system

Abstract

The system according to this embodiment aims to translate languages ​​in real time and facilitate smooth communication. [Solution] The system according to this embodiment comprises a recognition unit, a translation unit, and a display unit. The recognition unit recognizes the user's utterance in real time. The translation unit translates the utterance recognized by the recognition unit. The display unit displays the content translated by the translation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that real-time language translation is difficult and smooth communication is difficult.

[0005] The system according to the embodiment aims to translate languages in real time and perform smooth communication.

Means for Solving the Problems

[0006] The system according to the embodiment includes a recognition unit, a translation unit, and a display unit. The recognition unit recognizes a user's utterance in real time. The translation unit translates the utterance recognized by the recognition unit. The display unit displays the content translated by the translation unit.

Effects of the Invention

[0007] The system according to this embodiment can translate languages ​​in real time and enable smooth communication. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The translation support system according to an embodiment of the present invention is a system that uses AI technology to translate language in real time and displays it to the user through a glasses-type device. This translation support system uses AI to recognize the content spoken by the user in real time and translate it. The translated content is displayed on the display of the glasses-type device, allowing the user to visually confirm the translation result. This enables smooth communication across language barriers. This system utilizes advanced natural language processing technology and supports instant translation of multiple languages. By recognizing and translating the user's speech in real time, it is expected to improve communication speed, enhance the comprehension of language learners, and revitalize international exchange. Furthermore, its application to a wearable device allows for an intuitively designed user interface, improving ease of use. The target audience includes travelers, international business people, language learners, and all those who need to communicate in a multilingual environment. This eliminates the inconvenience of communication due to language barriers and enables instant and accurate communication. For example, the translation support system recognizes the content spoken by the user in real time and translates it. The translated content is displayed on the display of the glasses-type device, allowing the user to visually confirm the translation result. This enables smooth communication across language barriers. The translation support system can recognize, translate, and display user speech in real time, facilitating seamless communication.

[0029] The translation support system according to this embodiment comprises a recognition unit, a translation unit, and a display unit. The recognition unit recognizes the user's utterance in real time. The recognition unit converts the user's utterance into text data using, for example, speech recognition technology. The recognition unit can, for example, collect the user's utterance using a microphone and convert it into text data using a speech recognition algorithm. The recognition unit can also, for example, remove background noise using noise cancellation technology to improve the accuracy of utterance recognition. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or without AI. For example, the recognition unit can analyze speech data using an AI model and generate text data in order to recognize the user's utterance in real time. The translation unit translates the utterance recognized by the recognition unit. The translation unit performs instant translation of multiple languages ​​using, for example, natural language processing technology. The translation unit can, for example, translate utterances using text generation AI (e.g., LLM). The translation unit can also, for example, translate the content of utterances using multimodal generation AI. The translation unit may, for example, perform translations while considering contextual information to improve translation accuracy. Some or all of the above-described processes in the translation unit may be performed using AI, for example, or not. For example, the translation unit may use an AI model to analyze text data and generate a translation result in order to translate the user's utterance. The display unit displays the content translated by the translation unit. The display unit may, for example, display the translation result on the display of a glasses-type device. The display unit may, for example, use a glasses-type device such as AR glasses or smart glasses to visually display the translation result. The display unit may, for example, track the user's gaze and dynamically change the optimal display position. Some or all of the above-described processes in the display unit may, for example, be performed using AI, for example, or not. For example, the display unit may use an AI model to optimize the display content in order to display the translation result and provide it to the user. As a result, the translation support system according to the embodiment can achieve smooth communication by recognizing, translating, and displaying the user's utterance in real time.

[0030] The recognition unit recognizes user speech in real time. For example, it converts user speech into text data using speech recognition technology. Specifically, the recognition unit collects user speech using a high-sensitivity microphone, and the collected audio data is analyzed by a speech recognition algorithm. The speech recognition algorithm analyzes the audio waveform and decomposes it into phonemes and words, converting the speech content into text data. Furthermore, the recognition unit uses noise cancellation technology to remove background noise and improve the accuracy of speech recognition. Noise cancellation technology detects ambient noise in real time and removes it, enabling the acquisition of clear audio data. Some or all of the above processing in the recognition unit is often performed using AI. For example, an AI model can be used to analyze audio data and generate text data. By learning from a large amount of audio data, the AI ​​model can handle various speech patterns and accents. This allows the recognition unit to recognize user speech with high accuracy in real time. Furthermore, the recognition unit can analyze user speech content based on context and perform corrections to reduce misrecognition. For example, recognition accuracy can be improved by prioritizing the recognition of words and phrases that are likely to be predicted in a specific context. This allows the recognition unit to accurately and quickly convert the user's utterance into text data, which can then be used for subsequent translation processing.

[0031] The translation unit translates the utterances recognized by the recognition unit. The translation unit performs real-time translation between multiple languages, for example, using natural language processing techniques. Specifically, the translation unit analyzes the text data provided by the recognition unit and processes it for translation into the target language. The translation unit can translate utterances using text generation AI (e.g., LLM). Text generation AI learns from large amounts of language data, understanding the appropriate use of grammar and vocabulary, and generating highly accurate translations. Furthermore, the translation unit can also translate the content of utterances using multimodal generation AI. Multimodal generation AI achieves a richer contextual understanding by integrating and analyzing multiple modalities, including not only speech and text, but also images and videos. This allows the translation unit to provide translations that accurately capture the nuances and intent of the utterances. To improve translation accuracy, the translation unit can also consider contextual information during translation. For example, by considering the context before and after the conversation and the usage of specific technical terms, it can generate more natural and appropriate translations. Some or all of the above-mentioned processes in the translation unit are often performed using AI. By using AI models, the translation unit can translate user speech quickly and accurately, providing translation results in real time. This allows the translation unit to translate user speech in multiple languages, supporting smooth communication.

[0032] The display unit displays the content translated by the translation unit. For example, the display unit displays the translation results on the screen of a glasses-type device. Specifically, the display unit can visually display the translation results using glasses-type devices such as AR glasses or smart glasses. This allows users to view the translation results naturally and confirm the translated content without interrupting the flow of communication. The display unit can also track the user's gaze and dynamically change the optimal display position. By using eye-tracking technology, it can detect where the user is looking and display the translation results in the most easily viewable position. This allows users to confirm the translation results without moving their eyes. Some or all of the above processing in the display unit is often performed using AI. For example, an AI model can be used to optimize the display content and provide it to the user. The AI ​​model analyzes the user's gaze and movements and selects the optimal display method, thereby improving the user experience. Furthermore, the display unit can customize display settings according to the user's preferences. For example, by changing the font size and display color, the translation results can be displayed in the most easily viewable format for the user. This allows the display unit to provide users with an intuitive and user-friendly interface, enhancing the convenience of the translation support system.

