abstract

An object of a system according to an embodiment is to recognize sound in a moving image and automatically generate a telop corresponding to multiple languages.SOLUTION: A system includes a voice recognition unit, a translation unit, and a telop generation unit. The voice recognition unit recognizes voice in the moving image and generates text data. The translation unit translates the text data generated by the speech recognition unit into multiple languages. The telop generation unit automatically generates a telop corresponding to each language based on the text data translated by the translation unit.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] With conventional technology, it was difficult to automatically generate multilingual subtitles from audio within a video, and manual work was required.

[0005] The system according to the embodiment aims to recognize audio in a video and automatically generate subtitles in multiple languages. [Means for solving the problem]

[0006] The system according to the embodiment includes a speech recognition unit, a translation unit, and a caption generation unit. The speech recognition unit recognizes speech in a video and generates text data. The translation unit translates the text data generated by the speech recognition unit into multiple languages. The caption generation unit automatically generates captions corresponding to each language based on the text data translated by the translation unit. [Effects of the Invention]

[0007] The system according to the embodiment can recognize audio in a video and automatically generate subtitles in multiple languages. [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 recognizes audio in a video and generates subtitles in multiple languages. This system recognizes audio in a video, extracts text data, translates the extracted text data into multiple languages, and automatically generates subtitles corresponding to each language. For example, the system recognizes audio in a video. In this process, the system uses speech recognition technology to convert the audio in the video into text data. For example, the system recognizes audio, such as conversation or narration, and generates corresponding text data. The generated text data is then translated into multiple languages. A generation AI is used for translation. The generation AI analyzes input text data and translates it into multiple languages. It can support various languages, such as English, Japanese, and French. Furthermore, it automatically generates subtitles corresponding to each language based on the translated text data. The subtitles function as subtitles displayed during video playback. For example, Japanese and French subtitles can be added to a video containing English audio. This allows the system to provide easily understandable content for viewers of different languages ​​when streaming videos on YouTube (registered trademark) or TikTok (registered trademark). In addition, when creating minutes or scripts, audio can be converted into text data and recorded in multiple languages. Furthermore, when creating instructional videos, subtitles in different languages ​​can be added to accommodate international audiences. This allows the system to recognize audio within videos and generate subtitles in multiple languages. For example, when distributing videos to YouTube or TikTok, the system can provide content that is easy to understand even if viewers speak different languages. In addition, when creating minutes or scripts, audio can be converted into text data and recorded in multiple languages. Furthermore, when creating instructional videos, subtitles in different languages ​​can be added to accommodate international audiences.

[0029] A caption generation system according to an embodiment includes a speech recognition unit, a translation unit, and a caption generation unit. The speech recognition unit recognizes speech in a video and generates text data. The speech recognition unit recognizes speech, such as conversation or narration, and generates corresponding text data. The speech recognition unit can convert the speech data into text data using a speech recognition algorithm. For example, the speech recognition unit can achieve high-accuracy speech recognition by using a speech recognition algorithm based on deep learning. The speech recognition unit can also remove background noise using noise filtering technology to obtain clear speech data. The translation unit uses a generation AI to translate the text data generated by the speech recognition unit into multiple languages. The translation unit can support multiple languages, such as English, Japanese, and French. The translation unit uses the generation AI to analyze input text data and translate it into multiple languages. For example, the generation AI uses a text generation AI (e.g., LLM) to translate the text data into multiple languages. The translation unit can also translate the text data into multiple languages ​​using a multimodal generation AI. For example, the generation AI analyzes input text data and translates it into multiple languages. The caption generation unit automatically generates captions corresponding to each language based on the text data translated by the translation unit. The caption generation unit can add captions in Japanese and French to a video containing English audio, for example. The caption generation unit uses the generation AI to automatically generate captions corresponding to each language based on the translated text data. For example, the generation AI uses a text generation AI to generate captions corresponding to each language based on the translated text data. As a result, the caption generation system according to the embodiment can recognize audio in a video and generate captions in multiple languages.

[0030] The speech recognition unit includes an acquisition unit that recognizes speech in the video and generates text data. The acquisition unit recognizes speech in the video and generates text data. The acquisition unit recognizes speech, such as conversation or narration, and generates corresponding text data. The acquisition unit can also convert speech data into text data using a speech recognition algorithm. For example, the acquisition unit can use a speech recognition algorithm that uses deep learning to achieve highly accurate speech recognition. Furthermore, the acquisition unit can use noise filtering technology to remove background noise and acquire clear speech data. This allows the speech recognition unit to recognize speech in the video and generate text data.

[0031] The translation unit includes a providing unit that translates the generated text data into multiple languages. The providing unit translates the generated text data into multiple languages. The providing unit can support multiple languages, such as English, Japanese, and French. The providing unit uses a generation AI to analyze input text data and translate it into multiple languages. For example, the providing unit uses a text generation AI (e.g., LLM) to translate the text data into multiple languages. The providing unit can also translate the text data into multiple languages ​​using a multimodal generation AI. For example, the providing unit analyzes input text data and translates it into multiple languages. This allows the translation unit to translate the generated text data into multiple languages.

[0032] The speech recognition unit can add a filtering function that automatically removes background noise during speech recognition. For example, when recognizing speech in a video, the speech recognition unit filters out ambient noise to obtain clear speech data. The speech recognition unit can also automatically remove background music during conversations to improve the accuracy of speech recognition. The speech recognition unit can also remove environmental noise, such as wind noise and traffic noise, in real time to perform accurate speech recognition. This removes background noise, thereby improving the accuracy of speech recognition. Some or all of the above-described processing in the speech recognition unit may be performed using, or without, AI, for example. For example, the speech recognition unit can input the acquired speech data to a generation AI and have the generation AI remove the background noise.

[0033] During speech recognition, the speech recognition unit can analyze the characteristics of a speaker's voice and apply a different recognition algorithm to each speaker. For example, the speech recognition unit can analyze the pitch and tone of the speaker's voice and apply an individual recognition algorithm. The speech recognition unit can also learn the speaker's accent and pronunciation habits to improve recognition accuracy. The speech recognition unit can also apply an optimal recognition algorithm taking into account the speed and rhythm of the speaker's voice. This improves recognition accuracy by applying a different recognition algorithm to each speaker. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input data on the speaker's voice characteristics to a generation AI and have the generation AI apply the recognition algorithm.

[0034] The speech recognition unit can add a function to automatically recognize technical terms according to the content of the video during speech recognition. For example, in medical-related videos, the speech recognition unit can automatically recognize medical terms and generate accurate text data. Furthermore, in technical videos, the speech recognition unit can automatically recognize technical terms and abbreviations and generate appropriate text data. Furthermore, in legal-related videos, the speech recognition unit can automatically recognize legal terms and generate accurate text data. This allows accurate text data to be generated by recognizing technical terms according to the content of the video. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input technical terminology data to a generation AI and have the generation AI recognize the technical terms.

[0035] The speech recognition unit can apply different recognition algorithms to each scene in a video during speech recognition. For example, the speech recognition unit can apply a dialogue-style recognition algorithm to an interview scene to generate accurate text data. The speech recognition unit can also apply a speech-style recognition algorithm to a presentation scene to generate appropriate text data. The speech recognition unit can also apply a narration-style recognition algorithm to a documentary scene to generate accurate text data. By applying different recognition algorithms to each scene in a video, recognition accuracy is improved. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input data for each scene to a generation AI and cause the generation AI to apply a different recognition algorithm to each scene.

