system

Abstract

The system according to this embodiment aims to enable smooth communication among people who do not understand foreign languages ​​or sign language. [Solution] The system according to the embodiment comprises a capture unit, an analysis unit, a translation unit, a display unit, and an audio output unit. The capture unit captures information. The analysis unit analyzes the information captured by the capture unit. The translation unit translates the information analyzed by the analysis unit. The display unit displays the information translated by the translation unit. The audio output unit outputs the audio 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 persona chatbot control method performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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 prior art, there is a problem that it is difficult for people who cannot understand foreign languages or sign language to communicate smoothly.

[0005] The system according to the embodiment aims to enable people who cannot understand foreign languages or sign language to communicate smoothly.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a capture unit, an analysis unit, a translation unit, a display unit, and an audio output unit. The capture unit captures information. The analysis unit analyzes the information captured by the capture unit. The translation unit translates the information analyzed by the analysis unit. The display unit displays the information translated by the translation unit. The audio output unit outputs the audio translated by the translation unit. [Effects of the Invention]

[0007] The system according to this embodiment enables people who do not understand foreign languages ​​or sign language to communicate smoothly. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also 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 glasses system according to an embodiment of the present invention is a product for people who cannot read or speak foreign languages ​​or sign language. This translation glasses system utilizes generative AI to perform appropriate translation simultaneously with text analysis and language analysis, enabling information to be conveyed in an expression that suits the user's needs, thereby facilitating smooth communication. First, the user visually receives information that includes foreign languages ​​or sign language. For example, this could include foreign language books, movies, or videos without sign language commentary. This information is captured by cameras and sensors built into the translation glasses. Next, the generative AI analyzes the captured information. The generative AI performs text analysis and language analysis to generate an appropriate translation. For example, it can translate foreign language text into Japanese or convert sign language movements into text. In this process, the generative AI selects an expression that suits the user's needs and conveys the information. The translated information is displayed on the display of the translation glasses. For example, foreign language text is translated into Japanese and displayed on the display. In addition, because it has a built-in bone conduction speaker, it is also possible to convey the translated audio to the user. The voice tone of the audio is selectable, and it is also possible to use voice actors as a paid item. This system enables people who cannot read or speak foreign languages ​​or sign language to understand information without stress and communicate smoothly. For example, even if you don't understand the local language while traveling, using the translation glasses makes it easier to understand local information. Also, when watching videos without sign language commentary, using the translation glasses allows you to understand the content of the sign language. Furthermore, the generating AI supports multiple languages, so it can translate smoothly regardless of what language the user speaks. This is expected to contribute to solving various issues such as promoting intercultural understanding, facilitating international cooperation, and improving the quality of education and medical care. In short, the translation glasses system enables people who cannot read or speak foreign languages ​​or sign language to understand information without stress and communicate smoothly.

[0029] The translation glasses system according to this embodiment comprises a capture unit, an analysis unit, a translation unit, a display unit, and an audio output unit. The capture unit captures information. For example, the capture unit captures image information using a camera. The capture unit can also capture audio information using a microphone. Furthermore, the capture unit can also capture sign language movements using sensors. For example, the capture unit captures text from a foreign language book using a camera. The capture unit can also capture foreign language audio using a microphone. Furthermore, the capture unit can capture sign language movements using sensors. The analysis unit analyzes the information captured by the capture unit. The analysis unit performs character analysis and language analysis using a generation AI. For example, the analysis unit analyzes foreign language text using a generation AI and translates it into Japanese. Furthermore, the analysis unit can analyze sign language movements using a generation AI and convert them into text. Furthermore, the analysis unit can analyze audio information using a generation AI and convert it into text. The translation unit translates the information analyzed by the analysis unit. The translation unit generates an appropriate translation using a generation AI. For example, the translation unit uses generative AI to translate foreign language text into Japanese. The translation unit can also use generative AI to translate sign language movements into text. Furthermore, the translation unit can also use generative AI to translate audio information. The display unit displays the information translated by the translation unit. The display unit displays information using a display. For example, the display unit translates foreign language text into Japanese and displays it on the display. The display unit can also translate sign language movements into text and display it on the display. Furthermore, the display unit can translate audio information into text and display it on the display. The audio output unit outputs the audio translated by the translation unit. The audio output unit outputs audio using a speaker. For example, the audio output unit translates foreign language audio into Japanese and outputs it through the speaker. Furthermore, the audio output unit can also translate sign language movements into audio and output it through the speaker. Furthermore, the audio output unit can also translate text information into audio and output it through the speaker.As a result, the translation glasses system according to this embodiment can achieve smooth communication by capturing, analyzing, translating, displaying, and outputting information.

[0030] The capture unit captures information. For example, the capture unit captures image information using a camera. Specifically, the high-resolution camera mounted on the capture unit can clearly capture text information from books, signs, menus, etc. The camera has autofocus and image stabilization functions, allowing it to acquire stable images even when the user is moving. The capture unit can also capture audio information using a microphone. The microphone has a noise-canceling function to reduce ambient noise, allowing it to capture clear audio. Furthermore, the capture unit can also capture sign language movements using sensors. The sensors track the user's hand movements with high precision, accurately capturing the subtle movements and gestures of sign language. For example, when the capture unit captures text from a foreign language book using a camera, it converts the text into digital data using OCR (optical character recognition) technology. Also, when the capture unit captures foreign language audio using a microphone, it can convert the audio into text using speech recognition technology. Furthermore, when the capture unit captures sign language movements using sensors, it can convert the sign language into text using motion recognition technology. This allows the capture unit to capture diverse information with high accuracy and provide the data necessary for the next analysis step.

[0031] The analysis unit analyzes the information captured by the capture unit. The analysis unit performs character analysis and language analysis using generative AI. Specifically, the analysis unit analyzes foreign language text using generative AI and translates it into Japanese. The generative AI can understand context and meaning by utilizing natural language processing technology and generate appropriate translations. The analysis unit can also analyze sign language movements using generative AI and convert them into text. For sign language movements, the generative AI analyzes the movement patterns based on data acquired from sensors and generates corresponding text. Furthermore, the analysis unit can also analyze audio information using generative AI and convert it into text. Audio information is analyzed using speech recognition technology, and the generative AI understands the context and generates accurate text. For example, when the analysis unit analyzes foreign language text, the generative AI considers differences in grammar and vocabulary and translates it into natural-sounding Japanese. Also, when analyzing sign language movements, the generative AI analyzes data such as hand movements, position, and speed and generates corresponding Japanese text. When analyzing audio information, the generative AI considers the intonation and accent of the speech and generates accurate text. This allows the analysis unit to quickly and accurately analyze the captured information and provide the data necessary for the next translation step.

[0032] The translation unit translates the information analyzed by the analysis unit. The translation unit generates appropriate translations using generative AI. Specifically, the translation unit uses generative AI to translate foreign language text into Japanese. The generative AI can understand context and meaning and translate into natural-sounding Japanese. The translation unit can also use generative AI to translate sign language movements into text. The generative AI translates sign language movements into appropriate Japanese based on the text generated by the analysis unit. Furthermore, the translation unit can also translate audio information using generative AI. The generative AI translates audio information into appropriate Japanese based on the text generated by the analysis unit. For example, when the translation unit translates foreign language text, the generative AI considers differences in grammar and vocabulary to produce natural-sounding Japanese. When translating sign language movements, the generative AI analyzes data such as hand movements, position, and speed to generate corresponding Japanese text. When translating audio information, the generative AI considers the intonation and accent of the speech to generate accurate Japanese text. This allows the translation unit to translate the analyzed information quickly and accurately, providing the data necessary for the next display step.

[0033] The display unit displays information translated by the translation unit. The display unit uses a display to show information. Specifically, the display unit translates foreign language text into Japanese and displays it on the display. The display is high-resolution and highly visible, allowing users to easily check the information. The display unit can also translate sign language movements into text and display it on the display. The sign language movements are displayed on the display based on the text generated by the translation unit. Furthermore, the display unit can also translate audio information into text and display it on the display. The audio information is displayed on the display based on the text generated by the translation unit. For example, when the display unit translates foreign language text into Japanese and displays it on the display, it adjusts the font size and color to improve visibility. Also, when translating sign language movements into text and displaying it on the display, it displays the text corresponding to the sign language movements in real time. When translating audio information into text and displaying it on the display, it displays text that accurately reflects the content of the audio. In this way, the display unit can visually provide translated information to the user and support smooth communication.