[0033] The interface unit can intuitively design the user interface. The interface unit can simplify the operating procedure based on the results of usability tests, for example. The interface unit can also analyze the user's operation history and suggest the optimal operating method, for example. The interface unit can also dynamically change the operating procedure according to the user's operation status, for example. This improves usability through an intuitive user interface. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's operation history into an AI model and suggest the optimal operating method.

[0034] The multilingual support unit can support communication in a multilingual environment. For example, it can support major international languages ​​and specific regional languages. The multilingual support unit can also automatically select the appropriate language based on the user's language settings. Furthermore, it can dynamically change the appropriate language based on the user's utterances. This facilitates smooth communication in a multilingual environment. Some or all of the above-described processes in the multilingual support unit may be performed using AI, or not. For example, the multilingual support unit can input the user's utterances into an AI model and automatically select the appropriate language.

[0035] The recognition unit can recognize user speech in real time. The recognition unit converts user speech into text data using, for example, speech recognition technology. The recognition unit can collect user speech using, for example, a microphone and convert it into text data using a speech recognition algorithm. The recognition unit can also improve the accuracy of speech recognition by removing background noise using, for example, noise cancellation technology. This enables instant translation by recognizing user speech in real time. Some or all of the above processing in the recognition unit may be performed using, for example, AI, or not using AI. For example, the recognition unit can analyze speech data using an AI model and generate text data in order to recognize user speech in real time.

[0036] The translation unit can support real-time translation of multiple languages. For example, the translation unit can perform real-time translation of multiple languages ​​using natural language processing technology. The translation unit can translate utterances using text generation AI (e.g., LLM). The translation unit can also translate the content of utterances using multimodal generation AI. The translation unit can also consider contextual information to improve translation accuracy. This enables real-time translation of multiple languages, facilitating smooth communication in various languages. Some or all of the above-described processes in the translation unit may be performed using AI, or not. For example, the translation unit can analyze text data using an AI model to translate user utterances and generate translation results.

[0037] The display unit can display the translated content on the display of a glasses-type device. The display unit can visually display the translation results using a glasses-type device such as AR glasses or smart glasses. The display unit can also track the user's gaze and dynamically change the optimal display position. This allows the user to visually confirm the translation results by displaying the translated content on the display of the glasses-type device. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use an AI model to optimize the display content for displaying the translation results and provide it to the user.

[0038] The recognition unit can dynamically change its recognition algorithm according to the user's speaking speed and volume. For example, if the user speaks quickly, the AI ​​can apply a high-speed recognition algorithm to accurately recognize the speech. For example, if the user speaks slowly, the AI ​​can apply a low-speed recognition algorithm to accurately capture the details of the speech. For example, if the user speaks loudly, the AI ​​can apply a volume-dependent recognition algorithm to accurately recognize the speech. This improves recognition accuracy by changing the recognition algorithm according to the user's speaking speed and volume. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the user's speaking speed and volume data into a generating AI and cause the generating AI to dynamically change the recognition algorithm.

[0039] The recognition unit can filter background noise in real time to improve the accuracy of speech recognition. For example, if a user speaks in a noisy place, the AI ​​can filter background noise in real time to accurately recognize the speech. For example, if a user speaks in a windy place, the AI ​​can filter out wind noise to accurately recognize the speech. For example, if a user speaks in a place where music is playing, the AI ​​can filter out the music noise to accurately recognize the speech. This improves the accuracy of speech recognition by filtering out background noise. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input background noise data into a generating AI and have the generating AI perform noise filtering.

[0040] The recognition unit can automatically search for and display relevant information based on the user's utterance. For example, if the user talks about a specific place, the AI ​​can automatically search for and display information about that place. For example, if the user talks about a specific person, the AI ​​can automatically search for and display information about that person. For example, if the user talks about a specific event, the AI ​​can automatically search for and display information about that event. This deepens the user's understanding by displaying relevant information based on the user's utterance. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the user's utterance into a generating AI and have the generating AI perform the search for and display of relevant information.

[0041] The recognition unit can analyze the context of the user's utterance and generate auxiliary information to provide an appropriate translation. For example, if the user speaks in a specific context, the recognition unit can generate auxiliary information for the AI ​​to provide an appropriate translation based on that context. The recognition unit can also generate auxiliary information for the AI ​​to provide an appropriate translation based on a specific situation if the user speaks in a specific context. The recognition unit can also generate auxiliary information for the AI ​​to provide an appropriate translation based on a specific topic if the user speaks on that topic. By analyzing the context of the utterance, a more appropriate translation can be provided. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input contextual data of the user's utterance into a generating AI and have the generating AI perform the generation of auxiliary information.

[0042] The translation unit can provide customization features to appropriately handle technical terms and slang during translation. For example, if a user uses technical terms, the translation unit can provide customization features to enable the AI ​​to appropriately translate those terms. The translation unit can also provide customization features to enable the AI ​​to appropriately translate slang if a user uses it. The translation unit can also provide customization features to enable the AI ​​to appropriately translate industry-specific jargon if a user uses it. This improves the accuracy of translation by appropriately handling technical terms and slang. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input data on technical terms and slang into a generating AI and have the generating AI perform appropriate translations.

[0043] The translation unit can check and correct the grammatical and contextual consistency of the translation results in real time. For example, the translation unit can check the grammar of the translation results in real time and correct any errors. The translation unit can also check the contextual consistency of the translation results in real time and correct any errors. The translation unit can also check the grammatical and contextual consistency of the translation results in real time and correct any errors. This improves the quality of the translation by checking and correcting the grammatical and contextual consistency of the translation results. Some or all of the above processes in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the grammatical and contextual data of the translation results into a generating AI and have the generating AI perform consistency checks and corrections.

[0044] The translation unit can provide more appropriate translations by referring to the user's past translation history during translation. For example, the translation unit can refer to the user's past translation history to provide appropriate translations when the same expression is used. The translation unit can also refer to the user's past translation history to provide appropriate translations when used in the same context. The translation unit can also refer to the user's past translation history to provide appropriate translations when used on the same topic. This allows for more appropriate translations to be provided by referring to the user's past translation history. Some or all of the above processes in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the user's past translation history data into a generating AI and have the generating AI perform an appropriate translation.

[0045] The translation unit can display the translation results in multiple languages ​​simultaneously and allow the user to select one. For example, the translation unit can display the translation results in English and Japanese simultaneously and allow the user to select one. For example, the translation unit can also display the translation results in French and German simultaneously and allow the user to select one. For example, the translation unit can also display the translation results in Spanish and Chinese simultaneously and allow the user to select one. This allows the user to select by displaying the results in multiple languages ​​simultaneously. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input the translation results into a generating AI and have the generating AI perform the display in multiple languages.

