System

Abstract

An object of a system according to an embodiment is to translate ambient speech in real time and transmit the translated speech to a user.SOLUTION: A system according to an embodiment includes a voice acquisition unit, a translation unit, and a voice output unit. The sound acquisition unit collects ambient sound. The translation unit translates the voice collected by the voice acquisition unit in real time. The voice output unit transmits the voice translated by the translation unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies are not sufficient for real-time speech translation, and there is room for improvement in everyday life applications.

[0005] The system according to the embodiment aims to translate surrounding sounds in real time and convey the translation to the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a speech acquisition unit, a translation unit, and a speech output unit. The speech acquisition unit collects surrounding speech. The translation unit translates the speech collected by the speech acquisition unit in real time. The speech output unit transmits the speech translated by the translation unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can translate surrounding sounds in real time and convey the translation to the user. [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 translation system according to an embodiment of the present invention is a system that translates speech in real time and conveys the translation to a user. This allows the translation system to translate speech in real time and convey the translation to a user.

[0029] A translation system according to an embodiment includes a speech acquisition unit, a translation unit, and a speech output unit. The speech acquisition unit collects ambient speech. For example, a microphone built into earphones collects the ambient speech. Alternatively, the speech acquisition unit can collect speech using a microphone connected to a smartphone or other device. Furthermore, the speech acquisition unit can use noise-canceling technology to remove background noise and collect clear speech. For example, the speech acquisition unit filters ambient noise to improve translation accuracy. The translation unit translates the speech collected by the speech acquisition unit in real time. For example, a generation AI analyzes speech data and generates an appropriate translation. The generation AI converts speech into text using a text generation AI (e.g., LLM) and translates the text. The generation AI can also analyze speech data and generate a translation using a multimodal generation AI. For example, the generation AI analyzes the emotion of the speech and provides a translation based on the emotion. The speech output unit transmits the speech translated by the translation unit to a user. For example, the translated speech is transmitted to a user through earphones. The audio output unit can also play audio through a smartphone or other device. Furthermore, the audio output unit can notify the user of the translation result by displaying a text or using a vibration notification. For example, the audio output unit can display the translation result as text and visually notify the user. This allows the translation system according to the embodiment to translate audio in real time and notify the user of the translation result.

[0030] The voice acquisition unit can collect surrounding voices and send the voice data to the generation AI. The voice acquisition unit, for example, collects surrounding voices and sends the voice data to the generation AI. For example, the generation AI is used to analyze the emotion of the voice and provide a translation that corresponds to the emotion. For example, if the emotion of anger is included, a warning message is added to the translation result. The voice acquisition unit also analyzes the emotion of the voice and provides a translation that corresponds to the emotion. For example, if the emotion of joy is included, a positive nuance is added to the translation result. The voice acquisition unit also performs emotion analysis and provides a translation that corresponds to the emotion. For example, if the emotion of sadness is included, words of comfort are added to the translation result. In this way, surrounding voices can be collected and sent to the generation AI.

[0031] The translation unit can analyze voice data and generate an appropriate translation. The translation unit, for example, analyzes voice data and generates an appropriate translation. For example, it automatically removes background noise from the voice and sends clear voice data to the generation AI. For example, it filters out ambient noise to improve translation accuracy. The translation unit also uses noise canceling technology to remove background noise and sends clear voice data to the generation AI. For example, it removes wind noise and car sounds. The translation unit also removes background noise from the voice data and sends clear voice to the generation AI. For example, it removes echoes in a conference room to improve translation accuracy. This makes it possible to analyze voice data and generate an appropriate translation.

[0032] The voice output unit can convey the translated voice to the user. The voice output unit conveys the translated voice to the user, for example. For example, it learns the user's speaking speed and accent and provides an individually optimized translation. For example, it performs accurate translation even for users who speak fast. The voice output unit also analyzes the speaking speed and accent and provides an individually optimized translation. For example, it performs translation that takes into account accents specific to a region. The voice output unit also learns the user's speaking patterns and provides an individually optimized translation. For example, it performs natural translation even for users who speak slowly. In this way, the translated voice can be conveyed to the user.

[0033] The translation system includes a noise removal unit that automatically removes background noise from the audio and transmits clear audio data to the generation AI. The noise removal unit, for example, automatically removes background noise from the audio and transmits clear audio data to the generation AI. For example, it filters out ambient noise to improve translation accuracy. The noise removal unit also uses noise canceling technology to remove background noise and transmit clear audio data to the generation AI. For example, it removes wind noise and car sounds. The noise removal unit also removes background noise from the audio data and transmits clear audio to the generation AI. For example, it removes echoes in a conference room to improve translation accuracy. This makes it possible to remove background noise and transmit clear audio data to the generation AI.