[0036] The speech recognition unit can recognize regional accents and dialects by taking into account the user's geographical location information during speech recognition. For example, if the user is in the Kansai region, the speech recognition unit can recognize the Kansai dialect accent and dialect and generate accurate text data. Furthermore, if the user is in New York, the speech recognition unit can recognize New York-specific accents and slang and generate appropriate text data. Furthermore, if the user is in Paris, France, the speech recognition unit can recognize Parisian pronunciation and expressions and generate accurate text data. This allows accurate text data to be generated by recognizing regional accents and dialects. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input geographical location information to a generation AI and cause the generation AI to recognize regional accents and dialects.

[0037] During speech recognition, the speech recognition unit can improve recognition accuracy by referring to the user's past speech data. For example, the speech recognition unit can improve recognition accuracy by referring to speech data previously recorded by the user. The speech recognition unit can also learn the user's past pronunciation patterns and generate accurate text data. The speech recognition unit can also analyze the user's past speech data and apply a recognition algorithm corresponding to specific pronunciations or accents. This improves recognition accuracy by referring to the user's past speech data. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input past speech data into a generation AI and have the generation AI improve recognition accuracy.

[0038] The translation unit can automatically recognize technical terms and industry jargon during translation and provide an appropriate translation. For example, in medical-related text, the translation unit can automatically recognize medical terminology, and the generation AI can provide an accurate translation. In technical text, the translation unit can automatically recognize technical terms and abbreviations, and the generation AI can provide an appropriate translation. In legal-related text, the translation unit can automatically recognize legal terminology, and the generation AI can provide an accurate translation. This enables accurate translation by recognizing technical terms and industry jargon. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, AI, for example. For example, the translation unit can input technical terminology data into the generation AI and have the generation AI translate the technical terms.

[0039] The translation unit can apply an algorithm that generates a natural-looking translation by taking context into consideration during translation. For example, when translating a conversation, the translation unit uses a generation AI to translate in a natural dialogue format by taking context into consideration. Furthermore, when translating a technical document, the translation unit can use a generation AI to translate using specialized expressions by taking context into consideration. Furthermore, when translating a novel, the translation unit can use a generation AI to translate using emotive expressions by taking context into consideration. This enables a natural translation by taking context into consideration. Some or all of the above-mentioned processing in the translation unit may be performed using AI, for example, or may be performed without using AI. For example, the translation unit can input context data into the generation AI and cause the generation AI to generate a natural-looking translation.

[0040] The translation unit can add a function to generate multiple translation candidates and select the optimal translation during translation. For example, the translation unit has a generation AI that generates multiple translation candidates and the user selects the optimal translation. The translation unit can also have the generation AI automatically select the optimal translation candidate based on the context. The translation unit can also have the generation AI suggest the optimal translation candidate based on the user's past selection history. This allows the optimal translation to be selected by generating multiple translation candidates. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, AI, for example. For example, the translation unit can input translation candidate data to the generation AI and have the generation AI select the optimal translation candidate.

[0041] The translation unit can apply different translation algorithms depending on the genre of the video during translation. For example, the translation unit can have the generation AI perform translation using casual expressions for entertainment videos. Furthermore, the translation unit can have the generation AI perform translation using technical expressions for educational videos. Furthermore, the translation unit can have the generation AI perform translation using formal expressions for news videos. This enables appropriate translation by applying different translation algorithms depending on the genre of the video. Some or all of the above-mentioned processing in the translation unit may be performed using AI, for example, or may be performed without using AI. For example, the translation unit can input genre data to the generation AI and have the generation AI apply a translation algorithm depending on the genre.

[0042] During translation, the translation unit can improve translation accuracy by referring to the user's past translation history. For example, the translation unit can refer to a translation style previously selected by the user, and the generation AI can perform translation in a similar style. The translation unit can also analyze the user's past translation history, and the generation AI can perform translation by prioritizing specific expressions and terms. The translation unit can also apply an optimal translation algorithm based on the user's past translation history. This improves translation accuracy by referring to the user's past translation history. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, AI, for example. For example, the translation unit can input past translation history data into the generation AI and have the generation AI improve translation accuracy.

[0043] The translation unit can customize the translation style according to the user's language settings during translation. For example, if the user selects business language settings, the generation AI can perform translation using formal expressions. Furthermore, if the user selects casual language settings, the generation AI can perform translation using casual expressions. Furthermore, if the user selects academic language settings, the generation AI can perform translation using technical expressions. This enables more appropriate translation by customizing the translation style according to the user's language settings. Some or all of the above-described processing in the translation unit may be performed using AI, for example, or may be performed without using AI. For example, the translation unit can input language setting data into the generation AI and have the generation AI customize the translation style.

[0044] When generating subtitles, the subtitle generation unit can automatically adjust the display position of the subtitles according to the scene in the video. For example, in an interview scene, the subtitle generation unit displays the subtitles so as to avoid the speaker's face. Furthermore, in a presentation scene, the subtitle generation unit can display the subtitles according to the content of the slides. Furthermore, in a documentary scene, the subtitle generation unit can display the subtitles according to the content of the narration. In this way, by adjusting the display position of the subtitles according to the scene in the video, visibility is improved. Some or all of the above-mentioned processing in the subtitle generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the subtitle generation unit can input scene data to the generation AI and have the generation AI adjust the display position.

[0045] The caption generation unit can be added with a function to automatically select the font and color of captions when generating captions. The caption generation unit automatically selects an appropriate font to match the theme of the video. For example, an easy-to-read font is selected for an educational video. The caption generation unit can also automatically select a highly visible caption color to match the background color of the video. For example, a bright color caption is selected for a dark background. The caption generation unit can also automatically select a font and color to express emotions according to the content of the video. For example, a soft font and warm colors are selected for an emotional scene. This automatically selecting the font and color of the caption improves visibility. Some or all of the above-described processing in the caption generation unit may be performed using, or without, AI. For example, the caption generation unit can input font and color selection data into the generation AI and have the generation AI select the font and color.

[0046] The caption generation unit can add a function to generate multiple caption styles and select the optimal style when generating captions. In the caption generation unit, for example, a generation AI generates multiple caption styles and the user selects the optimal style. In the caption generation unit, the generation AI can automatically select the optimal caption style based on the content of the video. In the caption generation unit, the generation AI can suggest the optimal caption style based on the user's past selection history. This allows the optimal style to be selected by generating multiple caption styles. Some or all of the above-described processing in the caption generation unit may be performed using, or without, AI. For example, the caption generation unit can input caption style data to the generation AI and have the generation AI select the optimal caption style.

[0047] When generating subtitles, the subtitle generation unit can automatically adjust the length of the subtitles according to the content of the video. For example, in short scenes, the subtitle generation unit displays concise subtitles to quickly convey information to the viewer. In addition, in long scenes, the subtitle generation unit displays detailed subtitles to provide sufficient information to the viewer. Furthermore, the subtitle generation unit can automatically adjust the length of the subtitles appropriately according to the content of the video to provide optimal information to the viewer. In this way, optimal information can be provided to the viewer by adjusting the length of the subtitles according to the content of the video. Some or all of the above-described processing in the subtitle generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the subtitle generation unit can input content data of the video to the generation AI and have the generation AI adjust the length of the subtitles.