[0046] The display unit can track the user's gaze during display and dynamically change the optimal display position. For example, if the user is looking at the left side of the screen, the display unit moves the displayed content to the left. For example, if the user is looking at the right side of the screen, the display unit can also move the displayed content to the right. For example, if the user is looking at the center of the screen, the display unit can fix the displayed content in the center. This makes the displayed content easier to see by tracking the user's gaze and dynamically changing the optimal display position. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's gaze data into a generating AI and cause the generating AI to dynamically change the optimal display position.

[0047] The display unit can provide a function to customize the displayed content to match the user's visual characteristics. For example, if the user has color blindness, the display unit can provide display content that is compatible with color blindness. For example, if the user has impaired vision, the display unit can also provide display content in a larger font size. For example, if the user is visually fatigued, the display unit can also provide display content in eye-friendly colors. By customizing the displayed content to match the user's visual characteristics, visibility is improved. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's visual characteristics data into a generating AI and have the generating AI execute the customized display content.

[0048] The display unit can detect ambient light around the user and automatically adjust the display brightness when displaying information. For example, if the user is in a dark place, the display unit can automatically increase the display brightness. For example, if the user is in a bright place, the display unit can also automatically decrease the display brightness. For example, if the user is in a place of moderate brightness, the display unit can also appropriately adjust the display brightness. By automatically adjusting the display brightness according to the ambient light, visibility is improved. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input ambient light data into a generating AI and have the generating AI perform automatic brightness adjustment.

[0049] The display unit can be made accessible to visually impaired users by adding a function to read the displayed content aloud. For example, the display unit can provide a function to read the displayed content aloud, enabling visually impaired users to obtain information. For example, the display unit can also provide a function to read the displayed content aloud, enabling visually impaired users to check the translation results. For example, the display unit can provide a function to read the displayed content aloud, enabling visually impaired users to communicate smoothly. This allows information to be provided to visually impaired users by reading the displayed content aloud. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the displayed content into a generating AI and have the generating AI perform the voice reading.

[0050] The interface unit can suggest the optimal operation method by referring to the user's past operation history when the interface is operated. For example, the interface unit may prioritize suggesting operation methods that the user has frequently used in the past. The interface unit can also learn and suggest the optimal operation method from the user's past operation history. For example, when the user performs a specific operation, the interface unit can suggest the optimal operation method based on the user's past operation history. In this way, the optimal operation method can be suggested by referring to the user's past operation history. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's past operation history data into a generating AI and have the generating AI perform the task of suggesting the optimal operation method.

[0051] The interface unit can provide the optimal operating method when the interface is operated, taking into account the user's device information. For example, if the user is using a smartphone, the interface unit can provide an operating method that is adapted to the screen size. For example, if the user is using a tablet, the interface unit can also provide an operating method optimized for a larger screen. For example, if the user is using a smartwatch, the interface unit can also provide a simple and highly visible operating method. In this way, the optimal operating method can be provided by taking into account the user's device information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's device information into a generating AI and have the generating AI perform the task of providing the optimal operating method.

[0052] The multilingual support unit can provide optimal support by referring to the user's past language usage history when providing multilingual support. For example, the multilingual support unit can refer to the user's past language usage history and prioritize support for frequently used languages. The multilingual support unit can also refer to the user's past language usage history and prioritize support for languages ​​used in specific situations. The multilingual support unit can also refer to the user's past language usage history and prioritize support for languages ​​used in specific topics. This ensures that optimal support is provided by referring to the user's past language usage history. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input the user's past language usage history data into a generating AI and have the generating AI perform the task of providing optimal support.

[0053] The multilingual support unit can provide optimal support by considering the user's geographical location when providing multilingual support. For example, if the user is in a specific country, the multilingual support unit will prioritize supporting the main language of that country. For example, if the user is in a specific region, the multilingual support unit can also prioritize supporting the main language of that region. For example, if the user is in a specific city, the multilingual support unit can also prioritize supporting the main language of that city. This ensures that optimal support is provided by considering the user's geographical location. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing optimal support.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The recognition unit can automatically search for and display relevant information based on the user's utterances. For example, if a user talks about a specific place, the AI ​​can automatically search for and display information about that place. If a user talks about a specific person, the AI ​​can also automatically search for and display information about that person. If a user talks about a specific event, the AI ​​can also automatically search for and display information about that event. This allows for a deeper understanding of the user by displaying relevant information based on their utterances.

[0056] The recognition unit can dynamically change its recognition algorithm according to the user's speaking speed and volume. For example, if the user speaks quickly, the AI ​​can apply a high-speed recognition algorithm to accurately recognize the speech. If the user speaks slowly, the AI ​​can apply a low-speed recognition algorithm to accurately capture the details of the speech. If the user speaks loudly, the AI ​​can apply a recognition algorithm that adjusts to the volume to accurately recognize the speech. In this way, recognition accuracy is improved by changing the recognition algorithm according to the user's speaking speed and volume.

[0057] The translation department can provide customization features to properly handle technical terms and slang during translation. For example, if a user uses technical terms, it can provide customization features to ensure the AI ​​translates those terms appropriately. If a user uses slang, it can also provide customization features to ensure the AI ​​translates that slang appropriately. If a user uses specific industry jargon, it can also provide customization features to ensure the AI ​​translates that industry jargon appropriately. This improves translation accuracy by properly handling technical terms and slang.

[0058] The display unit can detect ambient light around the user and automatically adjust the display brightness. For example, if the user is in a dark place, the display brightness will automatically increase. If the user is in a bright place, the display brightness can also be automatically decreased. If the user is in a place with moderate brightness, the display brightness can be appropriately adjusted. This automatically adjusts the display brightness according to the surrounding ambient light, improving visibility.

[0059] The interface unit can provide the optimal operating method by considering the user's device information when operating the interface. For example, if the user is using a smartphone, it can provide an operating method adapted to the screen size. If the user is using a tablet, it can also provide an operating method optimized for the larger screen. If the user is using a smartwatch, it can also provide a simple and highly visible operating method. In this way, the optimal operating method can be provided by considering the user's device information.