[0034] The audio output unit outputs the audio translated by the translation unit. The audio output unit outputs the audio using a speaker. Specifically, the audio output unit translates foreign language audio into Japanese and outputs it through the speaker. The speaker provides high-quality, clear audio that is easily understandable to the user. The audio output unit can also translate sign language movements into audio and output it through the speaker. The sign language movements are converted into audio based on the text generated by the translation unit and output through the speaker. Furthermore, the audio output unit can also translate text information into audio and output it through the speaker. The text information is converted into audio based on the text generated by the translation unit and output through the speaker. For example, when the audio output unit translates foreign language audio into Japanese and outputs it through the speaker, it uses speech synthesis technology to generate natural-sounding audio. Also, when translating sign language movements into audio and outputting it through the speaker, it generates audio corresponding to the sign language movements in real time. When translating text information into audio and outputting it through the speaker, it generates audio that accurately reflects the content of the text. In this way, the audio output unit can provide translated information to the user audibly and support smooth communication.

[0035] The selection unit chooses expressions that meet the user's needs. For example, the selection unit analyzes the user's behavior history and selects appropriate expressions. For example, the selection unit selects appropriate expressions based on expressions the user has used in the past. The selection unit can also select appropriate expressions based on the results of user surveys. Furthermore, the selection unit can learn the user's preferences and select appropriate expressions. For example, the selection unit analyzes the user's past selection history and selects appropriate expressions. Furthermore, the selection unit can select appropriate expressions based on the results of user surveys. Furthermore, the selection unit can learn the user's preferences and select appropriate expressions. As a result, the selection unit can achieve more effective information transmission by selecting expressions that meet the user's needs.

[0036] The multilingual support unit performs multilingual support. The multilingual support unit uses generative AI to perform multilingual support. For example, the multilingual support unit uses generative AI to support multiple languages. For example, the multilingual support unit uses generative AI to support languages ​​such as English, Japanese, and French. Furthermore, the multilingual support unit can also use generative AI to improve translation accuracy. For example, the multilingual support unit uses generative AI to translate specialized terminology. Additionally, the multilingual support unit can use generative AI to perform contextually appropriate translations. For example, the multilingual support unit uses generative AI to perform contextually appropriate translations. As a result, the multilingual support unit enables smooth communication between users who speak different languages ​​by performing multilingual support.

[0037] The learning unit learns the user's preferences and past conversation history. The learning unit learns the user's preferences and past conversation history using generative AI. For example, the learning unit learns the user's past choice history using generative AI. For example, the learning unit learns the user's past conversation history using generative AI. The learning unit can also learn the user's preferences using generative AI. For example, the learning unit learns the user's survey results using generative AI. The learning unit can also learn the user's behavior history using generative AI. For example, the learning unit learns the user's past choice history using generative AI. The learning unit can also learn the user's past conversation history using generative AI. As a result, the learning unit can provide more personalized information by learning the user's preferences and past conversation history.

[0038] The bone conduction speaker unit incorporates a bone conduction speaker. The bone conduction speaker unit transmits sound using the bone conduction mechanism. For example, the bone conduction speaker unit transmits sound directly to the user using the bone conduction mechanism. For example, the bone conduction speaker unit transmits sound through the user's skull using the bone conduction mechanism. Furthermore, the bone conduction speaker unit can also transmit sound directly to the user's inner ear using the bone conduction mechanism. For example, the bone conduction speaker unit transmits sound directly to the user's inner ear using the bone conduction mechanism. Furthermore, the bone conduction speaker unit can also transmit sound directly to the user's auditory nerve using the bone conduction mechanism. For example, the bone conduction speaker unit transmits sound directly to the user's auditory nerve using the bone conduction mechanism. Thus, by incorporating a bone conduction speaker, the bone conduction speaker unit can directly transmit audio information to the user.

[0039] The voice selection unit allows the user to select the voice tone. The voice selection unit selects the voice tone according to the user's preference. For example, the voice selection unit can select a male voice, a female voice, a robot voice, etc., according to the user's preference. For example, the voice selection unit can select a male voice according to the user's preference. The voice selection unit can also select a female voice according to the user's preference. Furthermore, the voice selection unit can also select a robot voice according to the user's preference. For example, the voice selection unit can select a robot voice according to the user's preference. Furthermore, the voice selection unit can also select a voice actor's voice according to the user's preference. For example, the voice selection unit can select a voice actor's voice according to the user's preference. In this way, by allowing the user to select the voice tone, the voice selection unit can achieve voice output that matches the user's preference.

[0040] The capture unit tracks the user's gaze during capture and prioritizes capturing information in the user's line of sight. For example, if the user is looking at a specific text, the capture unit will prioritize capturing that text. The capture unit can also prioritize capturing sign language movements if the user is looking at them. For example, if the user moves their gaze, the capture unit can capture new information in the user's line of sight. In this way, the capture unit can prioritize capturing information in the user's line of sight by tracking their gaze.

[0041] The capture unit analyzes ambient sounds during capture, removes noise, and captures information. For example, the capture unit can analyze ambient noise, remove noise, and capture clear audio information. The capture unit can also analyze ambient sounds and prioritize the capture of important audio information. For example, the capture unit can analyze ambient sounds, remove noise, and capture sign language movements. In this way, the capture unit can capture clear information by analyzing ambient sounds and removing noise.

[0042] The capture unit prioritizes capturing highly relevant information by considering the user's geographical location during the capture process. For example, if the user is in a specific location, the capture unit prioritizes capturing information related to that location. For example, if the user is traveling, the capture unit can also prioritize capturing information related to tourist destinations. For example, if the user is participating in a specific event, the capture unit can also prioritize capturing information related to that event. In this way, the capture unit can prioritize capturing highly relevant information by considering the user's geographical location.

[0043] The capture unit analyzes the user's social media activity during capture and captures relevant information. For example, the capture unit captures relevant information based on information the user has shared on social media. The capture unit can also prioritize capturing information from accounts the user follows on social media. For example, the capture unit can capture information related to topics the user has shown interest in on social media. In this way, the capture unit can capture relevant information by analyzing the user's social media activity.

[0044] The analysis unit improves the accuracy of its analysis by considering the context of the captured information during analysis. For example, the analysis unit improves the accuracy of its analysis by considering the context before and after the captured text. The analysis unit can also improve the accuracy of its analysis by considering the context before and after the captured sign language movements. The analysis unit can also improve the accuracy of its analysis by considering the context before and after the captured audio. In this way, the analysis unit can improve the accuracy of its analysis by considering the context of the captured information.

[0045] The analysis unit selects the optimal analysis method by referring to the user's past analysis history during the analysis process. For example, the analysis unit selects the optimal analysis method based on the analysis methods the user has used in the past. The analysis unit can also select the most effective analysis method from the user's past analysis history, for example. The analysis unit can also select the optimal analysis method by analyzing the user's past analysis history, for example. In this way, the analysis unit can select the optimal analysis method by referring to the user's past analysis history.

[0046] The analysis unit applies different analysis algorithms depending on the category of the captured information during analysis. For example, the analysis unit applies a text analysis algorithm to text information. The analysis unit can also apply a motion analysis algorithm to sign language movements, for example. The analysis unit can also apply a speech analysis algorithm to speech information, for example. In this way, the analysis unit can improve the accuracy of the analysis by applying different analysis algorithms depending on the category of information.

[0047] The analysis unit improves the accuracy of its analysis by referring to relevant external databases during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to an external dictionary database. The analysis unit can also improve the accuracy of its analysis by referring to an external sign language database. The analysis unit can also improve the accuracy of its analysis by referring to an external speech database. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant external databases.

[0048] The translation unit adjusts the level of detail in the translation based on the importance of the captured information. For example, it provides detailed translations for important information. It can also provide concise translations for general information. For example, it can provide detailed translations for information that the user has shown particular interest in. This allows the translation unit to achieve more appropriate translations by adjusting the level of detail based on the importance of the information.

[0049] The translation unit applies different translation algorithms depending on the category of information during translation. For example, it applies a text translation algorithm to text information. It can also apply a motion translation algorithm to sign language movements, for example. It can also apply a speech translation algorithm to speech information, for example. By applying different translation algorithms depending on the category of information, the translation unit can improve the accuracy of the translation.

[0050] The translation team prioritizes translations based on when the information was submitted. For example, the team will prioritize translating the most recent information. The team can also prioritize information from a time period specified by the user. The team can also prioritize important historical information. This allows the team to prioritize translating more important information by prioritizing translations based on when the information was submitted.

[0051] The translation unit adjusts the order of translations based on the relevance of the information. For example, the translation unit prioritizes translating information that the user has shown interest in. The translation unit can also prioritize translating information that is highly relevant based on the user's past behavior history. The translation unit can also prioritize translating information that is highly relevant based on the user's current situation. In this way, the translation unit can prioritize translating more relevant information by adjusting the order of translations based on the relevance of the information.

[0052] The display unit analyzes the ambient light surrounding the screen and adjusts the display brightness to the optimal level. For example, in a bright environment, the display unit increases the display brightness to improve visibility. In a dark environment, for example, the display unit can also decrease the display brightness to reduce eye strain. The display unit can also adjust the display brightness in real time in response to changes in ambient light. This allows the display unit to adjust the display brightness to the optimal level by analyzing the ambient light surrounding the screen.