[0048] When generating captions, the caption generation unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the caption generation unit can display captions that fit the screen size. Furthermore, if the user is using a tablet, the caption generation unit can display captions optimized for a large screen. Furthermore, if the user is using a smartwatch, the caption generation unit can display captions that are concise and highly visible. This allows the optimal display method to be selected by taking into account the user's device information. Some or all of the above-described processing in the caption generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the caption generation unit can input device information data into the generation AI and cause the generation AI to select the optimal display method.

[0049] When generating captions, the caption generation unit can customize the display method by referring to the user's past caption settings. For example, the caption generation unit can refer to a caption style previously selected by the user and display the caption in a similar style. The caption generation unit can also analyze the user's past caption settings and prioritize specific fonts and colors when displaying captions. The caption generation unit can also suggest an optimal display method based on the user's past caption settings. This makes it possible to provide an optimal display method by referring to the user's past caption settings. Some or all of the above-described processing in the caption generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the caption generation unit can input past caption setting data into a generation AI and have the generation AI customize the display method.

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

[0051] During speech recognition, the speech recognition unit can analyze the characteristics of a speaker's voice and apply a different recognition algorithm to each speaker. For example, the speech recognition unit can analyze the pitch and tone of the speaker's voice and apply an individual recognition algorithm. The speech recognition unit can also learn the speaker's accent and pronunciation habits to improve recognition accuracy. The speech recognition unit can also apply an optimal recognition algorithm taking into account the speed and rhythm of the speaker's voice. This improves recognition accuracy by applying a different recognition algorithm to each speaker. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input data on the speaker's voice characteristics to a generation AI and have the generation AI apply the recognition algorithm.

[0052] The speech recognition unit can add a function to automatically recognize technical terms according to the content of the video during speech recognition. For example, in medical-related videos, the speech recognition unit can automatically recognize medical terms and generate accurate text data. Furthermore, in technical videos, the speech recognition unit can automatically recognize technical terms and abbreviations and generate appropriate text data. Furthermore, in legal-related videos, the speech recognition unit can automatically recognize legal terms and generate accurate text data. This allows accurate text data to be generated by recognizing technical terms according to the content of the video. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input technical terminology data to a generation AI and have the generation AI recognize the technical terms.

[0053] The speech recognition unit can apply different recognition algorithms to each scene in a video during speech recognition. For example, the speech recognition unit can apply a dialogue-style recognition algorithm to an interview scene to generate accurate text data. The speech recognition unit can also apply a speech-style recognition algorithm to a presentation scene to generate appropriate text data. The speech recognition unit can also apply a narration-style recognition algorithm to a documentary scene to generate accurate text data. By applying different recognition algorithms to each scene in a video, recognition accuracy is improved. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input data for each scene to a generation AI and cause the generation AI to apply a different recognition algorithm to each scene.

[0054] The speech recognition unit can recognize regional accents and dialects by taking into account the user's geographical location information during speech recognition. For example, if the user is in the Kansai region, the speech recognition unit can recognize the Kansai dialect accent and dialect and generate accurate text data. Furthermore, if the user is in New York, the speech recognition unit can recognize New York-specific accents and slang and generate appropriate text data. Furthermore, if the user is in Paris, France, the speech recognition unit can recognize Parisian pronunciation and expressions and generate accurate text data. This allows accurate text data to be generated by recognizing regional accents and dialects. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input geographical location information to a generation AI and cause the generation AI to recognize regional accents and dialects.

[0055] During speech recognition, the speech recognition unit can improve recognition accuracy by referring to the user's past speech data. For example, the speech recognition unit can improve recognition accuracy by referring to speech data previously recorded by the user. The speech recognition unit can also learn the user's past pronunciation patterns and generate accurate text data. The speech recognition unit can also analyze the user's past speech data and apply a recognition algorithm corresponding to specific pronunciations or accents. This improves recognition accuracy by referring to the user's past speech data. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input past speech data into a generation AI and have the generation AI improve recognition accuracy.

[0056] The translation unit can automatically recognize technical terms and industry jargon during translation and provide an appropriate translation. For example, in medical-related text, the translation unit can automatically recognize medical terminology, and the generation AI can provide an accurate translation. In technical text, the translation unit can automatically recognize technical terms and abbreviations, and the generation AI can provide an appropriate translation. In legal-related text, the translation unit can automatically recognize legal terminology, and the generation AI can provide an accurate translation. This enables accurate translation by recognizing technical terms and industry jargon. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, AI, for example. For example, the translation unit can input technical terminology data into the generation AI and have the generation AI translate the technical terms.

[0057] The translation unit can apply an algorithm that generates a natural-looking translation by taking context into consideration during translation. For example, when translating a conversation, the translation unit uses a generation AI to translate in a natural dialogue format by taking context into consideration. Furthermore, when translating a technical document, the translation unit can use a generation AI to translate using specialized expressions by taking context into consideration. Furthermore, when translating a novel, the translation unit can use a generation AI to translate using emotive expressions by taking context into consideration. This enables a natural translation by taking context into consideration. Some or all of the above-mentioned processing in the translation unit may be performed using AI, for example, or may be performed without using AI. For example, the translation unit can input context data into the generation AI and cause the generation AI to generate a natural-looking translation.

[0058] The translation unit can apply different translation algorithms depending on the genre of the video during translation. For example, the translation unit can have the generation AI perform translation using casual expressions for entertainment videos. Furthermore, the translation unit can have the generation AI perform translation using technical expressions for educational videos. Furthermore, the translation unit can have the generation AI perform translation using formal expressions for news videos. This enables appropriate translation by applying different translation algorithms depending on the genre of the video. Some or all of the above-mentioned processing in the translation unit may be performed using AI, for example, or may be performed without using AI. For example, the translation unit can input genre data to the generation AI and have the generation AI apply a translation algorithm depending on the genre.

[0059] During translation, the translation unit can improve translation accuracy by referring to the user's past translation history. For example, the translation unit can refer to a translation style previously selected by the user, and the generation AI can perform translation in a similar style. The translation unit can also analyze the user's past translation history, and the generation AI can perform translation by prioritizing specific expressions and terms. The translation unit can also apply an optimal translation algorithm based on the user's past translation history. This improves translation accuracy by referring to the user's past translation history. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, AI, for example. For example, the translation unit can input past translation history data into the generation AI and have the generation AI improve translation accuracy.

[0060] The translation unit can customize the translation style according to the user's language settings during translation. For example, if the user selects business language settings, the generation AI can perform translation using formal expressions. Furthermore, if the user selects casual language settings, the generation AI can perform translation using casual expressions. Furthermore, if the user selects academic language settings, the generation AI can perform translation using technical expressions. This enables more appropriate translation by customizing the translation style according to the user's language settings. Some or all of the above-described processing in the translation unit may be performed using AI, for example, or may be performed without using AI. For example, the translation unit can input language setting data into the generation AI and have the generation AI customize the translation style.

[0061] When generating subtitles, the subtitle generation unit can automatically adjust the display position of the subtitles according to the scene in the video. For example, in an interview scene, the subtitle generation unit displays the subtitles so as to avoid the speaker's face. Furthermore, in a presentation scene, the subtitle generation unit can display the subtitles according to the content of the slides. Furthermore, in a documentary scene, the subtitle generation unit can display the subtitles according to the content of the narration. In this way, by adjusting the display position of the subtitles according to the scene in the video, visibility is improved. Some or all of the above-mentioned processing in the subtitle generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the subtitle generation unit can input scene data to the generation AI and have the generation AI adjust the display position.