[0034] The translation system includes a learning unit that learns the user's speaking speed and accent and provides individually optimized translations. The learning unit, for example, learns the user's speaking speed and accent and provides individually optimized translations. For example, accurate translations are provided even for users who speak fast. The learning unit also analyzes the speaking speed and accent and provides individually optimized translations. For example, translations are provided that take regional accents into consideration. The learning unit also learns the user's speech patterns and provides individually optimized translations. For example, natural translations are provided even for users who speak slowly. In this way, it is possible to learn the user's speaking speed and accent and provide individually optimized translations.

[0035] The translation system is designed to be usable in meetings and presentations, and includes a system that simultaneously translates what is being said by multiple participants. A translation system is developed that can be used in meetings and presentations, for example, and simultaneously translates what is being said by multiple participants. For example, the system translates each participant's speech in real time and displays it as subtitles. A translation system is also developed that simultaneously translates what is being said by multiple participants, improving the efficiency of meetings. For example, the system analyzes the speech of each speaker individually and displays the translation results. A translation system is also developed that simultaneously translates what is being said in meetings and presentations, facilitating communication between participants who speak different languages. For example, the translation results are displayed on a screen in real time. This makes it possible to simultaneously translate what is being said by multiple participants in meetings and presentations.

[0036] The translation system incorporates a speech translation function into an in-vehicle system, enabling it to be used safely while driving. The translation system, for example, incorporates a speech translation function into an in-vehicle system, enabling it to be used safely while driving. For example, it can perform hands-free speech translation, allowing the driver to concentrate on driving. The translation system also integrates a speech translation function into the in-vehicle system to support communication while driving. For example, it works in conjunction with the navigation system to translate destination information. The translation system also incorporates a speech translation function into the in-vehicle system to ensure safety while driving. For example, it can start translation with a voice command, reducing manual operation while driving. This allows the speech translation function to be used safely while driving.

[0037] The translation system includes a cultural analysis unit that uses a generation AI to provide a translation that takes into account cultural nuances between different languages. The cultural analysis unit, for example, uses a generation AI to provide a translation that takes into account cultural nuances between different languages. For example, it appropriately translates jokes and idioms in a specific language. The cultural analysis unit also takes into account cultural differences between different languages, and the generation AI provides an appropriate translation. For example, it performs translation with an understanding of the cultural background. The cultural analysis unit also uses a generation AI to provide a translation that takes into account cultural nuances. For example, it appropriately translates expressions unique to a specific culture. This makes it possible to provide a translation that takes into account cultural nuances between different languages.

[0038] The translation system adds a function to learn the user's language history and prioritize translation of frequently used language pairs. The translation system, for example, learns the user's language history and adds a function to prioritize translation of frequently used language pairs. For example, the translation system automatically detects language pairs that the user frequently uses. The translation system also adds a function to analyze the language history and prioritize translation of frequently used language pairs. For example, the translation system identifies language pairs based on the user's past translation history. The translation system also adds a function to learn the user's language history and prioritize translation of frequently used language pairs. For example, the translation system displays language pairs that the user frequently uses with priority. This makes it possible to learn the user's language history and prioritize translation of frequently used language pairs.

[0039] The translation system includes a feedback collection unit that collects user feedback on the translation results and allows the generation AI to continuously learn and improve translation accuracy. The feedback collection unit, for example, collects user feedback on the translation results and allows the generation AI to continuously learn and improve translation accuracy. For example, it updates the translation model based on user evaluations. The feedback collection unit also collects user feedback and allows the generation AI to continuously learn and improve translation accuracy. For example, it reflects user opinions to improve the translation algorithm. The feedback collection unit also collects feedback on the translation results and allows the generation AI to continuously learn and improve translation accuracy. For example, it incorporates user correction suggestions to improve the translation model. In this way, user feedback can be collected and the generation AI can continuously learn to improve translation accuracy.

[0040] The translation system utilizes its multilingual capability as a language learning tool in educational settings, allowing students to learn different languages ​​in real time. The translation system, for example, utilizes its multilingual capability as a language learning tool in educational settings, allowing students to learn different languages ​​in real time. For example, it provides real-time translation during class, allowing students to understand different languages. The translation system also utilizes its multilingual capability as a language learning tool, allowing students to learn different languages ​​in real time. For example, it provides real-time translation during online classes. The translation system also utilizes its multilingual capability in educational settings, allowing students to learn different languages ​​in real time. For example, it integrates real-time translation functionality into a language learning app, allowing it to be used as a language learning tool in educational settings, allowing students to learn different languages ​​in real time.