[0053] The display unit prioritizes displaying highly relevant information, taking into account the user's geographical location. For example, if the user is in a specific location, the display unit will prioritize displaying information related to that location. For example, if the user is traveling, the display unit can prioritize displaying information related to tourist destinations. For example, if the user is participating in a specific event, the display unit can prioritize displaying information related to that event. In this way, the display unit can prioritize displaying highly relevant information by taking into account the user's geographical location.

[0054] The display unit analyzes the user's social media activity and displays relevant information when it is displayed. For example, the display unit displays relevant information based on information the user has shared on social media. The display unit can also, for example, prioritize displaying information from accounts the user follows on social media. The display unit can also, for example, display information related to topics the user has shown interest in on social media. In this way, the display unit can display relevant information by analyzing the user's social media activity.

[0055] The audio output unit analyzes ambient sounds and removes noise before outputting audio. For example, the audio output unit can analyze ambient noise and remove it to output clear audio. For example, the audio output unit can analyze ambient sounds and prioritize the output of important audio information. For example, the audio output unit can analyze ambient sounds, remove noise, and convert sign language movements into speech. In this way, the audio output unit can analyze ambient sounds and remove noise to output clear audio.

[0056] The audio output unit selects the optimal audio output method by referring to the user's past audio output history when outputting audio. For example, the audio output unit selects the optimal audio output method based on the audio output method the user has used in the past. For example, the audio output unit can also select the most effective audio output method from the user's past audio output history. For example, the audio output unit can analyze the user's past audio output history and select the optimal audio output method. In this way, the audio output unit can select the optimal audio output method by referring to the user's past audio output history.

[0057] The audio output unit prioritizes outputting relevant audio by considering the user's geographical location. For example, if the user is in a specific location, the audio output unit will prioritize outputting audio related to that location. For example, if the user is traveling, the audio output unit can also prioritize outputting audio related to tourist destinations. For example, if the user is participating in a specific event, the audio output unit can also prioritize outputting audio related to that event. In this way, the audio output unit can prioritize outputting relevant audio by considering the user's geographical location.

[0058] The audio output unit analyzes the user's social media activity and outputs relevant audio when outputting audio. For example, the audio output unit outputs relevant audio based on information the user has shared on social media. The audio output unit can also prioritize outputting information from accounts the user follows on social media. For example, the audio output unit can output audio related to topics the user has shown interest in on social media. In this way, the audio output unit can output relevant audio by analyzing the user's social media activity.

[0059] The selection unit, when making a selection, refers to the user's past selection history to choose the most appropriate expression method. For example, the selection unit may select the most appropriate expression method based on the expression methods the user has used in the past. The selection unit can also, for example, select the most effective expression method from the user's past selection history. The selection unit can also, for example, analyze the user's past selection history to select the most appropriate expression method. In this way, the selection unit can select the most appropriate expression method by referring to the user's past selection history.

[0060] The selection function prioritizes selecting highly relevant information by considering the user's geographical location. For example, if the user is in a specific location, it will prioritize information related to that location. If the user is traveling, it can also prioritize information related to tourist destinations. If the user is attending a specific event, it can also prioritize information related to that event. In this way, the selection function can prioritize selecting highly relevant information by considering the user's geographical location.

[0061] The multilingual support unit selects the optimal language support method by referring to the user's past language usage history when implementing multilingual support. For example, the multilingual support unit selects the optimal language support method based on the languages ​​the user has used in the past. For example, the multilingual support unit can also select the most effective language support method from the user's past language usage history. For example, the multilingual support unit can analyze the user's past language usage history and select the optimal language support method. In this way, the multilingual support unit can select the optimal language support method by referring to the user's past language usage history.

[0062] The multilingual support unit prioritizes the most relevant languages ​​when providing multilingual support, taking into account the user's geographical location. For example, if the user is in a specific location, the multilingual support unit will prioritize the language associated with that location. If the user is traveling, for example, the multilingual support unit can also prioritize the language associated with the tourist destination. If the user is attending a specific event, for example, the multilingual support unit can also prioritize the language associated with that event. In this way, the multilingual support unit can prioritize the most relevant languages ​​by taking into account the user's geographical location.

[0063] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. The learning unit can also select the most effective learning algorithm from past learning data. For example, the learning unit can analyze past learning data and select the optimal learning algorithm. In this way, the learning unit can optimize the learning algorithm by referring to past learning data.

[0064] The learning unit weights the training data based on when the captured information was submitted. For example, the learning unit prioritizes weighting the most recent information as training data. The learning unit can also prioritize weighting the information from a time specified by the user. For example, the learning unit can prioritize weighting the important information from the past as training data. This allows the learning unit to prioritize learning more important information by weighting the training data based on when the information was submitted.

[0065] The bone conduction speaker unit analyzes ambient sounds and removes noise before outputting sound. For example, the bone conduction speaker unit can analyze ambient noise and remove it to output clear sound. The bone conduction speaker unit can also analyze ambient sounds and prioritize the output of important audio information. For example, the bone conduction speaker unit can analyze ambient sounds, remove noise, and convert sign language movements into speech. As a result, the bone conduction speaker unit can analyze ambient sounds and remove noise to output clear sound.

[0066] The bone conduction speaker unit prioritizes outputting relevant audio by considering the user's geographical location when outputting through the bone conduction speaker. For example, if the user is in a specific location, the bone conduction speaker unit will prioritize outputting audio related to that location. For example, if the user is traveling, the bone conduction speaker unit can also prioritize outputting audio related to tourist destinations. For example, if the user is participating in a specific event, the bone conduction speaker unit can also prioritize outputting audio related to that event. In this way, the bone conduction speaker unit can prioritize outputting relevant audio by considering the user's geographical location.

[0067] The voice selection unit, when selecting a voice, selects the optimal voice tone by referring to the user's past voice selection history. For example, the voice selection unit can select the optimal voice tone based on the voice tone the user has used in the past. For example, the voice selection unit can also select the most effective voice tone from the user's past voice selection history. For example, the voice selection unit can analyze the user's past voice selection history and select the optimal voice tone. In this way, the voice selection unit can select the optimal voice tone by referring to the user's past voice selection history.

[0068] The voice selection unit prioritizes selecting voices that are highly relevant to the user's geographical location, taking this information into account during voice selection. For example, if the user is in a specific location, the voice selection unit will prioritize selecting voices related to that location. For example, if the user is traveling, the voice selection unit can also prioritize selecting voices related to tourist destinations. For example, if the user is participating in a specific event, the voice selection unit can also prioritize selecting voices related to that event. In this way, the voice selection unit can prioritize selecting voices that are highly relevant by taking the user's geographical location into account.

[0069] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0070] The capture unit tracks the user's gaze during capture, allowing it to prioritize capturing information in the user's line of sight. For example, if the user is looking at specific text, that text can be prioritized for capture. Similarly, if the user is looking at sign language movements, those movements can be prioritized for capture. Furthermore, if the user shifts their gaze, new information in their line of sight can be captured. In this way, the capture unit can prioritize capturing information in the user's line of sight by tracking their gaze.

[0071] The capture unit can analyze ambient sounds during capture, remove noise, and capture information. For example, it can analyze ambient noise, remove noise, and capture clear audio information. It can also analyze ambient sounds and prioritize the capture of important audio information. Furthermore, it can analyze ambient sounds, remove noise, and capture sign language movements. In this way, the capture unit can capture clear information by analyzing ambient sounds and removing noise.

[0072] The analysis unit can improve the accuracy of its analysis by considering the context of the captured information during analysis. For example, it can improve the accuracy of its analysis by considering the context before and after the captured text. It can also improve the accuracy of its analysis by considering the context before and after the captured sign language movements. Furthermore, it can improve the accuracy of its analysis by considering the context before and after the captured audio. In this way, the analysis unit can improve the accuracy of its analysis by considering the context of the captured information.

[0073] The translation unit can adjust the level of detail in the translation based on the importance of the captured information. For example, important information can be translated in detail, while general information can be translated concisely. Furthermore, information that the user has expressed particular interest in can also be translated in detail. This allows the translation unit to achieve more appropriate translations by adjusting the level of detail based on the importance of the information.

[0074] The display unit can analyze the ambient light surrounding the screen and adjust the display brightness to the optimal level. For example, in a bright environment, the display brightness can be increased to improve visibility. Conversely, in a dark environment, the display brightness can be decreased to reduce eye strain. Furthermore, the display brightness can be adjusted in real time in response to changes in ambient light. In this way, the display unit can adjust the display brightness to the optimal level by analyzing the ambient light surrounding the screen.

[0075] The following briefly describes the processing flow for example form 1.