[0062] The caption generation unit can be added with a function to automatically select the font and color of captions when generating captions. The caption generation unit automatically selects an appropriate font to match the theme of the video. For example, an easy-to-read font is selected for an educational video. The caption generation unit can also automatically select a highly visible caption color to match the background color of the video. For example, a bright color caption is selected for a dark background. The caption generation unit can also automatically select a font and color to express emotions according to the content of the video. For example, a soft font and warm colors are selected for an emotional scene. This automatically selecting the font and color of the caption improves visibility. Some or all of the above-described processing in the caption generation unit may be performed using, or without, AI. For example, the caption generation unit can input font and color selection data into the generation AI and have the generation AI select the font and color.

[0063] The caption generation unit can add a function to generate multiple caption styles and select the optimal style when generating captions. In the caption generation unit, for example, a generation AI generates multiple caption styles and the user selects the optimal style. In the caption generation unit, the generation AI can automatically select the optimal caption style based on the content of the video. In the caption generation unit, the generation AI can suggest the optimal caption style based on the user's past selection history. This allows the optimal style to be selected by generating multiple caption styles. Some or all of the above-described processing in the caption generation unit may be performed using, or without, AI. For example, the caption generation unit can input caption style data to the generation AI and have the generation AI select the optimal caption style.

[0064] When generating subtitles, the subtitle generation unit can automatically adjust the length of the subtitles according to the content of the video. For example, in short scenes, the subtitle generation unit displays concise subtitles to quickly convey information to the viewer. In addition, in long scenes, the subtitle generation unit displays detailed subtitles to provide sufficient information to the viewer. Furthermore, the subtitle generation unit can automatically adjust the length of the subtitles appropriately according to the content of the video to provide optimal information to the viewer. In this way, optimal information can be provided to the viewer by adjusting the length of the subtitles according to the content of the video. Some or all of the above-described processing in the subtitle generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the subtitle generation unit can input content data of the video to the generation AI and have the generation AI adjust the length of the subtitles.

[0065] When generating captions, the caption generation unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the caption generation unit can display captions that fit the screen size. Furthermore, if the user is using a tablet, the caption generation unit can display captions optimized for a large screen. Furthermore, if the user is using a smartwatch, the caption generation unit can display captions that are concise and highly visible. This allows the optimal display method to be selected by taking into account the user's device information. Some or all of the above-described processing in the caption generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the caption generation unit can input device information data into the generation AI and cause the generation AI to select the optimal display method.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The speech recognition unit recognizes the audio in the video and generates text data. The speech recognition unit recognizes audio, such as conversations and narration, and generates corresponding text data. It also converts the audio data into text data using a speech recognition algorithm. For example, a speech recognition algorithm using deep learning is used to achieve highly accurate speech recognition. Furthermore, noise filtering technology is used to remove background noise and obtain clear audio data. Step 2: The translation unit uses a generation AI to translate the text data generated by the speech recognition unit into multiple languages. The translation unit supports multiple languages, including English, Japanese, and French. The generation AI uses a text generation AI (e.g., LLM) to translate the text data into multiple languages. It can also use a multimodal generation AI to translate the text data into multiple languages. Step 3: The caption generation unit automatically generates captions for each language based on the text data translated by the translation unit. For example, Japanese and French captions can be added to a video containing English audio. Using generation AI, captions for each language are automatically generated based on the translated text data.

[0068] (Example 2) A system according to an embodiment of the present invention recognizes audio in a video and generates subtitles in multiple languages. This system recognizes audio in a video, extracts text data, translates the extracted text data into multiple languages, and automatically generates subtitles corresponding to each language. For example, the system recognizes audio in a video. In this process, it uses speech recognition technology to convert the audio in the video into text data. For example, it recognizes audio, such as conversation or narration, and generates corresponding text data. The generated text data is then translated into multiple languages. A generation AI is used for translation. The generation AI analyzes input text data and translates it into multiple languages. It can support various languages, such as English, Japanese, and French. Furthermore, it automatically generates subtitles corresponding to each language based on the translated text data. The subtitles function as subtitles displayed during video playback. For example, Japanese and French subtitles can be added to a video containing English audio. This allows the system to provide easily understandable content for viewers of different languages ​​when streaming videos to YouTube or TikTok. Furthermore, when creating minutes or scripts, the system can convert audio into text data and record them in multiple languages. Furthermore, when creating instructional videos, adding subtitles in different languages ​​allows for international audiences. This allows the system to recognize audio in the video and generate subtitles in multiple languages. For example, when distributing videos to YouTube or TikTok, the system can provide content that is easy to understand even if viewers speak different languages. Also, when creating minutes or scripts, the system can convert audio into text data and record in multiple languages. Furthermore, when creating instructional videos, adding subtitles in different languages ​​allows for international audiences.

[0069] A caption generation system according to an embodiment includes a speech recognition unit, a translation unit, and a caption generation unit. The speech recognition unit recognizes speech in a video and generates text data. The speech recognition unit recognizes speech, such as conversation or narration, and generates corresponding text data. The speech recognition unit can convert the speech data into text data using a speech recognition algorithm. For example, the speech recognition unit can achieve high-accuracy speech recognition by using a speech recognition algorithm based on deep learning. The speech recognition unit can also remove background noise using noise filtering technology to obtain clear speech data. The translation unit uses a generation AI to translate the text data generated by the speech recognition unit into multiple languages. The translation unit can support multiple languages, such as English, Japanese, and French. The translation unit uses the generation AI to analyze input text data and translate it into multiple languages. For example, the generation AI uses a text generation AI (e.g., LLM) to translate the text data into multiple languages. The translation unit can also translate the text data into multiple languages ​​using a multimodal generation AI. For example, the generation AI analyzes input text data and translates it into multiple languages. The caption generation unit automatically generates captions corresponding to each language based on the text data translated by the translation unit. The caption generation unit can add captions in Japanese and French to a video containing English audio, for example. The caption generation unit uses the generation AI to automatically generate captions corresponding to each language based on the translated text data. For example, the generation AI uses a text generation AI to generate captions corresponding to each language based on the translated text data. As a result, the caption generation system according to the embodiment can recognize audio in a video and generate captions in multiple languages.

[0070] The speech recognition unit includes an acquisition unit that recognizes speech in the video and generates text data. The acquisition unit recognizes speech in the video and generates text data. The acquisition unit recognizes speech, such as conversation or narration, and generates corresponding text data. The acquisition unit can also convert speech data into text data using a speech recognition algorithm. For example, the acquisition unit can use a speech recognition algorithm that uses deep learning to achieve highly accurate speech recognition. Furthermore, the acquisition unit can use noise filtering technology to remove background noise and acquire clear speech data. This allows the speech recognition unit to recognize speech in the video and generate text data.

[0071] The translation unit includes a providing unit that translates the generated text data into multiple languages. The providing unit translates the generated text data into multiple languages. The providing unit can support multiple languages, such as English, Japanese, and French. The providing unit uses a generation AI to analyze input text data and translate it into multiple languages. For example, the providing unit uses a text generation AI (e.g., LLM) to translate the text data into multiple languages. The providing unit can also translate the text data into multiple languages ​​using a multimodal generation AI. For example, the providing unit analyzes input text data and translates it into multiple languages. This allows the translation unit to translate the generated text data into multiple languages.