[0041] The translation system incorporates a multilingual feature into a tourist guide system to make it easier for tourists to understand the local language. The translation system, for example, incorporates a multilingual feature into a tourist guide system to make it easier for tourists to understand the local language. For example, it translates descriptions of tourist attractions in real time. The translation system also integrates a multilingual feature into a tourist guide system to make it easier for tourists to understand the local language. For example, it translates audio guides in real time. The translation system also integrates a multilingual feature into a tourist guide system to make it easier for tourists to understand the local language. For example, it translates signs and information boards at tourist attractions in real time. In this way, it can be incorporated into a tourist guide system to make it easier for tourists to understand the local language.

[0042] The translation system includes a resolution optimization unit that automatically optimizes the resolution of image data, enabling the generation AI to perform highly accurate translations. The resolution optimization unit, for example, automatically optimizes the resolution of image data, enabling the generation AI to perform highly accurate translations. For example, it converts low-resolution images to high resolution. The resolution optimization unit also optimizes the resolution, enabling the generation AI to perform highly accurate translations. For example, it makes blurry text clearer. The resolution optimization unit also automatically optimizes the resolution of image data, enabling the generation AI to perform highly accurate translations. For example, it makes text on old photographs easier to read. In this way, the resolution of image data can be automatically optimized, enabling the generation AI to perform highly accurate translations.

[0043] The translation system includes an eye tracking unit that tracks the user's gaze and prioritizes translating characters and signs that are in the user's line of sight. The eye tracking unit adds, for example, a function to track the user's gaze and prioritize translating characters and signs that are in the user's line of sight. For example, it translates signs that are in the user's line of sight in real time. The eye tracking unit also adds a function to use eye tracking technology to prioritize translating characters and signs that the user is paying attention to. For example, it analyzes eye movement to identify the translation target. The eye tracking unit also adds a function to track the user's gaze and prioritize translating characters and signs that are in the user's line of sight. For example, it translates a menu that is in the user's line of sight. This makes it possible to track the user's gaze and prioritize translating characters and signs that are in the user's line of sight.

[0044] The translation system applies a visual translation function to exhibits in museums and art galleries to make it easier for visitors to understand the explanations of the exhibits. The translation system, for example, applies a visual translation function to exhibits in museums and art galleries to make it easier for visitors to understand the explanations of the exhibits. For example, it translates the explanations of the exhibits in real time. The translation system also applies a visual translation function to exhibits in museums and art galleries to make it easier for visitors to understand the explanations of the exhibits. For example, it translates the captions of the exhibits. The translation system also applies a visual translation function to exhibits in museums and art galleries to make it easier for visitors to understand the explanations of the exhibits. For example, it translates the explanatory panels of the exhibits. In this way, the translation system applies a visual translation function to exhibits in museums and art galleries to make it easier for visitors to understand the explanations of the exhibits.

[0045] A translation system incorporates a visual translation function into a smartphone camera app and translates text in photos taken by a user in real time. For example, the translation system incorporates a visual translation function into a smartphone camera app and translates text in photos taken by a user in real time. For example, it translates signs and menus. A translation system also incorporates a visual translation function into a smartphone camera app and translates text in photos taken by a user in real time. For example, it translates documents and posters. A translation system also incorporates a visual translation function into a smartphone camera app and translates text in photos taken by a user in real time. For example, it translates product labels and instructions. In this way, a visual translation function can be incorporated into a smartphone camera app and translates text in photos taken by a user in real time.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The translation system can analyze the user's speech and set translation priorities based on specific keywords. For example, if a speech contains keywords of high urgency, it will prioritize translating those parts. It can also emphasize important information when translating it. Furthermore, if a user is talking about a specific topic, it can prioritize translating information related to that topic. This allows important information to be conveyed to the user quickly and accurately.

[0048] The translation system can analyze the user's speaking speed and adjust the translation speed according to the speaking speed. For example, if the user speaks quickly, the translation result can be provided quickly. On the other hand, if the user speaks slowly, the translation result can be provided slowly. Furthermore, if the user's speaking speed fluctuates, the translation speed can also be adjusted in accordance with the fluctuation. This makes it possible to provide appropriate translation results according to the user's speaking speed.

[0049] The translation system can analyze the content of a user's speech and automatically collect and translate information related to a specific topic. For example, if a user is talking about travel, travel-related information can be translated first. If a user is talking about business, business-related information can be translated first. Furthermore, if a user is talking about an academic topic, information related to that topic can be translated first. This makes it possible to provide appropriate translation results that meet the user's needs.