[0076] Step 1: The capture unit captures information. For example, the capture unit can capture image information using a camera. The capture unit can also capture audio information using a microphone. Furthermore, the capture unit can capture sign language movements using sensors. Step 2: The analysis unit analyzes the information captured by the capture unit. The analysis unit performs character analysis and language analysis using generative AI. For example, the analysis unit uses generative AI to analyze foreign language text and translate it into Japanese. The analysis unit can also use generative AI to analyze sign language movements and convert them into text. Furthermore, the analysis unit can use generative AI to analyze audio information and convert it into text. Step 3: The translation unit translates the information analyzed by the analysis unit. The translation unit generates an appropriate translation using generative AI. For example, the translation unit uses generative AI to translate foreign language text into Japanese. The translation unit can also use generative AI to translate sign language movements into text. Furthermore, the translation unit can use generative AI to translate audio information. Step 4: The display unit displays the information translated by the translation unit. The display unit displays the information using a display. For example, the display unit translates foreign language text into Japanese and displays it on the display. The display unit can also translate sign language movements into text and display it on the display. Furthermore, the display unit can translate audio information into text and display it on the display. Step 5: The audio output unit outputs the audio translated by the translation unit. The audio output unit outputs the audio using a speaker. For example, the audio output unit translates foreign language audio into Japanese and outputs it through the speaker. The audio output unit can also translate sign language movements into audio and output it through the speaker. Furthermore, the audio output unit can translate text information into audio and output it through the speaker.

[0077] (Example of form 2) The translation glasses system according to an embodiment of the present invention is a product for people who cannot read or speak foreign languages ​​or sign language. This translation glasses system utilizes generative AI to perform appropriate translation simultaneously with text analysis and language analysis, enabling information to be conveyed in an expression that suits the user's needs, thereby facilitating smooth communication. First, the user visually receives information that includes foreign languages ​​or sign language. For example, this could include foreign language books, movies, or videos without sign language commentary. This information is captured by cameras and sensors built into the translation glasses. Next, the generative AI analyzes the captured information. The generative AI performs text analysis and language analysis to generate an appropriate translation. For example, it can translate foreign language text into Japanese or convert sign language movements into text. In this process, the generative AI selects an expression that suits the user's needs and conveys the information. The translated information is displayed on the display of the translation glasses. For example, foreign language text is translated into Japanese and displayed on the display. In addition, because it has a built-in bone conduction speaker, it is also possible to convey the translated audio to the user. The voice tone of the audio is selectable, and it is also possible to use voice actors as a paid item. This system enables people who cannot read or speak foreign languages ​​or sign language to understand information without stress and communicate smoothly. For example, even if you don't understand the local language while traveling, using the translation glasses makes it easier to understand local information. Also, when watching videos without sign language commentary, using the translation glasses allows you to understand the content of the sign language. Furthermore, the generating AI supports multiple languages, so it can translate smoothly regardless of what language the user speaks. This is expected to contribute to solving various issues such as promoting intercultural understanding, facilitating international cooperation, and improving the quality of education and medical care. In short, the translation glasses system enables people who cannot read or speak foreign languages ​​or sign language to understand information without stress and communicate smoothly.

[0078] The translation glasses system according to this embodiment comprises a capture unit, an analysis unit, a translation unit, a display unit, and an audio output unit. The capture unit captures information. For example, the capture unit captures image information using a camera. The capture unit can also capture audio information using a microphone. Furthermore, the capture unit can also capture sign language movements using sensors. For example, the capture unit captures text from a foreign language book using a camera. The capture unit can also capture foreign language audio using a microphone. Furthermore, the capture unit can capture sign language movements using sensors. The analysis unit analyzes the information captured by the capture unit. The analysis unit performs character analysis and language analysis using a generation AI. For example, the analysis unit analyzes foreign language text using a generation AI and translates it into Japanese. Furthermore, the analysis unit can analyze sign language movements using a generation AI and convert them into text. Furthermore, the analysis unit can analyze audio information using a generation AI and convert it into text. The translation unit translates the information analyzed by the analysis unit. The translation unit generates an appropriate translation using a generation AI. For example, the translation unit uses generative AI to translate foreign language text into Japanese. The translation unit can also use generative AI to translate sign language movements into text. Furthermore, the translation unit can also use generative AI to translate audio information. The display unit displays the information translated by the translation unit. The display unit displays information using a display. For example, the display unit translates foreign language text into Japanese and displays it on the display. The display unit can also translate sign language movements into text and display it on the display. Furthermore, the display unit can translate audio information into text and display it on the display. The audio output unit outputs the audio translated by the translation unit. The audio output unit outputs audio using a speaker. For example, the audio output unit translates foreign language audio into Japanese and outputs it through the speaker. Furthermore, the audio output unit can also translate sign language movements into audio and output it through the speaker. Furthermore, the audio output unit can also translate text information into audio and output it through the speaker.As a result, the translation glasses system according to this embodiment can achieve smooth communication by capturing, analyzing, translating, displaying, and outputting information.

[0079] The capture unit captures information. For example, the capture unit captures image information using a camera. Specifically, the high-resolution camera mounted on the capture unit can clearly capture text information from books, signs, menus, etc. The camera has autofocus and image stabilization functions, allowing it to acquire stable images even when the user is moving. The capture unit can also capture audio information using a microphone. The microphone has a noise-canceling function to reduce ambient noise, allowing it to capture clear audio. Furthermore, the capture unit can also capture sign language movements using sensors. The sensors track the user's hand movements with high precision, accurately capturing the subtle movements and gestures of sign language. For example, when the capture unit captures text from a foreign language book using a camera, it converts the text into digital data using OCR (optical character recognition) technology. Also, when the capture unit captures foreign language audio using a microphone, it can convert the audio into text using speech recognition technology. Furthermore, when the capture unit captures sign language movements using sensors, it can convert the sign language into text using motion recognition technology. This allows the capture unit to capture diverse information with high accuracy and provide the data necessary for the next analysis step.

[0080] The analysis unit analyzes the information captured by the capture unit. The analysis unit performs character analysis and language analysis using generative AI. Specifically, the analysis unit analyzes foreign language text using generative AI and translates it into Japanese. The generative AI can understand context and meaning by utilizing natural language processing technology and generate appropriate translations. The analysis unit can also analyze sign language movements using generative AI and convert them into text. For sign language movements, the generative AI analyzes the movement patterns based on data acquired from sensors and generates corresponding text. Furthermore, the analysis unit can also analyze audio information using generative AI and convert it into text. Audio information is analyzed using speech recognition technology, and the generative AI understands the context and generates accurate text. For example, when the analysis unit analyzes foreign language text, the generative AI considers differences in grammar and vocabulary and translates it into natural-sounding Japanese. Also, when analyzing sign language movements, the generative AI analyzes data such as hand movements, position, and speed and generates corresponding Japanese text. When analyzing audio information, the generative AI considers the intonation and accent of the speech and generates accurate text. This allows the analysis unit to quickly and accurately analyze the captured information and provide the data necessary for the next translation step.

[0081] The translation unit translates the information analyzed by the analysis unit. The translation unit generates appropriate translations using generative AI. Specifically, the translation unit uses generative AI to translate foreign language text into Japanese. The generative AI can understand context and meaning and translate into natural-sounding Japanese. The translation unit can also use generative AI to translate sign language movements into text. The generative AI translates sign language movements into appropriate Japanese based on the text generated by the analysis unit. Furthermore, the translation unit can also translate audio information using generative AI. The generative AI translates audio information into appropriate Japanese based on the text generated by the analysis unit. For example, when the translation unit translates foreign language text, the generative AI considers differences in grammar and vocabulary to produce natural-sounding Japanese. When translating sign language movements, the generative AI analyzes data such as hand movements, position, and speed to generate corresponding Japanese text. When translating audio information, the generative AI considers the intonation and accent of the speech to generate accurate Japanese text. This allows the translation unit to translate the analyzed information quickly and accurately, providing the data necessary for the next display step.

[0082] The display unit displays information translated by the translation unit. The display unit uses a display to show information. Specifically, the display unit translates foreign language text into Japanese and displays it on the display. The display is high-resolution and highly visible, allowing users to easily check the information. The display unit can also translate sign language movements into text and display it on the display. The sign language movements are displayed on the display based on the text generated by the translation unit. Furthermore, the display unit can also translate audio information into text and display it on the display. The audio information is displayed on the display based on the text generated by the translation unit. For example, when the display unit translates foreign language text into Japanese and displays it on the display, it adjusts the font size and color to improve visibility. Also, when translating sign language movements into text and displaying it on the display, it displays the text corresponding to the sign language movements in real time. When translating audio information into text and displaying it on the display, it displays text that accurately reflects the content of the audio. In this way, the display unit can visually provide translated information to the user and support smooth communication.