[0072] The speech recognition unit can estimate the user's emotions and adjust the accuracy of speech recognition based on the estimated user emotions. For example, when the user is nervous, the speech recognition unit increases the sensitivity of speech recognition to recognize speech more accurately. Furthermore, when the user is relaxed, the speech recognition unit can set the sensitivity of speech recognition to normal to perform natural speech recognition. Furthermore, when the user is in a hurry, the speech recognition unit can increase the speech recognition speed to quickly generate text data. This enables more accurate speech recognition by adjusting the accuracy of speech recognition based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the speech recognition unit can input user emotion data into the generation AI and have the generation AI adjust the accuracy of speech recognition.

[0073] The speech recognition unit can add a filtering function that automatically removes background noise during speech recognition. For example, when recognizing speech in a video, the speech recognition unit filters out ambient noise to obtain clear speech data. The speech recognition unit can also automatically remove background music during conversations to improve the accuracy of speech recognition. The speech recognition unit can also remove environmental noise, such as wind noise and traffic noise, in real time to perform accurate speech recognition. This removes background noise, thereby improving the accuracy of speech recognition. Some or all of the above-described processing in the speech recognition unit may be performed using, or without, AI, for example. For example, the speech recognition unit can input the acquired speech data to a generation AI and have the generation AI remove the background noise.

[0074] During speech recognition, the speech recognition unit can analyze the characteristics of a speaker's voice and apply a different recognition algorithm to each speaker. For example, the speech recognition unit can analyze the pitch and tone of the speaker's voice and apply an individual recognition algorithm. The speech recognition unit can also learn the speaker's accent and pronunciation habits to improve recognition accuracy. The speech recognition unit can also apply an optimal recognition algorithm taking into account the speed and rhythm of the speaker's voice. This improves recognition accuracy by applying a different recognition algorithm to each speaker. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input data on the speaker's voice characteristics to a generation AI and have the generation AI apply the recognition algorithm.

[0075] The speech recognition unit can add a function to automatically recognize technical terms according to the content of the video during speech recognition. For example, in medical-related videos, the speech recognition unit can automatically recognize medical terms and generate accurate text data. Furthermore, in technical videos, the speech recognition unit can automatically recognize technical terms and abbreviations and generate appropriate text data. Furthermore, in legal-related videos, the speech recognition unit can automatically recognize legal terms and generate accurate text data. This allows accurate text data to be generated by recognizing technical terms according to the content of the video. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input technical terminology data to a generation AI and have the generation AI recognize the technical terms.

[0076] The speech recognition unit can estimate the user's emotion and adjust the timing of speech recognition based on the estimated user's emotion. For example, if the user is nervous, the speech recognition unit can delay the timing of speech recognition and perform recognition at a slower pace. Furthermore, if the user is relaxed, the speech recognition unit can perform speech recognition at a normal timing. Furthermore, if the user is in a hurry, the speech recognition unit can advance the timing of speech recognition and quickly generate text data. This enables more appropriate speech recognition by adjusting the timing of speech recognition based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the speech recognition unit can input user emotion data to the generation AI and cause the generation AI to adjust the timing of speech recognition.

[0077] The speech recognition unit can apply different recognition algorithms to each scene in a video during speech recognition. For example, the speech recognition unit can apply a dialogue-style recognition algorithm to an interview scene to generate accurate text data. The speech recognition unit can also apply a speech-style recognition algorithm to a presentation scene to generate appropriate text data. The speech recognition unit can also apply a narration-style recognition algorithm to a documentary scene to generate accurate text data. By applying different recognition algorithms to each scene in a video, recognition accuracy is improved. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input data for each scene to a generation AI and cause the generation AI to apply a different recognition algorithm to each scene.

[0078] The speech recognition unit can recognize regional accents and dialects by taking into account the user's geographical location information during speech recognition. For example, if the user is in the Kansai region, the speech recognition unit can recognize the Kansai dialect accent and dialect and generate accurate text data. Furthermore, if the user is in New York, the speech recognition unit can recognize New York-specific accents and slang and generate appropriate text data. Furthermore, if the user is in Paris, France, the speech recognition unit can recognize Parisian pronunciation and expressions and generate accurate text data. This allows accurate text data to be generated by recognizing regional accents and dialects. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input geographical location information to a generation AI and cause the generation AI to recognize regional accents and dialects.

[0079] During speech recognition, the speech recognition unit can improve recognition accuracy by referring to the user's past speech data. For example, the speech recognition unit can improve recognition accuracy by referring to speech data previously recorded by the user. The speech recognition unit can also learn the user's past pronunciation patterns and generate accurate text data. The speech recognition unit can also analyze the user's past speech data and apply a recognition algorithm corresponding to specific pronunciations or accents. This improves recognition accuracy by referring to the user's past speech data. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input past speech data into a generation AI and have the generation AI improve recognition accuracy.

[0080] The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can translate using soft expressions. If the user is nervous, the generation AI can translate using concise and clear expressions. If the user is excited, the generation AI can translate using expressions that emphasize the emotions. This allows for more appropriate translation by adjusting the translation expression based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the translation unit can be performed using AI, for example, or without AI. For example, the translation unit can input the user's emotion data into the generation AI and have the generation AI adjust the translation expression.

[0081] The translation unit can automatically recognize technical terms and industry jargon during translation and provide an appropriate translation. For example, in medical-related text, the translation unit can automatically recognize medical terminology, and the generation AI can provide an accurate translation. In technical text, the translation unit can automatically recognize technical terms and abbreviations, and the generation AI can provide an appropriate translation. In legal-related text, the translation unit can automatically recognize legal terminology, and the generation AI can provide an accurate translation. This enables accurate translation by recognizing technical terms and industry jargon. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, AI, for example. For example, the translation unit can input technical terminology data into the generation AI and have the generation AI translate the technical terms.

[0082] The translation unit can apply an algorithm that generates a natural-looking translation by taking context into consideration during translation. For example, when translating a conversation, the translation unit uses a generation AI to translate in a natural dialogue format by taking context into consideration. Furthermore, when translating a technical document, the translation unit can use a generation AI to translate using specialized expressions by taking context into consideration. Furthermore, when translating a novel, the translation unit can use a generation AI to translate using emotive expressions by taking context into consideration. This enables a natural translation by taking context into consideration. Some or all of the above-mentioned processing in the translation unit may be performed using AI, for example, or may be performed without using AI. For example, the translation unit can input context data into the generation AI and cause the generation AI to generate a natural-looking translation.

[0083] The translation unit can add a function to generate multiple translation candidates and select the optimal translation during translation. For example, the translation unit has a generation AI that generates multiple translation candidates and the user selects the optimal translation. The translation unit can also have the generation AI automatically select the optimal translation candidate based on the context. The translation unit can also have the generation AI suggest the optimal translation candidate based on the user's past selection history. This allows the optimal translation to be selected by generating multiple translation candidates. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, AI, for example. For example, the translation unit can input translation candidate data to the generation AI and have the generation AI select the optimal translation candidate.

[0084] The translation unit can estimate the user's emotions and determine translation priorities based on the estimated user emotions. For example, if the user is in a hurry, the translation unit can have the generation AI prioritize important parts in the translation. Furthermore, if the user is relaxed, the translation unit can have the generation AI translate the entire text evenly. Furthermore, if the user is excited, the translation unit can prioritize parts that emphasize emotions in the translation. Thus, by determining translation priorities based on the user's emotions, important parts can be translated with priority. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit can input the user's emotion data into the generation AI and have the generation AI determine the translation priorities.