[0060] The multilingual support department can provide optimal support by considering the user's geographical location. For example, if a user is in a specific country, it can prioritize support in the country's main language. If a user is in a specific region, it can prioritize support in the region's main language. If a user is in a specific city, it can prioritize support in the city's main language. This ensures that optimal support is provided by considering the user's geographical location.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The recognition unit recognizes the user's speech in real time. For example, the recognition unit converts the user's speech into text data using speech recognition technology. The recognition unit can collect the user's speech using a microphone and convert it into text data using a speech recognition algorithm. It can also remove background noise using noise cancellation technology to improve the accuracy of speech recognition. These processes may or may not be performed using AI. Step 2: The translation unit translates the utterances recognized by the recognition unit. For example, the translation unit performs instant translation between multiple languages ​​using natural language processing techniques. The translation unit can translate utterances using text generation AI (e.g., LLM) or multimodal generation AI. It can also improve translation accuracy by considering contextual information. These processes may or may not be performed using AI. Step 3: The display unit displays the content translated by the translation unit. For example, the display unit displays the translation result on the display of a glasses-type device. The display unit can visually display the translation result using glasses-type devices such as AR glasses or smart glasses. It can also track the user's gaze and dynamically change the optimal display position. These processes may or may not be performed using AI.

[0063] (Example of form 2) The translation support system according to an embodiment of the present invention is a system that uses AI technology to translate language in real time and displays it to the user through a glasses-type device. This translation support system uses AI to recognize the content spoken by the user in real time and translate it. The translated content is displayed on the display of the glasses-type device, allowing the user to visually confirm the translation result. This enables smooth communication across language barriers. This system utilizes advanced natural language processing technology and supports instant translation of multiple languages. By recognizing and translating the user's speech in real time, it is expected to improve communication speed, enhance the comprehension of language learners, and revitalize international exchange. Furthermore, its application to a wearable device allows for an intuitively designed user interface, improving ease of use. The target audience includes travelers, international business people, language learners, and all those who need to communicate in a multilingual environment. This eliminates the inconvenience of communication due to language barriers and enables instant and accurate communication. For example, the translation support system recognizes the content spoken by the user in real time and translates it. The translated content is displayed on the display of the glasses-type device, allowing the user to visually confirm the translation result. This enables smooth communication across language barriers. The translation support system can recognize, translate, and display user speech in real time, facilitating seamless communication.

[0064] The translation support system according to this embodiment comprises a recognition unit, a translation unit, and a display unit. The recognition unit recognizes the user's utterance in real time. The recognition unit converts the user's utterance into text data using, for example, speech recognition technology. The recognition unit can, for example, collect the user's utterance using a microphone and convert it into text data using a speech recognition algorithm. The recognition unit can also, for example, remove background noise using noise cancellation technology to improve the accuracy of utterance recognition. Some or all of the above-described processing in the recognition unit may be performed using, for example, AI, or without AI. For example, the recognition unit can analyze speech data using an AI model and generate text data in order to recognize the user's utterance in real time. The translation unit translates the utterance recognized by the recognition unit. The translation unit performs instant translation of multiple languages ​​using, for example, natural language processing technology. The translation unit can, for example, translate utterances using text generation AI (e.g., LLM). The translation unit can also, for example, translate the content of utterances using multimodal generation AI. The translation unit may, for example, perform translations while considering contextual information to improve translation accuracy. Some or all of the above-described processes in the translation unit may be performed using AI, for example, or not. For example, the translation unit may use an AI model to analyze text data and generate a translation result in order to translate the user's utterance. The display unit displays the content translated by the translation unit. The display unit may, for example, display the translation result on the display of a glasses-type device. The display unit may, for example, use a glasses-type device such as AR glasses or smart glasses to visually display the translation result. The display unit may, for example, track the user's gaze and dynamically change the optimal display position. Some or all of the above-described processes in the display unit may, for example, be performed using AI, for example, or not. For example, the display unit may use an AI model to optimize the display content in order to display the translation result and provide it to the user. As a result, the translation support system according to the embodiment can achieve smooth communication by recognizing, translating, and displaying the user's utterance in real time.

[0065] The recognition unit recognizes user speech in real time. For example, it converts user speech into text data using speech recognition technology. Specifically, the recognition unit collects user speech using a high-sensitivity microphone, and the collected audio data is analyzed by a speech recognition algorithm. The speech recognition algorithm analyzes the audio waveform and decomposes it into phonemes and words, converting the speech content into text data. Furthermore, the recognition unit uses noise cancellation technology to remove background noise and improve the accuracy of speech recognition. Noise cancellation technology detects ambient noise in real time and removes it, enabling the acquisition of clear audio data. Some or all of the above processing in the recognition unit is often performed using AI. For example, an AI model can be used to analyze audio data and generate text data. By learning from a large amount of audio data, the AI ​​model can handle various speech patterns and accents. This allows the recognition unit to recognize user speech with high accuracy in real time. Furthermore, the recognition unit can analyze user speech content based on context and perform corrections to reduce misrecognition. For example, recognition accuracy can be improved by prioritizing the recognition of words and phrases that are likely to be predicted in a specific context. This allows the recognition unit to accurately and quickly convert the user's utterance into text data, which can then be used for subsequent translation processing.

[0066] The translation unit translates the utterances recognized by the recognition unit. The translation unit performs real-time translation between multiple languages, for example, using natural language processing techniques. Specifically, the translation unit analyzes the text data provided by the recognition unit and processes it for translation into the target language. The translation unit can translate utterances using text generation AI (e.g., LLM). Text generation AI learns from large amounts of language data, understanding the appropriate use of grammar and vocabulary, and generating highly accurate translations. Furthermore, the translation unit can also translate the content of utterances using multimodal generation AI. Multimodal generation AI achieves a richer contextual understanding by integrating and analyzing multiple modalities, including not only speech and text, but also images and videos. This allows the translation unit to provide translations that accurately capture the nuances and intent of the utterances. To improve translation accuracy, the translation unit can also consider contextual information during translation. For example, by considering the context before and after the conversation and the usage of specific technical terms, it can generate more natural and appropriate translations. Some or all of the above-mentioned processes in the translation unit are often performed using AI. By using AI models, the translation unit can translate user speech quickly and accurately, providing translation results in real time. This allows the translation unit to translate user speech in multiple languages, supporting smooth communication.

[0067] The display unit displays the content translated by the translation unit. For example, the display unit displays the translation results on the screen of a glasses-type device. Specifically, the display unit can visually display the translation results using glasses-type devices such as AR glasses or smart glasses. This allows users to view the translation results naturally and confirm the translated content without interrupting the flow of communication. The display unit can also track the user's gaze and dynamically change the optimal display position. By using eye-tracking technology, it can detect where the user is looking and display the translation results in the most easily viewable position. This allows users to confirm the translation results without moving their eyes. Some or all of the above processing in the display unit is often performed using AI. For example, an AI model can be used to optimize the display content and provide it to the user. The AI ​​model analyzes the user's gaze and movements and selects the optimal display method, thereby improving the user experience. Furthermore, the display unit can customize display settings according to the user's preferences. For example, by changing the font size and display color, the translation results can be displayed in the most easily viewable format for the user. This allows the display unit to provide users with an intuitive and user-friendly interface, enhancing the convenience of the translation support system.