[0083] The audio output unit outputs the audio translated by the translation unit. The audio output unit outputs the audio using a speaker. Specifically, the audio output unit translates foreign language audio into Japanese and outputs it through the speaker. The speaker provides high-quality, clear audio that is easily understandable to the user. The audio output unit can also translate sign language movements into audio and output it through the speaker. The sign language movements are converted into audio based on the text generated by the translation unit and output through the speaker. Furthermore, the audio output unit can also translate text information into audio and output it through the speaker. The text information is converted into audio based on the text generated by the translation unit and output through the speaker. For example, when the audio output unit translates foreign language audio into Japanese and outputs it through the speaker, it uses speech synthesis technology to generate natural-sounding audio. Also, when translating sign language movements into audio and outputting it through the speaker, it generates audio corresponding to the sign language movements in real time. When translating text information into audio and outputting it through the speaker, it generates audio that accurately reflects the content of the text. In this way, the audio output unit can provide translated information to the user audibly and support smooth communication.

[0084] The selection unit chooses expressions that meet the user's needs. For example, the selection unit analyzes the user's behavior history and selects appropriate expressions. For example, the selection unit selects appropriate expressions based on expressions the user has used in the past. The selection unit can also select appropriate expressions based on the results of user surveys. Furthermore, the selection unit can learn the user's preferences and select appropriate expressions. For example, the selection unit analyzes the user's past selection history and selects appropriate expressions. Furthermore, the selection unit can select appropriate expressions based on the results of user surveys. Furthermore, the selection unit can learn the user's preferences and select appropriate expressions. As a result, the selection unit can achieve more effective information transmission by selecting expressions that meet the user's needs.

[0085] The multilingual support unit performs multilingual support. The multilingual support unit uses generative AI to perform multilingual support. For example, the multilingual support unit uses generative AI to support multiple languages. For example, the multilingual support unit uses generative AI to support languages ​​such as English, Japanese, and French. Furthermore, the multilingual support unit can also use generative AI to improve translation accuracy. For example, the multilingual support unit uses generative AI to translate specialized terminology. Additionally, the multilingual support unit can use generative AI to perform contextually appropriate translations. For example, the multilingual support unit uses generative AI to perform contextually appropriate translations. As a result, the multilingual support unit enables smooth communication between users who speak different languages ​​by performing multilingual support.

[0086] The learning unit learns the user's preferences and past conversation history. The learning unit learns the user's preferences and past conversation history using generative AI. For example, the learning unit learns the user's past choice history using generative AI. For example, the learning unit learns the user's past conversation history using generative AI. The learning unit can also learn the user's preferences using generative AI. For example, the learning unit learns the user's survey results using generative AI. The learning unit can also learn the user's behavior history using generative AI. For example, the learning unit learns the user's past choice history using generative AI. The learning unit can also learn the user's past conversation history using generative AI. As a result, the learning unit can provide more personalized information by learning the user's preferences and past conversation history.

[0087] The bone conduction speaker unit incorporates a bone conduction speaker. The bone conduction speaker unit transmits sound using the bone conduction mechanism. For example, the bone conduction speaker unit transmits sound directly to the user using the bone conduction mechanism. For example, the bone conduction speaker unit transmits sound through the user's skull using the bone conduction mechanism. Furthermore, the bone conduction speaker unit can also transmit sound directly to the user's inner ear using the bone conduction mechanism. For example, the bone conduction speaker unit transmits sound directly to the user's inner ear using the bone conduction mechanism. Furthermore, the bone conduction speaker unit can also transmit sound directly to the user's auditory nerve using the bone conduction mechanism. For example, the bone conduction speaker unit transmits sound directly to the user's auditory nerve using the bone conduction mechanism. Thus, by incorporating a bone conduction speaker, the bone conduction speaker unit can directly transmit audio information to the user.

[0088] The voice selection unit allows the user to select the voice tone. The voice selection unit selects the voice tone according to the user's preference. For example, the voice selection unit can select a male voice, a female voice, a robot voice, etc., according to the user's preference. For example, the voice selection unit can select a male voice according to the user's preference. The voice selection unit can also select a female voice according to the user's preference. Furthermore, the voice selection unit can also select a robot voice according to the user's preference. For example, the voice selection unit can select a robot voice according to the user's preference. Furthermore, the voice selection unit can also select a voice actor's voice according to the user's preference. For example, the voice selection unit can select a voice actor's voice according to the user's preference. In this way, by allowing the user to select the voice tone, the voice selection unit can achieve voice output that matches the user's preference.

[0089] The capture unit estimates the user's emotions and adjusts the timing of captures based on the estimated emotions. For example, if the user is excited, the capture unit increases the frequency of captures to obtain more detailed information. For example, if the user is relaxed, the capture unit can also decrease the frequency of captures to obtain only the minimum necessary information. For example, if the user is stressed, the capture unit can adjust the timing of captures to reduce the user's burden. In this way, the capture unit can obtain more appropriate information by adjusting the timing of captures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The capture unit tracks the user's gaze during capture and prioritizes capturing information in the user's line of sight. For example, if the user is looking at a specific text, the capture unit will prioritize capturing that text. The capture unit can also prioritize capturing sign language movements if the user is looking at them. For example, if the user moves their gaze, the capture unit can capture new information in the user's line of sight. In this way, the capture unit can prioritize capturing information in the user's line of sight by tracking their gaze.

[0091] The capture unit analyzes ambient sounds during capture, removes noise, and captures information. For example, the capture unit can analyze ambient noise, remove noise, and capture clear audio information. The capture unit can also analyze ambient sounds and prioritize the capture of important audio information. For example, the capture unit can analyze ambient sounds, remove noise, and capture sign language movements. In this way, the capture unit can capture clear information by analyzing ambient sounds and removing noise.

[0092] The capture unit estimates the user's emotions and determines the priority of information to capture based on the estimated emotions. For example, if the user is excited, the capture unit will prioritize capturing visually stimulating information. For example, if the user is relaxed, the capture unit may prioritize capturing calming information. For example, if the user is stressed, the capture unit may prioritize capturing stress-reducing information. In this way, the capture unit can prioritize capturing more important information by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The capture unit prioritizes capturing highly relevant information by considering the user's geographical location during the capture process. For example, if the user is in a specific location, the capture unit prioritizes capturing information related to that location. For example, if the user is traveling, the capture unit can also prioritize capturing information related to tourist destinations. For example, if the user is participating in a specific event, the capture unit can also prioritize capturing information related to that event. In this way, the capture unit can prioritize capturing highly relevant information by considering the user's geographical location.

[0094] The capture unit analyzes the user's social media activity during capture and captures relevant information. For example, the capture unit captures relevant information based on information the user has shared on social media. The capture unit can also prioritize capturing information from accounts the user follows on social media. For example, the capture unit can capture information related to topics the user has shown interest in on social media. In this way, the capture unit can capture relevant information by analyzing the user's social media activity.

[0095] The analysis unit estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated emotions. For example, if the user is excited, the analysis unit performs a more detailed analysis to improve accuracy. For example, if the user is relaxed, the analysis unit can perform only the minimum necessary analysis and adjust the accuracy. For example, if the user is stressed, the analysis unit can adjust the accuracy of the analysis to reduce the user's burden. In this way, the analysis unit can achieve more appropriate analysis by adjusting the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The analysis unit improves the accuracy of its analysis by considering the context of the captured information during analysis. For example, the analysis unit improves the accuracy of its analysis by considering the context before and after the captured text. The analysis unit can also improve the accuracy of its analysis by considering the context before and after the captured sign language movements. The analysis unit can also improve the accuracy of its analysis by considering the context before and after the captured audio. In this way, the analysis unit can improve the accuracy of its analysis by considering the context of the captured information.

[0097] The analysis unit selects the optimal analysis method by referring to the user's past analysis history during the analysis process. For example, the analysis unit selects the optimal analysis method based on the analysis methods the user has used in the past. The analysis unit can also select the most effective analysis method from the user's past analysis history, for example. The analysis unit can also select the optimal analysis method by analyzing the user's past analysis history, for example. In this way, the analysis unit can select the optimal analysis method by referring to the user's past analysis history.

[0098] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. In this way, the analysis unit can achieve a more appropriate display by adjusting the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The analysis unit applies different analysis algorithms depending on the category of the captured information during analysis. For example, the analysis unit applies a text analysis algorithm to text information. The analysis unit can also apply a motion analysis algorithm to sign language movements, for example. The analysis unit can also apply a speech analysis algorithm to speech information, for example. In this way, the analysis unit can improve the accuracy of the analysis by applying different analysis algorithms depending on the category of information.

[0100] The analysis unit improves the accuracy of its analysis by referring to relevant external databases during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to an external dictionary database. The analysis unit can also improve the accuracy of its analysis by referring to an external sign language database. The analysis unit can also improve the accuracy of its analysis by referring to an external speech database. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant external databases.