[0085] The translation unit can apply different translation algorithms depending on the genre of the video during translation. For example, the translation unit can have the generation AI perform translation using casual expressions for entertainment videos. Furthermore, the translation unit can have the generation AI perform translation using technical expressions for educational videos. Furthermore, the translation unit can have the generation AI perform translation using formal expressions for news videos. This enables appropriate translation by applying different translation algorithms depending on the genre of the video. Some or all of the above-mentioned processing in the translation unit may be performed using AI, for example, or may be performed without using AI. For example, the translation unit can input genre data to the generation AI and have the generation AI apply a translation algorithm depending on the genre.

[0086] During translation, the translation unit can improve translation accuracy by referring to the user's past translation history. For example, the translation unit can refer to a translation style previously selected by the user, and the generation AI can perform translation in a similar style. The translation unit can also analyze the user's past translation history, and the generation AI can perform translation by prioritizing specific expressions and terms. The translation unit can also apply an optimal translation algorithm based on the user's past translation history. This improves translation accuracy by referring to the user's past translation history. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, AI, for example. For example, the translation unit can input past translation history data into the generation AI and have the generation AI improve translation accuracy.

[0087] The translation unit can customize the translation style according to the user's language settings during translation. For example, if the user selects business language settings, the generation AI can perform translation using formal expressions. Furthermore, if the user selects casual language settings, the generation AI can perform translation using casual expressions. Furthermore, if the user selects academic language settings, the generation AI can perform translation using technical expressions. This enables more appropriate translation by customizing the translation style according to the user's language settings. Some or all of the above-described processing in the translation unit may be performed using AI, for example, or may be performed without using AI. For example, the translation unit can input language setting data into the generation AI and have the generation AI customize the translation style.

[0088] The caption generation unit can estimate the user's emotions and adjust the caption display method based on the estimated user's emotions. For example, if the user is nervous, the caption generation unit can display simple, highly visible captions. Furthermore, if the user is relaxed, the caption generation unit can display captions that include detailed information. Furthermore, if the user is in a hurry, the caption generation unit can display captions that focus on the main points. By adjusting the caption display method based on the user's emotions, highly visible captions can be displayed. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the caption generation unit can be performed using, for example, AI, or without AI. For example, the caption generation unit can input user emotion data into the generation AI and have the generation AI adjust the caption display method.

[0089] When generating subtitles, the subtitle generation unit can automatically adjust the display position of the subtitles according to the scene in the video. For example, in an interview scene, the subtitle generation unit displays the subtitles so as to avoid the speaker's face. Furthermore, in a presentation scene, the subtitle generation unit can display the subtitles according to the content of the slides. Furthermore, in a documentary scene, the subtitle generation unit can display the subtitles according to the content of the narration. In this way, by adjusting the display position of the subtitles according to the scene in the video, visibility is improved. Some or all of the above-mentioned processing in the subtitle generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the subtitle generation unit can input scene data to the generation AI and have the generation AI adjust the display position.

[0090] The caption generation unit can be added with a function to automatically select the font and color of captions when generating captions. The caption generation unit automatically selects an appropriate font to match the theme of the video. For example, an easy-to-read font is selected for an educational video. The caption generation unit can also automatically select a highly visible caption color to match the background color of the video. For example, a bright color caption is selected for a dark background. The caption generation unit can also automatically select a font and color to express emotions according to the content of the video. For example, a soft font and warm colors are selected for an emotional scene. This automatically selecting the font and color of the caption improves visibility. Some or all of the above-described processing in the caption generation unit may be performed using, or without, AI. For example, the caption generation unit can input font and color selection data into the generation AI and have the generation AI select the font and color.

[0091] The caption generation unit can add a function to generate multiple caption styles and select the optimal style when generating captions. In the caption generation unit, for example, a generation AI generates multiple caption styles and the user selects the optimal style. In the caption generation unit, the generation AI can automatically select the optimal caption style based on the content of the video. In the caption generation unit, the generation AI can suggest the optimal caption style based on the user's past selection history. This allows the optimal style to be selected by generating multiple caption styles. Some or all of the above-described processing in the caption generation unit may be performed using, or without, AI. For example, the caption generation unit can input caption style data to the generation AI and have the generation AI select the optimal caption style.

[0092] The caption generation unit can estimate the user's emotions and adjust the display timing of the caption based on the estimated user's emotions. For example, if the user is nervous, the caption generation unit can delay the display timing of the caption and display it at a slower pace. Furthermore, if the user is relaxed, the caption generation unit can display the caption at a normal timing. Furthermore, if the user is in a hurry, the caption generation unit can advance the display timing of the caption to provide information quickly. This improves visibility by adjusting the display timing of the caption based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the caption generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the caption generation unit can input user's emotion data into the generation AI and cause the generation AI to adjust the display timing of the caption.

[0093] When generating subtitles, the subtitle generation unit can automatically adjust the length of the subtitles according to the content of the video. For example, in short scenes, the subtitle generation unit displays concise subtitles to quickly convey information to the viewer. In addition, in long scenes, the subtitle generation unit displays detailed subtitles to provide sufficient information to the viewer. Furthermore, the subtitle generation unit can automatically adjust the length of the subtitles appropriately according to the content of the video to provide optimal information to the viewer. In this way, optimal information can be provided to the viewer by adjusting the length of the subtitles according to the content of the video. Some or all of the above-described processing in the subtitle generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the subtitle generation unit can input content data of the video to the generation AI and have the generation AI adjust the length of the subtitles.

[0094] When generating captions, the caption generation unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the caption generation unit can display captions that fit the screen size. Furthermore, if the user is using a tablet, the caption generation unit can display captions optimized for a large screen. Furthermore, if the user is using a smartwatch, the caption generation unit can display captions that are concise and highly visible. This allows the optimal display method to be selected by taking into account the user's device information. Some or all of the above-described processing in the caption generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the caption generation unit can input device information data into the generation AI and cause the generation AI to select the optimal display method.

[0095] When generating captions, the caption generation unit can customize the display method by referring to the user's past caption settings. For example, the caption generation unit can refer to a caption style previously selected by the user and display the caption in a similar style. The caption generation unit can also analyze the user's past caption settings and prioritize specific fonts and colors when displaying captions. The caption generation unit can also suggest an optimal display method based on the user's past caption settings. This makes it possible to provide an optimal display method by referring to the user's past caption settings. Some or all of the above-described processing in the caption generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the caption generation unit can input past caption setting data into a generation AI and have the generation AI customize the display method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned speech recognition unit, translation unit, and caption generation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the speech recognition unit can recognize speech in a video using the microphone 38B of the smart device 14, and generate text data by the specific processing unit 290 of the data processing device 12. For example, the translation unit can translate the text data into multiple languages ​​using a generation AI by the specific processing unit 290 of the data processing device 12. For example, the caption generation unit can automatically generate captions corresponding to each language based on the text data translated by the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned speech recognition unit, translation unit, and caption generation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the speech recognition unit can recognize speech in a video using the microphone 238 of the smart glasses 214, and generate text data by the specific processing unit 290 of the data processing device 12. For example, the translation unit can translate the text data into multiple languages ​​using a generation AI by the specific processing unit 290 of the data processing device 12. For example, the caption generation unit can automatically generate captions corresponding to each language based on the text data translated by the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned speech recognition unit, translation unit, and caption generation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the speech recognition unit can recognize speech in a video using the microphone 238 of the headset-type terminal 314, and generate text data by the specific processing unit 290 of the data processing device 12. For example, the translation unit can translate the text data into multiple languages ​​using a generation AI by the specific processing unit 290 of the data processing device 12. For example, the caption generation unit can automatically generate captions corresponding to each language based on the text data translated by the control unit 46A of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned speech recognition unit, translation unit, and caption generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the speech recognition unit can recognize speech in a video using the microphone 238 of the robot 414, and generate text data by the specific processing unit 290 of the data processing device 12. For example, the translation unit can translate the text data into multiple languages ​​using a generation AI by the specific processing unit 290 of the data processing device 12. For example, the caption generation unit can automatically generate captions corresponding to each language based on the text data translated by the control unit 46A of the robot 414.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] During speech recognition, the speech recognition unit can analyze the characteristics of a speaker's voice and apply a different recognition algorithm to each speaker. For example, the speech recognition unit can analyze the pitch and tone of the speaker's voice and apply an individual recognition algorithm. The speech recognition unit can also learn the speaker's accent and pronunciation habits to improve recognition accuracy. The speech recognition unit can also apply an optimal recognition algorithm taking into account the speed and rhythm of the speaker's voice. This improves recognition accuracy by applying a different recognition algorithm to each speaker. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input data on the speaker's voice characteristics to a generation AI and have the generation AI apply the recognition algorithm.