[0050] The translation system can analyze the content of a user's speech and adjust the tone of the translation based on specific keywords. For example, if the user is expressing gratitude, the translation result can be adjusted to a more polite tone. Also, if the user is giving instructions, the translation result can be adjusted to a clearer tone. Furthermore, if the user is asking a question, the translation result can be provided in a tone appropriate to the question. This makes it possible to provide translation results in a tone appropriate to the content of the user's speech.

[0051] The translation system can analyze the content of a user's speech and automatically collect and translate information related to a specific topic. For example, if a user is talking about travel, travel-related information can be translated first. If a user is talking about business, business-related information can be translated first. Furthermore, if a user is talking about an academic topic, information related to that topic can be translated first. This makes it possible to provide appropriate translation results that meet the user's needs.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The audio capture unit collects ambient audio. For example, audio can be collected using a microphone built into earphones or a microphone connected to a smartphone. It can also use noise-canceling technology to remove background noise and collect clear audio. Step 2: The translation unit translates the speech collected by the speech acquisition unit in real time. For example, it uses a generation AI to analyze the speech data and generate an appropriate translation. The generation AI uses a text generation AI (e.g., LLM) to convert the speech to text and then translates the text. It can also use a multimodal generation AI to analyze the emotion of the speech and provide a translation based on the emotion. Step 3: The audio output unit transmits the translated audio to the user. For example, the translated audio is transmitted to the user through earphones. The audio can also be played back through a smartphone or other device. The translation result can also be displayed as text or via a vibration notification.

[0054] (Example 2) A translation system according to an embodiment of the present invention is a system that translates speech in real time and conveys the translation to a user. This allows the translation system to translate speech in real time and convey the translation to a user.

[0055] A translation system according to an embodiment includes a speech acquisition unit, a translation unit, and a speech output unit. The speech acquisition unit collects ambient speech. For example, a microphone built into earphones collects the ambient speech. Alternatively, the speech acquisition unit can collect speech using a microphone connected to a smartphone or other device. Furthermore, the speech acquisition unit can use noise-canceling technology to remove background noise and collect clear speech. For example, the speech acquisition unit filters ambient noise to improve translation accuracy. The translation unit translates the speech collected by the speech acquisition unit in real time. For example, a generation AI analyzes speech data and generates an appropriate translation. The generation AI converts speech into text using a text generation AI (e.g., LLM) and translates the text. The generation AI can also analyze speech data and generate a translation using a multimodal generation AI. For example, the generation AI analyzes the emotion of the speech and provides a translation based on the emotion. The speech output unit transmits the speech translated by the translation unit to a user. For example, the translated speech is transmitted to a user through earphones. The audio output unit can also play audio through a smartphone or other device. Furthermore, the audio output unit can notify the user of the translation result by displaying a text or using a vibration notification. For example, the audio output unit can display the translation result as text and visually notify the user. This allows the translation system according to the embodiment to translate audio in real time and notify the user of the translation result.

[0056] The voice acquisition unit can collect surrounding voices and send the voice data to the generation AI. The voice acquisition unit, for example, collects surrounding voices and sends the voice data to the generation AI. For example, the generation AI is used to analyze the emotion of the voice and provide a translation that corresponds to the emotion. For example, if the emotion of anger is included, a warning message is added to the translation result. The voice acquisition unit also analyzes the emotion of the voice and provides a translation that corresponds to the emotion. For example, if the emotion of joy is included, a positive nuance is added to the translation result. The voice acquisition unit also performs emotion analysis and provides a translation that corresponds to the emotion. For example, if the emotion of sadness is included, words of comfort are added to the translation result. In this way, surrounding voices can be collected and sent to the generation AI.

[0057] The translation unit can analyze voice data and generate an appropriate translation. The translation unit, for example, analyzes voice data and generates an appropriate translation. For example, it automatically removes background noise from the voice and sends clear voice data to the generation AI. For example, it filters out ambient noise to improve translation accuracy. The translation unit also uses noise canceling technology to remove background noise and sends clear voice data to the generation AI. For example, it removes wind noise and car sounds. The translation unit also removes background noise from the voice data and sends clear voice to the generation AI. For example, it removes echoes in a conference room to improve translation accuracy. This makes it possible to analyze voice data and generate an appropriate translation.