[0101] The translation unit estimates the user's emotions and adjusts the translation's expression based on the estimated emotions. For example, if the user is relaxed, the translation unit will use softer language. If the user is in a hurry, the translation unit may use concise and direct language. If the user is excited, the translation unit may use emotionally emphatic language. In this way, the translation unit can achieve more appropriate translations by adjusting the expression of the translation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The translation unit adjusts the level of detail in the translation based on the importance of the captured information. For example, it provides detailed translations for important information. It can also provide concise translations for general information. For example, it can provide detailed translations for information that the user has shown particular interest in. This allows the translation unit to achieve more appropriate translations by adjusting the level of detail based on the importance of the information.

[0103] The translation unit applies different translation algorithms depending on the category of information during translation. For example, it applies a text translation algorithm to text information. It can also apply a motion translation algorithm to sign language movements, for example. It can also apply a speech translation algorithm to speech information, for example. By applying different translation algorithms depending on the category of information, the translation unit can improve the accuracy of the translation.

[0104] The translation unit estimates the user's emotions and adjusts the translation length based on the estimated emotions. For example, if the user is in a hurry, the translation unit will produce a short, concise translation. If the user is relaxed, the translation unit may produce a longer translation that includes detailed explanations. If the user is excited, the translation unit may produce a longer translation that emphasizes emotions. In this way, the translation unit can achieve a more appropriate translation by adjusting the translation length according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The translation team prioritizes translations based on when the information was submitted. For example, the team will prioritize translating the most recent information. The team can also prioritize information from a time period specified by the user. The team can also prioritize important historical information. This allows the team to prioritize translating more important information by prioritizing translations based on when the information was submitted.

[0106] The translation unit adjusts the order of translations based on the relevance of the information. For example, the translation unit prioritizes translating information that the user has shown interest in. The translation unit can also prioritize translating information that is highly relevant based on the user's past behavior history. The translation unit can also prioritize translating information that is highly relevant based on the user's current situation. In this way, the translation unit can prioritize translating more relevant information by adjusting the order of translations based on the relevance of the information.

[0107] The display unit estimates the user's emotions and adjusts the display method based on the estimated emotions. For example, if the user is tense, the display unit may provide a display method with calm colors. For example, if the user is enjoying themselves, the display unit may provide a display method with bright colors. For example, if the user is tired, the display unit may provide a simple and highly visible display method. In this way, the display unit can achieve a more appropriate display by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The display unit analyzes the ambient light surrounding the screen and adjusts the display brightness to the optimal level. For example, in a bright environment, the display unit increases the display brightness to improve visibility. In a dark environment, for example, the display unit can also decrease the display brightness to reduce eye strain. The display unit can also adjust the display brightness in real time in response to changes in ambient light. This allows the display unit to adjust the display brightness to the optimal level by analyzing the ambient light surrounding the screen.

[0109] The display unit estimates the user's emotions and determines the priority of information to display based on the estimated emotions. For example, if the user is excited, the display unit will prioritize displaying visually stimulating information. For example, if the user is relaxed, the display unit may prioritize displaying calming information. For example, if the user is stressed, the display unit may prioritize displaying stress-reducing information. In this way, the display unit can prioritize displaying more important information by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The display unit prioritizes displaying highly relevant information, taking into account the user's geographical location. For example, if the user is in a specific location, the display unit will prioritize displaying information related to that location. For example, if the user is traveling, the display unit can prioritize displaying information related to tourist destinations. For example, if the user is participating in a specific event, the display unit can prioritize displaying information related to that event. In this way, the display unit can prioritize displaying highly relevant information by taking into account the user's geographical location.

[0111] The display unit analyzes the user's social media activity and displays relevant information when it is displayed. For example, the display unit displays relevant information based on information the user has shared on social media. The display unit can also, for example, prioritize displaying information from accounts the user follows on social media. The display unit can also, for example, display information related to topics the user has shown interest in on social media. In this way, the display unit can display relevant information by analyzing the user's social media activity.

[0112] The voice output unit estimates the user's emotions and adjusts the tone of the voice output based on the estimated emotions. For example, if the user is relaxed, the voice output unit will output in a soft tone. If the user is in a hurry, the voice output unit can also output in a quick and concise tone. If the user is excited, the voice output unit can also output in an emotionally emphasized tone. In this way, the voice output unit can achieve more appropriate voice output by adjusting the tone of the voice output according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The audio output unit analyzes ambient sounds and removes noise before outputting audio. For example, the audio output unit can analyze ambient noise and remove it to output clear audio. For example, the audio output unit can analyze ambient sounds and prioritize the output of important audio information. For example, the audio output unit can analyze ambient sounds, remove noise, and convert sign language movements into speech. In this way, the audio output unit can analyze ambient sounds and remove noise to output clear audio.

[0114] The audio output unit selects the optimal audio output method by referring to the user's past audio output history when outputting audio. For example, the audio output unit selects the optimal audio output method based on the audio output method the user has used in the past. For example, the audio output unit can also select the most effective audio output method from the user's past audio output history. For example, the audio output unit can analyze the user's past audio output history and select the optimal audio output method. In this way, the audio output unit can select the optimal audio output method by referring to the user's past audio output history.

[0115] The audio output unit estimates the user's emotions and determines the priority of audio output based on the estimated emotions. For example, if the user is excited, the audio output unit will prioritize outputting emotionally emphasizing sounds. For example, if the user is relaxed, the audio output unit may also prioritize outputting softer sounds. For example, if the user is stressed, the audio output unit may also prioritize outputting stress-reducing sounds. In this way, the audio output unit can prioritize outputting more important sounds by determining the priority of audio output according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The audio output unit prioritizes outputting relevant audio by considering the user's geographical location. For example, if the user is in a specific location, the audio output unit will prioritize outputting audio related to that location. For example, if the user is traveling, the audio output unit can also prioritize outputting audio related to tourist destinations. For example, if the user is participating in a specific event, the audio output unit can also prioritize outputting audio related to that event. In this way, the audio output unit can prioritize outputting relevant audio by considering the user's geographical location.

[0117] The audio output unit analyzes the user's social media activity and outputs relevant audio when outputting audio. For example, the audio output unit outputs relevant audio based on information the user has shared on social media. The audio output unit can also prioritize outputting information from accounts the user follows on social media. For example, the audio output unit can output audio related to topics the user has shown interest in on social media. In this way, the audio output unit can output relevant audio by analyzing the user's social media activity.

[0118] The selection unit estimates the user's emotions and adjusts the expression used for selection based on the estimated emotions. For example, if the user is relaxed, the selection unit provides a selection method using softer expressions. If the user is in a hurry, the selection unit may also provide a selection method using quick and concise expressions. If the user is excited, the selection unit may also provide a selection method using emotionally emphasized expressions. In this way, the selection unit can achieve more appropriate expressions by adjusting the expression method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0119] The selection unit, when making a selection, refers to the user's past selection history to choose the most appropriate expression method. For example, the selection unit may select the most appropriate expression method based on the expression methods the user has used in the past. The selection unit can also, for example, select the most effective expression method from the user's past selection history. The selection unit can also, for example, analyze the user's past selection history to select the most appropriate expression method. In this way, the selection unit can select the most appropriate expression method by referring to the user's past selection history.

[0120] The selection unit estimates the user's emotions and determines the priority of information to select based on the estimated emotions. For example, if the user is excited, the selection unit will prioritize visually stimulating information. If the user is relaxed, the selection unit may also prioritize calming information. If the user is stressed, the selection unit may also prioritize stress-reducing information. In this way, the selection unit can prioritize more important information by determining the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0121] The selection function prioritizes selecting highly relevant information by considering the user's geographical location. For example, if the user is in a specific location, it will prioritize information related to that location. If the user is traveling, it can also prioritize information related to tourist destinations. If the user is attending a specific event, it can also prioritize information related to that event. In this way, the selection function can prioritize selecting highly relevant information by considering the user's geographical location.

[0122] The multilingual support unit estimates the user's emotions and adjusts its multilingual support method based on the estimated emotions. For example, if the user is relaxed, the multilingual support unit will use softer language. If the user is in a hurry, the multilingual support unit can also use quick and concise language. If the user is excited, the multilingual support unit can also use emotionally emphasizing language. In this way, the multilingual support unit can achieve more appropriate multilingual support by adjusting its method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0123] The multilingual support unit selects the optimal language support method by referring to the user's past language usage history when implementing multilingual support. For example, the multilingual support unit selects the optimal language support method based on the languages ​​the user has used in the past. For example, the multilingual support unit can also select the most effective language support method from the user's past language usage history. For example, the multilingual support unit can analyze the user's past language usage history and select the optimal language support method. In this way, the multilingual support unit can select the optimal language support method by referring to the user's past language usage history.