[0068] The interface unit can intuitively design the user interface. The interface unit can simplify the operating procedure based on the results of usability tests, for example. The interface unit can also analyze the user's operation history and suggest the optimal operating method, for example. The interface unit can also dynamically change the operating procedure according to the user's operation status, for example. This improves usability through an intuitive user interface. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's operation history into an AI model and suggest the optimal operating method.

[0069] The multilingual support unit can support communication in a multilingual environment. For example, it can support major international languages ​​and specific regional languages. The multilingual support unit can also automatically select the appropriate language based on the user's language settings. Furthermore, it can dynamically change the appropriate language based on the user's utterances. This facilitates smooth communication in a multilingual environment. Some or all of the above-described processes in the multilingual support unit may be performed using AI, or not. For example, the multilingual support unit can input the user's utterances into an AI model and automatically select the appropriate language.

[0070] The recognition unit can recognize user speech in real time. The recognition unit converts user speech into text data using, for example, speech recognition technology. The recognition unit can collect user speech using, for example, a microphone and convert it into text data using a speech recognition algorithm. The recognition unit can also improve the accuracy of speech recognition by removing background noise using, for example, noise cancellation technology. This enables instant translation by recognizing user speech in real time. Some or all of the above processing in the recognition unit may be performed using, for example, AI, or not using AI. For example, the recognition unit can analyze speech data using an AI model and generate text data in order to recognize user speech in real time.

[0071] The translation unit can support real-time translation of multiple languages. For example, the translation unit can perform real-time translation of multiple languages ​​using natural language processing technology. The translation unit can translate utterances using text generation AI (e.g., LLM). The translation unit can also translate the content of utterances using multimodal generation AI. The translation unit can also consider contextual information to improve translation accuracy. This enables real-time translation of multiple languages, facilitating smooth communication in various languages. Some or all of the above-described processes in the translation unit may be performed using AI, or not. For example, the translation unit can analyze text data using an AI model to translate user utterances and generate translation results.

[0072] The display unit can display the translated content on the display of a glasses-type device. The display unit can visually display the translation results using a glasses-type device such as AR glasses or smart glasses. The display unit can also track the user's gaze and dynamically change the optimal display position. This allows the user to visually confirm the translation results by displaying the translated content on the display of the glasses-type device. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can use an AI model to optimize the display content for displaying the translation results and provide it to the user.

[0073] The recognition unit can estimate the user's emotions and adjust the accuracy of speech recognition based on the estimated emotions. For example, if the user is nervous, the AI ​​in the recognition unit can adjust the speed and volume of speech to improve the accuracy of speech recognition. For example, if the user is relaxed, the AI ​​in the recognition unit can also consider the intonation and pauses of speech to recognize natural speech. For example, if the user is excited, the AI ​​in the recognition unit can also compensate for fluctuations in the speed and volume of speech to maintain the accuracy of speech recognition. This improves recognition accuracy by adjusting the accuracy of speech recognition according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of speech recognition accuracy based on emotions.

[0074] The recognition unit can dynamically change its recognition algorithm according to the user's speaking speed and volume. For example, if the user speaks quickly, the AI ​​can apply a high-speed recognition algorithm to accurately recognize the speech. For example, if the user speaks slowly, the AI ​​can apply a low-speed recognition algorithm to accurately capture the details of the speech. For example, if the user speaks loudly, the AI ​​can apply a volume-dependent recognition algorithm to accurately recognize the speech. This improves recognition accuracy by changing the recognition algorithm according to the user's speaking speed and volume. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the user's speaking speed and volume data into a generating AI and cause the generating AI to dynamically change the recognition algorithm.

[0075] The recognition unit can filter background noise in real time to improve the accuracy of speech recognition. For example, if a user speaks in a noisy place, the AI ​​can filter background noise in real time to accurately recognize the speech. For example, if a user speaks in a windy place, the AI ​​can filter out wind noise to accurately recognize the speech. For example, if a user speaks in a place where music is playing, the AI ​​can filter out the music noise to accurately recognize the speech. This improves the accuracy of speech recognition by filtering out background noise. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input background noise data into a generating AI and have the generating AI perform noise filtering.

[0076] The recognition unit can estimate the user's emotions and adjust the display method of the recognition results based on the estimated user emotions. For example, if the user is nervous, the recognition unit can provide a simple and highly visible display method. For example, if the user is relaxed, the recognition unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the recognition unit can also provide a display method that gets straight to the point. By adjusting the display method of the recognition results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method based on the emotions.

[0077] The recognition unit can automatically search for and display relevant information based on the user's utterance. For example, if the user talks about a specific place, the AI ​​can automatically search for and display information about that place. For example, if the user talks about a specific person, the AI ​​can automatically search for and display information about that person. For example, if the user talks about a specific event, the AI ​​can automatically search for and display information about that event. This deepens the user's understanding by displaying relevant information based on the user's utterance. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input the user's utterance into a generating AI and have the generating AI perform the search for and display of relevant information.

[0078] The recognition unit can analyze the context of the user's utterance and generate auxiliary information to provide an appropriate translation. For example, if the user speaks in a specific context, the recognition unit can generate auxiliary information for the AI ​​to provide an appropriate translation based on that context. The recognition unit can also generate auxiliary information for the AI ​​to provide an appropriate translation based on a specific situation if the user speaks in a specific context. The recognition unit can also generate auxiliary information for the AI ​​to provide an appropriate translation based on a specific topic if the user speaks on that topic. By analyzing the context of the utterance, a more appropriate translation can be provided. Some or all of the above processing in the recognition unit may be performed using AI, for example, or without AI. For example, the recognition unit can input contextual data of the user's utterance into a generating AI and have the generating AI perform the generation of auxiliary information.

[0079] The translation unit can estimate the user's emotions and adjust the translation's expression based on those emotions. For example, if the user is nervous, the translation unit can provide a simple and easy-to-understand expression. If the user is relaxed, the translation unit can also provide an expression that includes detailed information. If the user is in a hurry, the translation unit can also provide an expression that gets straight to the point. By adjusting the translation's expression according to the user's emotions, a more appropriate translation is provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the translation unit may be performed using AI, or not using AI. For example, the translation unit can input user emotion data into a generative AI and have the generative AI perform an adjustment of the translation's expression based on the emotion.