[0058] The voice output unit can convey the translated voice to the user. The voice output unit conveys the translated voice to the user, for example. For example, it learns the user's speaking speed and accent and provides an individually optimized translation. For example, it performs accurate translation even for users who speak fast. The voice output unit also analyzes the speaking speed and accent and provides an individually optimized translation. For example, it performs translation that takes into account accents specific to a region. The voice output unit also learns the user's speaking patterns and provides an individually optimized translation. For example, it performs natural translation even for users who speak slowly. In this way, the translated voice can be conveyed to the user.

[0059] The translation system includes an emotion analysis unit that analyzes speech and provides a translation that corresponds to the emotion. The emotion analysis unit uses, for example, generative AI to analyze the emotion of the speech and provide a translation that corresponds to the emotion. For example, if the emotion of anger is included, a warning message is added to the translation result. The emotion analysis unit also analyzes the emotion of the speech and provides a translation that corresponds to the emotion. For example, if the emotion of joy is included, a positive nuance is added to the translation result. The emotion analysis unit also performs emotion analysis and provides a translation that corresponds to the emotion. For example, if the emotion of sadness is included, words of comfort are added to the translation result. In this way, it is possible to analyze the emotion of the speech and provide a translation that corresponds to the emotion.

[0060] The translation system includes a noise removal unit that automatically removes background noise from the audio and transmits clear audio data to the generation AI. The noise removal unit, for example, automatically removes background noise from the audio and transmits clear audio data to the generation AI. For example, it filters out ambient noise to improve translation accuracy. The noise removal unit also uses noise canceling technology to remove background noise and transmit clear audio data to the generation AI. For example, it removes wind noise and car sounds. The noise removal unit also removes background noise from the audio data and transmits clear audio to the generation AI. For example, it removes echoes in a conference room to improve translation accuracy. This makes it possible to remove background noise and transmit clear audio data to the generation AI.

[0061] The translation system includes a learning unit that learns the user's speaking speed and accent and provides individually optimized translations. The learning unit, for example, learns the user's speaking speed and accent and provides individually optimized translations. For example, accurate translations are provided even for users who speak fast. The learning unit also analyzes the speaking speed and accent and provides individually optimized translations. For example, translations are provided that take regional accents into consideration. The learning unit also learns the user's speech patterns and provides individually optimized translations. For example, natural translations are provided even for users who speak slowly. In this way, it is possible to learn the user's speaking speed and accent and provide individually optimized translations.

[0062] The translation system is designed to be usable in meetings and presentations, and includes a system that simultaneously translates what is being said by multiple participants. A translation system is developed that can be used in meetings and presentations, for example, and simultaneously translates what is being said by multiple participants. For example, the system translates each participant's speech in real time and displays it as subtitles. A translation system is also developed that simultaneously translates what is being said by multiple participants, improving the efficiency of meetings. For example, the system analyzes the speech of each speaker individually and displays the translation results. A translation system is also developed that simultaneously translates what is being said in meetings and presentations, facilitating communication between participants who speak different languages. For example, the translation results are displayed on a screen in real time. This makes it possible to simultaneously translate what is being said by multiple participants in meetings and presentations.

[0063] The translation system incorporates a speech translation function into an in-vehicle system, enabling it to be used safely while driving. The translation system, for example, incorporates a speech translation function into an in-vehicle system, enabling it to be used safely while driving. For example, it can perform hands-free speech translation, allowing the driver to concentrate on driving. The translation system also integrates a speech translation function into the in-vehicle system to support communication while driving. For example, it works in conjunction with the navigation system to translate destination information. The translation system also incorporates a speech translation function into the in-vehicle system to ensure safety while driving. For example, it can start translation with a voice command, reducing manual operation while driving. This allows the speech translation function to be used safely while driving.

[0064] The translation system uses an emotion estimation function to add a setting to start translation only when the user is relaxed. The translation system, for example, uses the emotion estimation function to add a setting to start translation only when the user is relaxed. For example, the translation system analyzes the user's heart rate and facial expressions to detect a relaxed state. The translation system also monitors the user's emotional state in real time and adds a setting to start translation only when the user is relaxed. For example, translation is performed when the stress level is low. The translation system also uses the emotion estimation function to add a setting to start translation only when the user is relaxed, providing a less stressful translation experience. For example, translation automatically starts when a relaxed state is detected. This makes it possible to provide a less stressful translation experience by starting translation only when the user is relaxed.

[0065] The translation system includes a cultural analysis unit that uses a generation AI to provide a translation that takes into account cultural nuances between different languages. The cultural analysis unit, for example, uses a generation AI to provide a translation that takes into account cultural nuances between different languages. For example, it appropriately translates jokes and idioms in a specific language. The cultural analysis unit also takes into account cultural differences between different languages, and the generation AI provides an appropriate translation. For example, it performs translation with an understanding of the cultural background. The cultural analysis unit also uses a generation AI to provide a translation that takes into account cultural nuances. For example, it appropriately translates expressions unique to a specific culture. This makes it possible to provide a translation that takes into account cultural nuances between different languages.