[0124] The multilingual support unit estimates the user's emotions and determines the priority of multilingual support based on the estimated emotions. For example, if the user is excited, the multilingual support unit will prioritize languages ​​that emphasize those emotions. For example, if the user is relaxed, the multilingual support unit may also prioritize softer languages. For example, if the user is stressed, the multilingual support unit may also prioritize languages ​​that reduce stress. In this way, the multilingual support unit can prioritize more important languages ​​by determining the priority of multilingual support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0125] The multilingual support unit prioritizes the most relevant languages ​​when providing multilingual support, taking into account the user's geographical location. For example, if the user is in a specific location, the multilingual support unit will prioritize the language associated with that location. If the user is traveling, for example, the multilingual support unit can also prioritize the language associated with the tourist destination. If the user is attending a specific event, for example, the multilingual support unit can also prioritize the language associated with that event. In this way, the multilingual support unit can prioritize the most relevant languages ​​by taking into account the user's geographical location.

[0126] The learning unit estimates the user's emotions and selects training data based on the estimated emotions. For example, if the user is relaxed, the learning unit will select training data using soft expressions. If the user is in a hurry, the learning unit may also select training data using quick and concise expressions. If the user is excited, the learning unit may also select training data using emotionally emphasized expressions. This allows the learning unit to achieve more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0127] The learning unit optimizes the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. The learning unit can also select the most effective learning algorithm from past learning data. For example, the learning unit can analyze past learning data and select the optimal learning algorithm. In this way, the learning unit can optimize the learning algorithm by referring to past learning data.

[0128] The learning unit estimates the user's emotions and adjusts the learning frequency based on the estimated emotions. For example, if the user is relaxed, the learning unit can reduce the learning frequency to lessen the burden. If the user is in a hurry, for example, the learning unit can increase the learning frequency to accelerate learning. If the user is excited, for example, the learning unit can adjust the learning frequency to advance learning effectively. In this way, the learning unit can achieve more appropriate learning by adjusting the learning frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0129] The learning unit weights the training data based on when the captured information was submitted. For example, the learning unit prioritizes weighting the most recent information as training data. The learning unit can also prioritize weighting the information from a time specified by the user. For example, the learning unit can prioritize weighting the important information from the past as training data. This allows the learning unit to prioritize learning more important information by weighting the training data based on when the information was submitted.

[0130] The bone conduction speaker unit estimates the user's emotions and adjusts the output method of the bone conduction speaker based on the estimated user's emotions. For example, if the user is relaxed, the bone conduction speaker unit will output sound in a soft tone. For example, if the user is in a hurry, the bone conduction speaker unit can also output sound in a quick and concise tone. For example, if the user is excited, the bone conduction speaker unit can also output sound in an emotionally emphasized tone. In this way, the bone conduction speaker unit can achieve more appropriate sound output by adjusting the output method of the bone conduction speaker according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0131] The bone conduction speaker unit analyzes ambient sounds and removes noise before outputting sound. For example, the bone conduction speaker unit can analyze ambient noise and remove it to output clear sound. The bone conduction speaker unit can also analyze ambient sounds and prioritize the output of important audio information. For example, the bone conduction speaker unit can analyze ambient sounds, remove noise, and convert sign language movements into speech. As a result, the bone conduction speaker unit can analyze ambient sounds and remove noise to output clear sound.

[0132] The bone conduction speaker unit estimates the user's emotions and determines the output priority of the bone conduction speaker based on the estimated user emotions. For example, if the user is excited, the bone conduction speaker unit will prioritize outputting emotionally emphasizing sounds. For example, if the user is relaxed, the bone conduction speaker unit can also prioritize outputting soft sounds. For example, if the user is stressed, the bone conduction speaker unit can also prioritize outputting stress-reducing sounds. In this way, the bone conduction speaker unit can prioritize outputting more important sounds by determining the output priority of the bone conduction speaker according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0133] The bone conduction speaker unit prioritizes outputting relevant audio by considering the user's geographical location when outputting through the bone conduction speaker. For example, if the user is in a specific location, the bone conduction speaker unit will prioritize outputting audio related to that location. For example, if the user is traveling, the bone conduction speaker unit can also prioritize outputting audio related to tourist destinations. For example, if the user is participating in a specific event, the bone conduction speaker unit can also prioritize outputting audio related to that event. In this way, the bone conduction speaker unit can prioritize outputting relevant audio by considering the user's geographical location.

[0134] The voice selection unit estimates the user's emotions and adjusts the tone of voice based on the estimated emotions. For example, if the user is relaxed, the voice selection unit will output a soft tone of voice. For example, if the user is in a hurry, the voice selection unit can also output a quick and concise tone of voice. For example, if the user is excited, the voice selection unit can also output a tone of voice that emphasizes emotion. In this way, the voice selection unit can achieve more appropriate voice output by adjusting the tone of voice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0135] The voice selection unit, when selecting a voice, selects the optimal voice tone by referring to the user's past voice selection history. For example, the voice selection unit can select the optimal voice tone based on the voice tone the user has used in the past. For example, the voice selection unit can also select the most effective voice tone from the user's past voice selection history. For example, the voice selection unit can analyze the user's past voice selection history and select the optimal voice tone. In this way, the voice selection unit can select the optimal voice tone by referring to the user's past voice selection history.

[0136] The voice selection unit estimates the user's emotions and determines the priority of voices based on the estimated emotions. For example, if the user is excited, the voice selection unit will prioritize selecting voices that emphasize those emotions. For example, if the user is relaxed, the voice selection unit may also prioritize selecting softer voices. For example, if the user is stressed, the voice selection unit may also prioritize selecting stress-reducing voices. In this way, the voice selection unit can prioritize selecting more important voices by determining the priority of voices according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0137] The voice selection unit prioritizes selecting voices that are highly relevant to the user's geographical location, taking this information into account during voice selection. For example, if the user is in a specific location, the voice selection unit will prioritize selecting voices related to that location. For example, if the user is traveling, the voice selection unit can also prioritize selecting voices related to tourist destinations. For example, if the user is participating in a specific event, the voice selection unit can also prioritize selecting voices related to that event. In this way, the voice selection unit can prioritize selecting voices that are highly relevant by taking the user's geographical location into account.

[0138] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0139] The capture unit can estimate the user's emotions and adjust the timing of captures based on those emotions. For example, if the user is excited, the capture frequency can be increased to obtain more detailed information. Conversely, if the user is relaxed, the capture frequency can be decreased to obtain only the minimum necessary information. Furthermore, if the user is stressed, the capture timing can be adjusted to reduce the user's burden. In this way, the capture unit can obtain more appropriate information by adjusting the capture timing according to the user's emotions.

[0140] The capture unit tracks the user's gaze during capture, allowing it to prioritize capturing information in the user's line of sight. For example, if the user is looking at specific text, that text can be prioritized for capture. Similarly, if the user is looking at sign language movements, those movements can be prioritized for capture. Furthermore, if the user shifts their gaze, new information in their line of sight can be captured. In this way, the capture unit can prioritize capturing information in the user's line of sight by tracking their gaze.

[0141] The capture unit can analyze ambient sounds during capture, remove noise, and capture information. For example, it can analyze ambient noise, remove noise, and capture clear audio information. It can also analyze ambient sounds and prioritize the capture of important audio information. Furthermore, it can analyze ambient sounds, remove noise, and capture sign language movements. In this way, the capture unit can capture clear information by analyzing ambient sounds and removing noise.

[0142] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is excited, it can perform a more detailed analysis to improve accuracy. If the user is relaxed, it can perform only the minimum necessary analysis and adjust the accuracy. Furthermore, if the user is stressed, it can adjust the accuracy of the analysis to reduce the user's burden. In this way, the analysis unit can achieve more appropriate analysis by adjusting the accuracy of the analysis according to the user's emotions.

[0143] The analysis unit can improve the accuracy of its analysis by considering the context of the captured information during analysis. For example, it can improve the accuracy of its analysis by considering the context before and after the captured text. It can also improve the accuracy of its analysis by considering the context before and after the captured sign language movements. Furthermore, it can improve the accuracy of its analysis by considering the context before and after the captured audio. In this way, the analysis unit can improve the accuracy of its analysis by considering the context of the captured information.

[0144] The translation unit can estimate the user's emotions and adjust the translation's expression based on that estimation. For example, if the user is relaxed, it can use softer language in the translation. If the user is in a hurry, it can use concise and direct language. Furthermore, if the user is excited, it can use emotionally emphasizing language in the translation. In this way, the translation unit can achieve more appropriate translations by adjusting the expression of the translation according to the user's emotions.

[0145] The translation unit can adjust the level of detail in the translation based on the importance of the captured information. For example, important information can be translated in detail, while general information can be translated concisely. Furthermore, information that the user has expressed particular interest in can also be translated in detail. This allows the translation unit to achieve more appropriate translations by adjusting the level of detail based on the importance of the information.