[0080] The translation unit can provide customization features to appropriately handle technical terms and slang during translation. For example, if a user uses technical terms, the translation unit can provide customization features to enable the AI ​​to appropriately translate those terms. The translation unit can also provide customization features to enable the AI ​​to appropriately translate slang if a user uses it. The translation unit can also provide customization features to enable the AI ​​to appropriately translate industry-specific jargon if a user uses it. This improves the accuracy of translation by appropriately handling technical terms and slang. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input data on technical terms and slang into a generating AI and have the generating AI perform appropriate translations.

[0081] The translation unit can check and correct the grammatical and contextual consistency of the translation results in real time. For example, the translation unit can check the grammar of the translation results in real time and correct any errors. The translation unit can also check the contextual consistency of the translation results in real time and correct any errors. The translation unit can also check the grammatical and contextual consistency of the translation results in real time and correct any errors. This improves the quality of the translation by checking and correcting the grammatical and contextual consistency of the translation results. Some or all of the above processes in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the grammatical and contextual data of the translation results into a generating AI and have the generating AI perform consistency checks and corrections.

[0082] The translation unit can estimate the user's emotions and adjust the tone of the translation based on the estimated emotions. For example, if the user is tense, the translation unit will provide a calm tone. If the user is relaxed, the translation unit may also provide a cheerful tone. If the user is in a hurry, the translation unit may also provide a quick and concise tone. By adjusting the tone of the translation according to the user's emotions, a more appropriate translation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input user emotion data into a generative AI and have the generative AI perform emotion-based tone adjustments.

[0083] The translation unit can provide more appropriate translations by referring to the user's past translation history during translation. For example, the translation unit can refer to the user's past translation history to provide appropriate translations when the same expression is used. The translation unit can also refer to the user's past translation history to provide appropriate translations when used in the same context. The translation unit can also refer to the user's past translation history to provide appropriate translations when used on the same topic. This allows for more appropriate translations to be provided by referring to the user's past translation history. Some or all of the above processes in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the user's past translation history data into a generating AI and have the generating AI perform an appropriate translation.

[0084] The translation unit can display the translation results in multiple languages ​​simultaneously and allow the user to select one. For example, the translation unit can display the translation results in English and Japanese simultaneously and allow the user to select one. For example, the translation unit can also display the translation results in French and German simultaneously and allow the user to select one. For example, the translation unit can also display the translation results in Spanish and Chinese simultaneously and allow the user to select one. This allows the user to select by displaying the results in multiple languages ​​simultaneously. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input the translation results into a generating AI and have the generating AI perform the display in multiple languages.

[0085] The display unit can estimate the user's emotions and adjust the font size and color of the displayed content based on the estimated emotions. For example, if the user is tense, the display unit may use a font with calm colors. For example, if the user is relaxed, the display unit may use a font with bright colors. For example, if the user is in a hurry, the display unit may use a large font size. This improves visibility by adjusting the font size and color of the displayed content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into the generative AI and have the generative AI perform adjustments to the font size and color based on the emotions.

[0086] The display unit can track the user's gaze during display and dynamically change the optimal display position. For example, if the user is looking at the left side of the screen, the display unit moves the displayed content to the left. For example, if the user is looking at the right side of the screen, the display unit can also move the displayed content to the right. For example, if the user is looking at the center of the screen, the display unit can fix the displayed content in the center. This makes the displayed content easier to see by tracking the user's gaze and dynamically changing the optimal display position. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's gaze data into a generating AI and cause the generating AI to dynamically change the optimal display position.

[0087] The display unit can provide a function to customize the displayed content to match the user's visual characteristics. For example, if the user has color blindness, the display unit can provide display content that is compatible with color blindness. For example, if the user has impaired vision, the display unit can also provide display content in a larger font size. For example, if the user is visually fatigued, the display unit can also provide display content in eye-friendly colors. By customizing the displayed content to match the user's visual characteristics, visibility is improved. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the user's visual characteristics data into a generating AI and have the generating AI execute the customized display content.

[0088] The display unit can estimate the user's emotions and adjust the layout of the displayed content based on the estimated emotions. For example, if the user is tense, the display unit can provide a simple and highly visible layout. For example, if the user is relaxed, the display unit can also provide a layout that includes detailed information. For example, if the user is in a hurry, the display unit can also provide a layout that gets straight to the point. By adjusting the layout of the displayed content according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input user emotion data into a generative AI and have the generative AI perform layout adjustments based on emotions.

[0089] The display unit can detect ambient light around the user and automatically adjust the display brightness when displaying information. For example, if the user is in a dark place, the display unit can automatically increase the display brightness. For example, if the user is in a bright place, the display unit can also automatically decrease the display brightness. For example, if the user is in a place of moderate brightness, the display unit can also appropriately adjust the display brightness. By automatically adjusting the display brightness according to the ambient light, visibility is improved. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input ambient light data into a generating AI and have the generating AI perform automatic brightness adjustment.

[0090] The display unit can be made accessible to visually impaired users by adding a function to read the displayed content aloud. For example, the display unit can provide a function to read the displayed content aloud, enabling visually impaired users to obtain information. For example, the display unit can also provide a function to read the displayed content aloud, enabling visually impaired users to check the translation results. For example, the display unit can provide a function to read the displayed content aloud, enabling visually impaired users to communicate smoothly. This allows information to be provided to visually impaired users by reading the displayed content aloud. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input the displayed content into a generating AI and have the generating AI perform the voice reading.

[0091] The interface unit can estimate the user's emotions and adjust the interface operation method based on the estimated user emotions. For example, if the user is tense, the interface unit can provide a simple and intuitive operation method. For example, if the user is relaxed, the interface unit can also provide detailed operation options. For example, if the user is in a hurry, the interface unit can also provide shortcuts for quick operation. This improves usability by adjusting the interface operation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the interface unit may be performed using AI, for example, or not using AI. For example, the interface unit can input user emotion data into a generative AI and have the generative AI perform adjustments to the operation method based on emotions.

[0092] The interface unit can suggest the optimal operation method by referring to the user's past operation history when the interface is operated. For example, the interface unit may prioritize suggesting operation methods that the user has frequently used in the past. The interface unit can also learn and suggest the optimal operation method from the user's past operation history. For example, when the user performs a specific operation, the interface unit can suggest the optimal operation method based on the user's past operation history. In this way, the optimal operation method can be suggested by referring to the user's past operation history. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's past operation history data into a generating AI and have the generating AI perform the task of suggesting the optimal operation method.

[0093] The interface unit can estimate the user's emotions and adjust the interface design based on the estimated emotions. For example, if the user is tense, the interface unit may provide a design with calming colors. If the user is relaxed, the interface unit may also provide a design with bright colors. If the user is in a hurry, the interface unit may also provide a simple and highly visible design. By adjusting the interface design according to the user's emotions, usability is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input user emotion data into a generative AI and have the generative AI perform emotion-based design adjustments.