[0066] The translation system adds a function to learn the user's language history and prioritize translation of frequently used language pairs. The translation system, for example, learns the user's language history and adds a function to prioritize translation of frequently used language pairs. For example, the translation system automatically detects language pairs that the user frequently uses. The translation system also adds a function to analyze the language history and prioritize translation of frequently used language pairs. For example, the translation system identifies language pairs based on the user's past translation history. The translation system also adds a function to learn the user's language history and prioritize translation of frequently used language pairs. For example, the translation system displays language pairs that the user frequently uses with priority. This makes it possible to learn the user's language history and prioritize translation of frequently used language pairs.

[0067] The translation system includes a feedback collection unit that collects user feedback on the translation results and allows the generation AI to continuously learn and improve translation accuracy. The feedback collection unit, for example, collects user feedback on the translation results and allows the generation AI to continuously learn and improve translation accuracy. For example, it updates the translation model based on user evaluations. The feedback collection unit also collects user feedback and allows the generation AI to continuously learn and improve translation accuracy. For example, it reflects user opinions to improve the translation algorithm. The feedback collection unit also collects feedback on the translation results and allows the generation AI to continuously learn and improve translation accuracy. For example, it incorporates user correction suggestions to improve the translation model. In this way, user feedback can be collected and the generation AI can continuously learn to improve translation accuracy.

[0068] The translation system utilizes its multilingual capability as a language learning tool in educational settings, allowing students to learn different languages ​​in real time. The translation system, for example, utilizes its multilingual capability as a language learning tool in educational settings, allowing students to learn different languages ​​in real time. For example, it provides real-time translation during class, allowing students to understand different languages. The translation system also utilizes its multilingual capability as a language learning tool, allowing students to learn different languages ​​in real time. For example, it provides real-time translation during online classes. The translation system also utilizes its multilingual capability in educational settings, allowing students to learn different languages ​​in real time. For example, it integrates real-time translation functionality into a language learning app, allowing it to be used as a language learning tool in educational settings, allowing students to learn different languages ​​in real time.

[0069] The translation system incorporates a multilingual feature into a tourist guide system to make it easier for tourists to understand the local language. The translation system, for example, incorporates a multilingual feature into a tourist guide system to make it easier for tourists to understand the local language. For example, it translates descriptions of tourist attractions in real time. The translation system also integrates a multilingual feature into a tourist guide system to make it easier for tourists to understand the local language. For example, it translates audio guides in real time. The translation system also integrates a multilingual feature into a tourist guide system to make it easier for tourists to understand the local language. For example, it translates signs and information boards at tourist attractions in real time. In this way, it can be incorporated into a tourist guide system to make it easier for tourists to understand the local language.

[0070] The translation system uses an emotion estimation function to analyze the emotion a user has toward a specific language and adds a function to adjust the tone of the translation based on that emotion. For example, the translation system uses the emotion estimation function to analyze the emotion a user has toward a specific language and adjust the tone of the translation based on that emotion. For example, it translates a language with a positive emotion in a friendly tone. The translation system also analyzes the user's emotion and adjusts the tone of the translation based on that emotion. For example, it translates a language with a negative emotion in a polite tone. The translation system also uses the emotion estimation function to analyze the emotion a user has toward a specific language and adjust the tone of the translation based on that emotion. For example, it changes the expression of the translation depending on the emotion score. This makes it possible to analyze the emotion a user has toward a specific language and adjust the tone of the translation based on that emotion.

[0071] The translation system includes a visual emotion analysis unit that uses a generation AI to analyze the emotion of visual information and provide a translation that corresponds to the emotion. The visual emotion analysis unit, for example, uses a generation AI to analyze the emotion of visual information and provide a translation that corresponds to the emotion. For example, a caution message is added to the translation of a warning sign. The visual emotion analysis unit also analyzes the emotion of visual information and provides a translation that corresponds to the emotion. For example, emotion is reflected in the translation of an advertisement. The visual emotion analysis unit also uses a generation AI to analyze the emotion of visual information and provide a translation that corresponds to the emotion. For example, emotion is added to the translation of an inspiring poster. This makes it possible to analyze the emotion of visual information and provide a translation that corresponds to the emotion.