[0098] The speech recognition unit can add a function to automatically recognize technical terms according to the content of the video during speech recognition. For example, in medical-related videos, the speech recognition unit can automatically recognize medical terms and generate accurate text data. Furthermore, in technical videos, the speech recognition unit can automatically recognize technical terms and abbreviations and generate appropriate text data. Furthermore, in legal-related videos, the speech recognition unit can automatically recognize legal terms and generate accurate text data. This allows accurate text data to be generated by recognizing technical terms according to the content of the video. Some or all of the above-mentioned processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input technical terminology data to a generation AI and have the generation AI recognize the technical terms.

[0099] The speech recognition unit can estimate the user's emotion and adjust the timing of speech recognition based on the estimated user's emotion. For example, if the user is nervous, the speech recognition unit can delay the timing of speech recognition and perform recognition at a slower pace. Furthermore, if the user is relaxed, the speech recognition unit can perform speech recognition at a normal timing. Furthermore, if the user is in a hurry, the speech recognition unit can advance the timing of speech recognition and quickly generate text data. This enables more appropriate speech recognition by adjusting the timing of speech recognition based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the speech recognition unit can input user emotion data to the generation AI and cause the generation AI to adjust the timing of speech recognition.

[0100] The speech recognition unit can apply different recognition algorithms to each scene in a video during speech recognition. For example, the speech recognition unit can apply a dialogue-style recognition algorithm to an interview scene to generate accurate text data. The speech recognition unit can also apply a speech-style recognition algorithm to a presentation scene to generate appropriate text data. The speech recognition unit can also apply a narration-style recognition algorithm to a documentary scene to generate accurate text data. By applying different recognition algorithms to each scene in a video, recognition accuracy is improved. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input data for each scene to a generation AI and cause the generation AI to apply a different recognition algorithm to each scene.

[0101] The speech recognition unit can recognize regional accents and dialects by taking into account the user's geographical location information during speech recognition. For example, if the user is in the Kansai region, the speech recognition unit can recognize the Kansai dialect accent and dialect and generate accurate text data. Furthermore, if the user is in New York, the speech recognition unit can recognize New York-specific accents and slang and generate appropriate text data. Furthermore, if the user is in Paris, France, the speech recognition unit can recognize Parisian pronunciation and expressions and generate accurate text data. This allows accurate text data to be generated by recognizing regional accents and dialects. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input geographical location information to a generation AI and cause the generation AI to recognize regional accents and dialects.

[0102] During speech recognition, the speech recognition unit can improve recognition accuracy by referring to the user's past speech data. For example, the speech recognition unit can improve recognition accuracy by referring to speech data previously recorded by the user. The speech recognition unit can also learn the user's past pronunciation patterns and generate accurate text data. The speech recognition unit can also analyze the user's past speech data and apply a recognition algorithm corresponding to specific pronunciations or accents. This improves recognition accuracy by referring to the user's past speech data. Some or all of the above-described processing in the speech recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the speech recognition unit can input past speech data into a generation AI and have the generation AI improve recognition accuracy.

[0103] The translation unit can estimate the user's emotions and adjust the translation expression based on the estimated user's emotions. For example, if the user is relaxed, the generation AI can translate using soft expressions. If the user is nervous, the generation AI can translate using concise and clear expressions. If the user is excited, the generation AI can translate using expressions that emphasize the emotions. This allows for more appropriate translation by adjusting the translation expression based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the translation unit can be performed using AI, for example, or without AI. For example, the translation unit can input the user's emotion data into the generation AI and have the generation AI adjust the translation expression.

[0104] The translation unit can automatically recognize technical terms and industry jargon during translation and provide an appropriate translation. For example, in medical-related text, the translation unit can automatically recognize medical terminology, and the generation AI can provide an accurate translation. In technical text, the translation unit can automatically recognize technical terms and abbreviations, and the generation AI can provide an appropriate translation. In legal-related text, the translation unit can automatically recognize legal terminology, and the generation AI can provide an accurate translation. This enables accurate translation by recognizing technical terms and industry jargon. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, AI, for example. For example, the translation unit can input technical terminology data into the generation AI and have the generation AI translate the technical terms.

[0105] The translation unit can apply an algorithm that generates a natural-looking translation by taking context into consideration during translation. For example, when translating a conversation, the translation unit uses a generation AI to translate in a natural dialogue format by taking context into consideration. Furthermore, when translating a technical document, the translation unit can use a generation AI to translate using specialized expressions by taking context into consideration. Furthermore, when translating a novel, the translation unit can use a generation AI to translate using emotive expressions by taking context into consideration. This enables a natural translation by taking context into consideration. Some or all of the above-mentioned processing in the translation unit may be performed using AI, for example, or may be performed without using AI. For example, the translation unit can input context data into the generation AI and cause the generation AI to generate a natural-looking translation.

[0106] The translation unit can estimate the user's emotions and determine translation priorities based on the estimated user emotions. For example, if the user is in a hurry, the translation unit can have the generation AI prioritize important parts in the translation. Furthermore, if the user is relaxed, the translation unit can have the generation AI translate the entire text evenly. Furthermore, if the user is excited, the translation unit can prioritize parts that emphasize emotions in the translation. Thus, by determining translation priorities based on the user's emotions, important parts can be translated with priority. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the translation unit can be performed using, for example, AI, or without AI. For example, the translation unit can input the user's emotion data into the generation AI and have the generation AI determine the translation priorities.

[0107] The translation unit can apply different translation algorithms depending on the genre of the video during translation. For example, the translation unit can have the generation AI perform translation using casual expressions for entertainment videos. Furthermore, the translation unit can have the generation AI perform translation using technical expressions for educational videos. Furthermore, the translation unit can have the generation AI perform translation using formal expressions for news videos. This enables appropriate translation by applying different translation algorithms depending on the genre of the video. Some or all of the above-mentioned processing in the translation unit may be performed using AI, for example, or may be performed without using AI. For example, the translation unit can input genre data to the generation AI and have the generation AI apply a translation algorithm depending on the genre.