[0094] The interface unit can provide the optimal operating method when the interface is operated, taking into account the user's device information. For example, if the user is using a smartphone, the interface unit can provide an operating method that is adapted to the screen size. For example, if the user is using a tablet, the interface unit can also provide an operating method optimized for a larger screen. For example, if the user is using a smartwatch, the interface unit can also provide a simple and highly visible operating method. In this way, the optimal operating method can be provided by taking into account the user's device information. Some or all of the above processing in the interface unit may be performed using AI, for example, or without AI. For example, the interface unit can input the user's device information into a generating AI and have the generating AI perform the task of providing the optimal operating method.

[0095] The multilingual support unit can estimate the user's emotions and determine the priority of multilingual support based on the estimated emotions. For example, if the user is stressed, the multilingual support unit will prioritize support in the primary language. For example, if the user is relaxed, the multilingual support unit can also support multiple languages ​​simultaneously. For example, if the user is in a hurry, the multilingual support unit can also prioritize support in the most frequently used language. This allows for more appropriate support to be provided by determining the priority of multilingual support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0096] The multilingual support unit can provide optimal support by referring to the user's past language usage history when providing multilingual support. For example, the multilingual support unit can refer to the user's past language usage history and prioritize support for frequently used languages. The multilingual support unit can also refer to the user's past language usage history and prioritize support for languages ​​used in specific situations. The multilingual support unit can also refer to the user's past language usage history and prioritize support for languages ​​used in specific topics. This ensures that optimal support is provided by referring to the user's past language usage history. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input the user's past language usage history data into a generating AI and have the generating AI perform the task of providing optimal support.

[0097] The multilingual support unit can estimate the user's emotions and adjust the display method of the multilingual support based on the estimated user emotions. For example, if the user is nervous, the multilingual support unit can provide a simple and highly visible display method. For example, if the user is relaxed, the multilingual support unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the multilingual support unit can also provide a display method that gets straight to the point. By adjusting the display method of the multilingual support according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the display method based on emotions.

[0098] The multilingual support unit can provide optimal support by considering the user's geographical location when providing multilingual support. For example, if the user is in a specific country, the multilingual support unit will prioritize supporting the main language of that country. For example, if the user is in a specific region, the multilingual support unit can also prioritize supporting the main language of that region. For example, if the user is in a specific city, the multilingual support unit can also prioritize supporting the main language of that city. This ensures that optimal support is provided by considering the user's geographical location. Some or all of the above processing in the multilingual support unit may be performed using AI, for example, or without AI. For example, the multilingual support unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing optimal support.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The recognition unit can automatically search for and display relevant information based on the user's utterances. For example, if a user talks about a specific place, the AI ​​can automatically search for and display information about that place. If a user talks about a specific person, the AI ​​can also automatically search for and display information about that person. If a user talks about a specific event, the AI ​​can also automatically search for and display information about that event. This allows for a deeper understanding of the user by displaying relevant information based on their utterances.

[0101] The interface can estimate the user's emotions and adjust how the interface is operated based on those emotions. For example, if the user is tense, it can provide simple and intuitive operation methods. If the user is relaxed, it can provide detailed operation options. If the user is in a hurry, it can provide shortcuts for quick operation. In this way, the usability is improved by adjusting the interface operation methods according to the user's emotions.

[0102] The multilingual support unit can estimate the user's emotions and prioritize multilingual support based on those emotions. For example, if the user is stressed, it will prioritize support in the primary language. If the user is relaxed, it can support multiple languages ​​simultaneously. If the user is in a hurry, it can prioritize support in the most frequently used language. This allows for more appropriate support to be provided by prioritizing multilingual support according to the user's emotions.

[0103] The display unit can estimate the user's emotions and adjust the font size and color of the displayed content based on those emotions. For example, if the user is nervous, a calm colored font can be used. If the user is relaxed, a bright colored font can be used. If the user is in a hurry, a larger font size can be used. By adjusting the font size and color of the displayed content according to the user's emotions, visibility is improved.

[0104] The translation unit can estimate the user's emotions and adjust the tone of the translation based on that estimation. For example, if the user is nervous, it can provide a calm tone of translation. If the user is relaxed, it can provide a cheerful tone of translation. If the user is in a hurry, it can provide a quick and concise tone of translation. By adjusting the tone of the translation according to the user's emotions, a more appropriate translation can be provided.

[0105] The recognition unit can dynamically change its recognition algorithm according to the user's speaking speed and volume. For example, if the user speaks quickly, the AI ​​can apply a high-speed recognition algorithm to accurately recognize the speech. If the user speaks slowly, the AI ​​can apply a low-speed recognition algorithm to accurately capture the details of the speech. If the user speaks loudly, the AI ​​can apply a recognition algorithm that adjusts to the volume to accurately recognize the speech. In this way, recognition accuracy is improved by changing the recognition algorithm according to the user's speaking speed and volume.

[0106] The translation department can provide customization features to properly handle technical terms and slang during translation. For example, if a user uses technical terms, it can provide customization features to ensure the AI ​​translates those terms appropriately. If a user uses slang, it can also provide customization features to ensure the AI ​​translates that slang appropriately. If a user uses specific industry jargon, it can also provide customization features to ensure the AI ​​translates that industry jargon appropriately. This improves translation accuracy by properly handling technical terms and slang.

[0107] The display unit can detect ambient light around the user and automatically adjust the display brightness. For example, if the user is in a dark place, the display brightness will automatically increase. If the user is in a bright place, the display brightness can also be automatically decreased. If the user is in a place with moderate brightness, the display brightness can be appropriately adjusted. This automatically adjusts the display brightness according to the surrounding ambient light, improving visibility.

[0108] The interface unit can provide the optimal operating method by considering the user's device information when operating the interface. For example, if the user is using a smartphone, it can provide an operating method adapted to the screen size. If the user is using a tablet, it can also provide an operating method optimized for the larger screen. If the user is using a smartwatch, it can also provide a simple and highly visible operating method. In this way, the optimal operating method can be provided by considering the user's device information.