[0072] The translation system includes a resolution optimization unit that automatically optimizes the resolution of image data, enabling the generation AI to perform highly accurate translations. The resolution optimization unit, for example, automatically optimizes the resolution of image data, enabling the generation AI to perform highly accurate translations. For example, it converts low-resolution images to high resolution. The resolution optimization unit also optimizes the resolution, enabling the generation AI to perform highly accurate translations. For example, it makes blurry text clearer. The resolution optimization unit also automatically optimizes the resolution of image data, enabling the generation AI to perform highly accurate translations. For example, it makes text on old photographs easier to read. In this way, the resolution of image data can be automatically optimized, enabling the generation AI to perform highly accurate translations.

[0073] The translation system includes an eye tracking unit that tracks the user's gaze and prioritizes translating characters and signs that are in the user's line of sight. The eye tracking unit adds, for example, a function to track the user's gaze and prioritize translating characters and signs that are in the user's line of sight. For example, it translates signs that are in the user's line of sight in real time. The eye tracking unit also adds a function to use eye tracking technology to prioritize translating characters and signs that the user is paying attention to. For example, it analyzes eye movement to identify the translation target. The eye tracking unit also adds a function to track the user's gaze and prioritize translating characters and signs that are in the user's line of sight. For example, it translates a menu that is in the user's line of sight. This makes it possible to track the user's gaze and prioritize translating characters and signs that are in the user's line of sight.

[0074] The translation system applies a visual translation function to exhibits in museums and art galleries to make it easier for visitors to understand the explanations of the exhibits. The translation system, for example, applies a visual translation function to exhibits in museums and art galleries to make it easier for visitors to understand the explanations of the exhibits. For example, it translates the explanations of the exhibits in real time. The translation system also applies a visual translation function to exhibits in museums and art galleries to make it easier for visitors to understand the explanations of the exhibits. For example, it translates the captions of the exhibits. The translation system also applies a visual translation function to exhibits in museums and art galleries to make it easier for visitors to understand the explanations of the exhibits. For example, it translates the explanatory panels of the exhibits. In this way, the translation system applies a visual translation function to exhibits in museums and art galleries to make it easier for visitors to understand the explanations of the exhibits.

[0075] A translation system incorporates a visual translation function into a smartphone camera app and translates text in photos taken by a user in real time. For example, the translation system incorporates a visual translation function into a smartphone camera app and translates text in photos taken by a user in real time. For example, it translates signs and menus. A translation system also incorporates a visual translation function into a smartphone camera app and translates text in photos taken by a user in real time. For example, it translates documents and posters. A translation system also incorporates a visual translation function into a smartphone camera app and translates text in photos taken by a user in real time. For example, it translates product labels and instructions. In this way, a visual translation function can be incorporated into a smartphone camera app and translates text in photos taken by a user in real time.

[0076] The translation system adds a function that uses an emotion estimation function to analyze the emotion a user has toward visual information and adjusts the way a translation is displayed based on that emotion. For example, the translation system uses the emotion estimation function to analyze the emotion a user has toward visual information and adjusts the way a translation is displayed based on that emotion. For example, information that has a positive emotion is displayed in a bright color. The translation system also analyzes the user's emotion and adjusts the way a translation is displayed based on that emotion. For example, information that has a negative emotion is displayed in a subdued color. The translation system also uses the emotion estimation function to analyze the emotion a user has toward visual information and adjusts the way a translation is displayed based on that emotion. For example, the font size is changed depending on the emotion score. This makes it possible to analyze the emotion a user has toward visual information and adjust the way a translation is displayed based on that emotion.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The translation system can estimate the user's emotions and adjust the tone of the translation based on the estimated emotions. For example, if the user is feeling angry, the translation result can be adjusted to a gentler tone. Alternatively, if the user is feeling happy, the translation result can be adjusted to a more positive tone. Furthermore, if the user is feeling sad, the translation result can be adjusted to a comforting tone. This makes it possible to provide translation results that correspond to the user's emotions.

[0079] The translation system can analyze the user's speech and set translation priorities based on specific keywords. For example, if a speech contains keywords of high urgency, it will prioritize translating those parts. It can also emphasize important information when translating it. Furthermore, if a user is talking about a specific topic, it can prioritize translating information related to that topic. This allows important information to be conveyed to the user quickly and accurately.

[0080] The translation system can estimate the user's emotions and adjust the way the translation is displayed based on the estimated emotions. For example, if the user is relaxed, the translation result can be displayed in a larger font. If the user is nervous, the translation result can be displayed in a more subdued color. Furthermore, if the user is excited, the translation result can be highlighted. This allows the translation result to be displayed in an appropriate way according to the user's emotions.