[0108] During translation, the translation unit can improve translation accuracy by referring to the user's past translation history. For example, the translation unit can refer to a translation style previously selected by the user, and the generation AI can perform translation in a similar style. The translation unit can also analyze the user's past translation history, and the generation AI can perform translation by prioritizing specific expressions and terms. The translation unit can also apply an optimal translation algorithm based on the user's past translation history. This improves translation accuracy by referring to the user's past translation history. Some or all of the above-mentioned processing in the translation unit may be performed using, or without, AI, for example. For example, the translation unit can input past translation history data into the generation AI and have the generation AI improve translation accuracy.

[0109] The translation unit can customize the translation style according to the user's language settings during translation. For example, if the user selects business language settings, the generation AI can perform translation using formal expressions. Furthermore, if the user selects casual language settings, the generation AI can perform translation using casual expressions. Furthermore, if the user selects academic language settings, the generation AI can perform translation using technical expressions. This enables more appropriate translation by customizing the translation style according to the user's language settings. Some or all of the above-described processing in the translation unit may be performed using AI, for example, or may be performed without using AI. For example, the translation unit can input language setting data into the generation AI and have the generation AI customize the translation style.

[0110] The caption generation unit can estimate the user's emotions and adjust the caption display method based on the estimated user's emotions. For example, if the user is nervous, the caption generation unit can display simple, highly visible captions. Furthermore, if the user is relaxed, the caption generation unit can display captions that include detailed information. Furthermore, if the user is in a hurry, the caption generation unit can display captions that focus on the main points. By adjusting the caption display method based on the user's emotions, highly visible captions can be displayed. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the caption generation unit can be performed using, for example, AI, or without AI. For example, the caption generation unit can input user emotion data into the generation AI and have the generation AI adjust the caption display method.

[0111] When generating subtitles, the subtitle generation unit can automatically adjust the display position of the subtitles according to the scene in the video. For example, in an interview scene, the subtitle generation unit displays the subtitles so as to avoid the speaker's face. Furthermore, in a presentation scene, the subtitle generation unit can display the subtitles according to the content of the slides. Furthermore, in a documentary scene, the subtitle generation unit can display the subtitles according to the content of the narration. In this way, by adjusting the display position of the subtitles according to the scene in the video, visibility is improved. Some or all of the above-mentioned processing in the subtitle generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the subtitle generation unit can input scene data to the generation AI and have the generation AI adjust the display position.

[0112] The caption generation unit can be added with a function to automatically select the font and color of captions when generating captions. The caption generation unit automatically selects an appropriate font to match the theme of the video. For example, an easy-to-read font is selected for an educational video. The caption generation unit can also automatically select a highly visible caption color to match the background color of the video. For example, a bright color caption is selected for a dark background. The caption generation unit can also automatically select a font and color to express emotions according to the content of the video. For example, a soft font and warm colors are selected for an emotional scene. This automatically selecting the font and color of the caption improves visibility. Some or all of the above-described processing in the caption generation unit may be performed using, or without, AI. For example, the caption generation unit can input font and color selection data into the generation AI and have the generation AI select the font and color.

[0113] The caption generation unit can add a function to generate multiple caption styles and select the optimal style when generating captions. In the caption generation unit, for example, a generation AI generates multiple caption styles and the user selects the optimal style. In the caption generation unit, the generation AI can automatically select the optimal caption style based on the content of the video. In the caption generation unit, the generation AI can suggest the optimal caption style based on the user's past selection history. This allows the optimal style to be selected by generating multiple caption styles. Some or all of the above-described processing in the caption generation unit may be performed using, or without, AI. For example, the caption generation unit can input caption style data to the generation AI and have the generation AI select the optimal caption style.

[0114] The caption generation unit can estimate the user's emotions and adjust the display timing of the caption based on the estimated user's emotions. For example, if the user is nervous, the caption generation unit can delay the display timing of the caption and display it at a slower pace. Furthermore, if the user is relaxed, the caption generation unit can display the caption at a normal timing. Furthermore, if the user is in a hurry, the caption generation unit can advance the display timing of the caption to provide information quickly. This improves visibility by adjusting the display timing of the caption based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the caption generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the caption generation unit can input user's emotion data into the generation AI and cause the generation AI to adjust the display timing of the caption.

[0115] When generating subtitles, the subtitle generation unit can automatically adjust the length of the subtitles according to the content of the video. For example, in short scenes, the subtitle generation unit displays concise subtitles to quickly convey information to the viewer. In addition, in long scenes, the subtitle generation unit displays detailed subtitles to provide sufficient information to the viewer. Furthermore, the subtitle generation unit can automatically adjust the length of the subtitles appropriately according to the content of the video to provide optimal information to the viewer. In this way, optimal information can be provided to the viewer by adjusting the length of the subtitles according to the content of the video. Some or all of the above-described processing in the subtitle generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the subtitle generation unit can input content data of the video to the generation AI and have the generation AI adjust the length of the subtitles.

[0116] When generating captions, the caption generation unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the caption generation unit can display captions that fit the screen size. Furthermore, if the user is using a tablet, the caption generation unit can display captions optimized for a large screen. Furthermore, if the user is using a smartwatch, the caption generation unit can display captions that are concise and highly visible. This allows the optimal display method to be selected by taking into account the user's device information. Some or all of the above-described processing in the caption generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the caption generation unit can input device information data into the generation AI and cause the generation AI to select the optimal display method.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The speech recognition unit recognizes the audio in the video and generates text data. The speech recognition unit recognizes audio, such as conversations and narration, and generates corresponding text data. It also converts the audio data into text data using a speech recognition algorithm. For example, a speech recognition algorithm using deep learning is used to achieve highly accurate speech recognition. Furthermore, noise filtering technology is used to remove background noise and obtain clear audio data. Step 2: The translation unit uses a generation AI to translate the text data generated by the speech recognition unit into multiple languages. The translation unit supports multiple languages, including English, Japanese, and French. The generation AI uses a text generation AI (e.g., LLM) to translate the text data into multiple languages. It can also use a multimodal generation AI to translate the text data into multiple languages. Step 3: The caption generation unit automatically generates captions for each language based on the text data translated by the translation unit. For example, Japanese and French captions can be added to a video containing English audio. Using generation AI, captions for each language are automatically generated based on the translated text data.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 speech recognition unit that recognizes speech in the video and generates text data; a translation unit that translates the text data generated by the speech recognition unit into multiple languages; a caption generating unit that automatically generates captions corresponding to each language based on the text data translated by the translation unit. A system characterized by:

2. The voice recognition unit Includes an acquisition unit that recognizes audio in the video and generates text data The system of claim 1 .

3. The translation unit Includes a providing unit that translates the generated text data into multiple languages The system of claim 1 .

4. The voice recognition unit Estimate the user's emotions and adjust the accuracy of speech recognition based on the estimated user emotions. The system of claim 1 .

5. The voice recognition unit Add a filtering function to automatically remove background noise during voice recognition. The system of claim 1 .

6. The voice recognition unit During speech recognition, the speaker's voice characteristics are analyzed and different recognition algorithms are applied to each speaker. The system of claim 1 .

7. The voice recognition unit Add a function to automatically recognize technical terms based on the content of the video when using voice recognition. The system of claim 1 .

8. The voice recognition unit Estimate the user's emotion and adjust the timing of speech recognition based on the estimated user emotion. The system of claim 1 .

9. The voice recognition unit When recognizing speech, different recognition algorithms are applied to each scene in the video. The system of claim 1 .

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