[0109] The multilingual support department can provide optimal support by considering the user's geographical location. For example, if a user is in a specific country, it can prioritize support in the country's main language. If a user is in a specific region, it can prioritize support in the region's main language. If a user is in a specific city, it can prioritize support in the city's main language. This ensures that optimal support is provided by considering the user's geographical location.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The recognition unit recognizes the user's speech in real time. For example, the recognition unit converts the user's speech into text data using speech recognition technology. The recognition unit can collect the user's speech using a microphone and convert it into text data using a speech recognition algorithm. It can also remove background noise using noise cancellation technology to improve the accuracy of speech recognition. These processes may or may not be performed using AI. Step 2: The translation unit translates the utterances recognized by the recognition unit. For example, the translation unit performs instant translation between multiple languages ​​using natural language processing techniques. The translation unit can translate utterances using text generation AI (e.g., LLM) or multimodal generation AI. It can also improve translation accuracy by considering contextual information. These processes may or may not be performed using AI. Step 3: The display unit displays the content translated by the translation unit. For example, the display unit displays the translation result on the display of a glasses-type device. The display unit can visually display the translation result using glasses-type devices such as AR glasses or smart glasses. It can also track the user's gaze and dynamically change the optimal display position. These processes may or may not be performed using AI.

[0112] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0115] Each of the multiple elements described above, including the recognition unit, translation unit, display unit, interface unit, and multilingual support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recognition unit is implemented by the microphone 38B and control unit 46A of the smart device 14 and recognizes the user's speech in real time. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12 and translates the recognized speech. The display unit is implemented by the display 40A of the smart device 14 and displays the translation result. The interface unit is implemented by the control unit 46A of the smart device 14 and intuitively designs the user interface. The multilingual support unit is implemented by the specific processing unit 290 of the data processing unit 12 and supports communication in a multilingual environment. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the recognition unit, translation unit, display unit, interface unit, and multilingual support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the recognition unit is implemented by the microphone 238 and control unit 46A of the smart glasses 214, and recognizes the user's speech in real time. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12, and translates the recognized speech. The display unit is implemented by the display of the smart glasses 214, and displays the translation result. The interface unit is implemented by the control unit 46A of the smart glasses 214, and intuitively designs the user interface. The multilingual support unit is implemented by the specific processing unit 290 of the data processing unit 12, and supports communication in a multilingual environment. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the recognition unit, translation unit, display unit, interface unit, and multilingual support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recognition unit is implemented by the microphone 238 and control unit 46A of the headset terminal 314, and recognizes the user's speech in real time. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12, and translates the recognized speech. The display unit is implemented by the display 343 of the headset terminal 314, and displays the translation result. The interface unit is implemented by the control unit 46A of the headset terminal 314, and intuitively designs the user interface. The multilingual support unit is implemented by the specific processing unit 290 of the data processing unit 12, and supports communication in a multilingual environment. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 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.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the recognition unit, translation unit, display unit, interface unit, and multilingual support unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the recognition unit is implemented by the microphone 238 and control unit 46A of the robot 414, and recognizes the user's speech in real time. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12, and translates the recognized speech. The display unit is implemented by the display of the robot 414, and displays the translation result. The interface unit is implemented by the control unit 46A of the robot 414, and intuitively designs the user interface. The multilingual support unit is implemented by the specific processing unit 290 of the data processing unit 12, and supports communication in a multilingual environment. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0165] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) A recognition unit that recognizes user speech in real time, A translation unit that translates the utterance recognized by the recognition unit, The system includes a display unit that displays the content translated by the translation unit. A system characterized by the following features. (Note 2) It features an interface section for intuitively designing the user interface. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a multilingual support unit to facilitate communication in a multilingual environment. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned recognition unit, Recognize user speech in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned translation department, Supports instant translation of multiple languages The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is The translated content is displayed on the display of the glasses-type device. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned recognition unit, It estimates the user's emotions and adjusts the accuracy of speech recognition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned recognition unit, The recognition algorithm is dynamically changed according to the user's speaking speed and volume. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recognition unit, Filtering background noise in real time improves speech recognition accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recognition unit, It estimates the user's emotions and adjusts how the recognition results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recognition unit, Based on the user's utterances, the system automatically searches for and displays relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recognition unit, It analyzes the context of the user's utterance and generates auxiliary information to provide an appropriate translation. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned translation department, It estimates the user's emotions and adjusts the translation's expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned translation department, It provides customization features to properly handle technical terms and slang during translation. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned translation department, The grammatical and contextual consistency of the translation results is checked and corrected in real time. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned translation department, It estimates the user's emotions and adjusts the tone of the translation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned translation department, During translation, the system refers to the user's past translation history to provide more appropriate translations. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned translation department, The translation results are displayed simultaneously in multiple languages, allowing the user to select the language they prefer. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is It estimates the user's emotions and adjusts the font size and color of the displayed content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is When displayed, it tracks the user's gaze and dynamically changes the optimal display position. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is It provides a function to customize the displayed content to suit the user's visual characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is It estimates the user's emotions and adjusts the layout of the displayed content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is When displaying the screen, it detects the ambient light surrounding the user and automatically adjusts the display brightness. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is A feature has been added to read the displayed content aloud, making it accessible to visually impaired users. The system described in Appendix 1, characterized by the features described herein. (Note 25) The interface unit is It estimates the user's emotions and adjusts how the interface is operated based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The interface unit is When the user interacts with the interface, the system refers to their past operation history to suggest the optimal method of operation. The system described in Appendix 2, characterized by the features described herein. (Note 27) The interface unit is It estimates the user's emotions and adjusts the interface design based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The interface unit is When operating the interface, the system provides the optimal operating method by taking into account the user's device information. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned multilingual support unit is It estimates user sentiment and determines the priority of multilingual support based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 30) The aforementioned multilingual support unit is When providing multilingual support, we refer to the user's past language usage history to provide optimal support. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned multilingual support unit is It estimates the user's emotions and adjusts how multilingual support is displayed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned multilingual support unit is When providing multilingual support, we take the user's geographical location into consideration to provide optimal support. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A recognition unit that recognizes user speech in real time, A translation unit that translates the utterance recognized by the recognition unit, The system includes a display unit that displays the content translated by the translation unit. A system characterized by the following features.

2. It features an interface section for intuitively designing the user interface. The system according to feature 1.

3. It features a multilingual support unit to facilitate communication in a multilingual environment. The system according to feature 1.

4. The aforementioned recognition unit, Recognize user speech in real time. The system according to feature 1.

5. The aforementioned translation department, Supports instant translation of multiple languages The system according to feature 1.

6. The aforementioned display unit is The translated content is displayed on the display of the glasses-type device. The system according to feature 1.

7. The aforementioned recognition unit, It estimates the user's emotions and adjusts the accuracy of speech recognition based on the estimated emotions. The system according to feature 1.

8. The aforementioned recognition unit, The recognition algorithm is dynamically changed according to the user's speaking speed and volume. The system according to feature 1.

9. The aforementioned recognition unit, Filtering background noise in real time improves speech recognition accuracy. The system according to feature 1.

10. The aforementioned recognition unit, It estimates the user's emotions and adjusts how the recognition results are displayed based on the estimated emotions. The system according to feature 1.

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