[0081] The translation system can analyze the user's speaking speed and adjust the translation speed according to the speaking speed. For example, if the user speaks quickly, the translation result can be provided quickly. On the other hand, if the user speaks slowly, the translation result can be provided slowly. Furthermore, if the user's speaking speed fluctuates, the translation speed can also be adjusted in accordance with the fluctuation. This makes it possible to provide appropriate translation results according to the user's speaking speed.

[0082] The translation system can estimate the user's emotions and adjust the translation content based on the estimated emotions. For example, if the user is feeling angry, a warning message can be added to the translation result. If the user is feeling happy, a positive nuance can be added to the translation result. Furthermore, if the user is feeling sad, words of comfort can be added to the translation result. This makes it possible to provide appropriate translation results according to the user's emotions.

[0083] The translation system can analyze the content of a user's speech and automatically collect and translate information related to a specific topic. For example, if a user is talking about travel, travel-related information can be translated first. If a user is talking about business, business-related information can be translated first. Furthermore, if a user is talking about an academic topic, information related to that topic can be translated first. This makes it possible to provide appropriate translation results that meet the user's needs.

[0084] The translation system can estimate the user's emotions and set translation priorities based on the estimated emotions. For example, if the user feels a high level of urgency, it can prioritize translation of that part. Also, if the user is trying to convey important information, it can emphasize that part in the translation. Furthermore, if the user has a specific emotion, it can provide appropriate translation results according to that emotion. This makes it possible to provide appropriate translation results according to the user's emotions.

[0085] The translation system can analyze the content of a user's speech and adjust the tone of the translation based on specific keywords. For example, if the user is expressing gratitude, the translation result can be adjusted to a more polite tone. Also, if the user is giving instructions, the translation result can be adjusted to a clearer tone. Furthermore, if the user is asking a question, the translation result can be provided in a tone appropriate to the question. This makes it possible to provide translation results in a tone appropriate to the content of the user's speech.

[0086] The translation system can estimate the user's emotions and adjust the way the translation is displayed based on the estimated emotions. For example, if the user is relaxed, the translation result can be displayed in a larger font. If the user is nervous, the translation result can be displayed in a more subdued color. Furthermore, if the user is excited, the translation result can be highlighted. This allows the translation result to be displayed in an appropriate way according to the user's emotions.

[0087] The translation system can analyze the content of a user's speech and automatically collect and translate information related to a specific topic. For example, if a user is talking about travel, travel-related information can be translated first. If a user is talking about business, business-related information can be translated first. Furthermore, if a user is talking about an academic topic, information related to that topic can be translated first. This makes it possible to provide appropriate translation results that meet the user's needs.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The audio capture unit collects ambient audio. For example, audio can be collected using a microphone built into earphones or a microphone connected to a smartphone. It can also use noise-canceling technology to remove background noise and collect clear audio. Step 2: The translation unit translates the speech collected by the speech acquisition unit in real time. For example, it uses a generation AI to analyze the speech data and generate an appropriate translation. The generation AI uses a text generation AI (e.g., LLM) to convert the speech to text and then translates the text. It can also use a multimodal generation AI to analyze the emotion of the speech and provide a translation based on the emotion. Step 3: The audio output unit transmits the translated audio to the user. For example, the translated audio is transmitted to the user through earphones. The audio can also be played back through a smartphone or other device. The translation result can also be displayed as text or via a vibration notification.

[0090] 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.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0092] 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.

[0093] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0109] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0119] 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.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0124] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[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 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.

[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 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).

[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] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] In the robot 414, 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 robot 414 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.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0144] 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."

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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. [Explanation of symbols]

[0157] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a voice acquisition unit that collects surrounding voices; a translation unit that translates the speech collected by the speech acquisition unit in real time; a voice output unit that outputs the voice translated by the translation unit to the user. A system characterized by:

2. The voice acquisition unit Collects surrounding sounds and sends the audio data to a generating AI 2. The system of claim 1.

3. The translation unit Analyzes speech data and generates appropriate translations 2. The system of claim 1.

4. The audio output unit conveying the translated speech to the user 2. The system of claim 1.

5. An emotion analysis unit analyzes the speech and provides a translation according to the emotion.

2. The system of claim 1.

6. It has a noise removal unit that automatically removes background noise from the audio and sends clear audio data to the generation AI.

2. The system of claim 1.

7. A learning unit is provided that learns the user's speaking speed and accent and provides individually optimized translations.

2. The system of claim 1.

8. The system can be used in a conference or presentation and can simultaneously translate the speech of multiple participants.

2. The system of claim 1.

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