[0146] The display unit can estimate the user's emotions and adjust the display method based on those emotions. For example, if the user is tense, it can provide a display method with calming colors. If the user is enjoying themselves, it can provide a display method with bright colors. Furthermore, if the user is tired, it can provide a simple and highly visible display method. In this way, the display unit can achieve a more appropriate display by adjusting the display method according to the user's emotions.

[0147] The display unit can analyze the ambient light surrounding the screen and adjust the display brightness to the optimal level. For example, in a bright environment, the display brightness can be increased to improve visibility. Conversely, in a dark environment, the display brightness can be decreased to reduce eye strain. Furthermore, the display brightness can be adjusted in real time in response to changes in ambient light. In this way, the display unit can adjust the display brightness to the optimal level by analyzing the ambient light surrounding the screen.

[0148] The voice output unit can estimate the user's emotions and adjust the tone of the voice output based on those emotions. For example, if the user is relaxed, the voice can be output in a soft tone. If the user is in a hurry, the voice can be output in a quick and concise tone. Furthermore, if the user is excited, the voice can be output in an emotionally emphasized tone. In this way, the voice output unit can achieve more appropriate voice output by adjusting the tone of the voice output according to the user's emotions.

[0149] The following briefly describes the processing flow for example form 2.

[0150] Step 1: The capture unit captures information. For example, the capture unit can capture image information using a camera. The capture unit can also capture audio information using a microphone. Furthermore, the capture unit can capture sign language movements using sensors. Step 2: The analysis unit analyzes the information captured by the capture unit. The analysis unit performs character analysis and language analysis using generative AI. For example, the analysis unit uses generative AI to analyze foreign language text and translate it into Japanese. The analysis unit can also use generative AI to analyze sign language movements and convert them into text. Furthermore, the analysis unit can use generative AI to analyze audio information and convert it into text. Step 3: The translation unit translates the information analyzed by the analysis unit. The translation unit generates an appropriate translation using generative AI. For example, the translation unit uses generative AI to translate foreign language text into Japanese. The translation unit can also use generative AI to translate sign language movements into text. Furthermore, the translation unit can use generative AI to translate audio information. Step 4: The display unit displays the information translated by the translation unit. The display unit displays the information using a display. For example, the display unit translates foreign language text into Japanese and displays it on the display. The display unit can also translate sign language movements into text and display it on the display. Furthermore, the display unit can translate audio information into text and display it on the display. Step 5: The audio output unit outputs the audio translated by the translation unit. The audio output unit outputs the audio using a speaker. For example, the audio output unit translates foreign language audio into Japanese and outputs it through the speaker. The audio output unit can also translate sign language movements into audio and output it through the speaker. Furthermore, the audio output unit can translate text information into audio and output it through the speaker.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the capture unit, analysis unit, translation unit, display unit, and audio output unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the capture unit captures information using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs character analysis and language analysis using generation AI. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an appropriate translation using generation AI. The display unit displays the translated information on the display 40A of the smart device 14. The audio output unit outputs the translated audio using the speaker 40B of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0155] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] Each of the multiple elements described above, including the capture unit, analysis unit, translation unit, display unit, and audio output unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the capture unit captures information using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs character analysis and language analysis using generation AI. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an appropriate translation using generation AI. The display unit displays the translated information on the display of the smart glasses 214. The audio output unit outputs the translated audio using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0171] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] Each of the multiple elements described above, including the capture unit, analysis unit, translation unit, display unit, and audio output unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the capture unit captures information using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs character analysis and language analysis using generation AI. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an appropriate translation using generation AI. The display unit displays the translated information on the display 343 of the headset terminal 314. The audio output unit outputs the translated audio using the speaker 240 of the headset terminal 314. 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.

[0187] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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).

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.).

[0200] 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.

[0201] 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.

[0202] 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.

[0203] Each of the multiple elements described above, including the capture unit, analysis unit, translation unit, display unit, and audio output unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the capture unit captures information using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs character analysis and language analysis using a generation AI. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates an appropriate translation using a generation AI. The display unit displays the translated information on the display of the robot 414. The audio output unit outputs the translated audio using the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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."

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] (Note 1) A capture unit that captures information, An analysis unit that analyzes the information captured by the aforementioned capture unit, A translation unit that translates the information analyzed by the aforementioned analysis unit, A display unit that displays the information translated by the aforementioned translation unit, The system includes an audio output unit that outputs the audio translated by the translation unit. A system characterized by the following features. (Note 2) It includes a selection section that allows users to choose the expression that best suits their needs. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a multilingual support unit that handles multiple languages. The system described in Appendix 1, characterized by the features described herein. (Note 4) It features a learning unit that learns user preferences and past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 5) It features a bone conduction speaker unit with a built-in bone conduction speaker. The system described in Appendix 1, characterized by the features described herein. (Note 6) It features a voice selection unit that allows users to choose the tone of voice. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned capture unit is It estimates the user's emotions and adjusts the timing of captures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned capture unit is During capture, the system tracks the user's gaze and prioritizes capturing information in the direction of their gaze. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned capture unit is During capture, the system analyzes ambient sounds, removes noise, and captures the information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned capture unit is It estimates the user's emotions and determines the priority of information to capture based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned capture unit is During capture, the system prioritizes capturing highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned capture unit is During capture, the system analyzes the user's social media activity and captures relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the context of the captured information is taken into consideration to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the system selects the optimal analysis method by referring to the user's past analysis history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the captured information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant external databases to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) The aforementioned translation department, During translation, adjust the level of detail based on the importance of the captured information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned translation department, During translation, different translation algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned translation department, It estimates the user's sentiment and adjusts the translation length based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned translation department, During translation, translation priorities are determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned translation department, During translation, the order of translations is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is During display, the system analyzes the ambient light and adjusts the display brightness to the optimal level. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is It estimates the user's emotions and determines the priority of the information to display based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is When displaying information, the system prioritizes showing more relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is When displaying information, the system analyzes the user's social media activity and displays relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned audio output unit is It estimates the user's emotions and adjusts the tone of the voice output based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned audio output unit is When outputting audio, the system analyzes ambient noise, removes noise, and then outputs the audio. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned audio output unit is When outputting audio, the system selects the optimal audio output method by referring to the user's past audio output history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned audio output unit is It estimates the user's emotions and determines the priority of voice output based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned audio output unit is When outputting audio, the system prioritizes outputting audio that is highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned audio output unit is When outputting audio, the system analyzes the user's social media activity and outputs relevant audio. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned selection unit is It estimates the user's emotions and adjusts the expression methods selected based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned selection unit is When a selection is made, the system refers to the user's past selection history to select the most appropriate method of expression. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned selection unit is It estimates the user's emotions and determines the priority of information to select based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned selection unit is When making a selection, the system prioritizes selecting the most relevant information, taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned multilingual support unit is It estimates the user's emotions and adjusts the multilingual support method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned multilingual support unit is When implementing multilingual support, the system selects the optimal language support method by referring to the user's past language usage history. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned multilingual support unit is Estimate the user's emotion and determine the priority order of multilingual support based on the estimated user emotion The system according to Appendix 3, characterized by the above. (Appendix 43) The multilingual support unit When providing multilingual support, preferentially support languages with high relevance considering the user's geographical location information The system according to Appendix 3, characterized by the above. (Appendix 44) The learning unit Estimate the user's emotion and select learning data based on the estimated user emotion The system according to Appendix 4, characterized by the above. (Appendix 45) The learning unit When learning, optimize the learning algorithm by referring to past learning data The system according to Appendix 4, characterized by the above. (Appendix 46) The learning unit Estimate the user's emotion and adjust the learning frequency based on the estimated user emotion The system according to Appendix 4, characterized by the above. (Appendix 47) The learning unit When learning, perform weighting of learning data based on the submission time of the captured information The system according to Appendix 4, characterized by the above. (Appendix 48) The bone conduction speaker unit Estimate the user's emotion and adjust the output method of the bone conduction speaker based on the estimated user emotion The system according to Appendix 5, characterized by the above. (Appendix 49) The bone conduction speaker unit When outputting sound from the bone conduction speaker, analyze the ambient environmental sound, remove noise, and output the sound The system according to Appendix 5, characterized by the above. (Appendix 50) The bone conduction speaker unit The system estimates the user's emotions and determines the output priority of the bone conduction speakers based on the estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 51) The bone conduction speaker unit is When using bone conduction speakers, the system prioritizes outputting audio that is highly relevant to the user's geographical location. The system described in Appendix 5, characterized by the features described herein. (Note 52) The aforementioned voice selection unit is It estimates the user's emotions and adjusts the tone of voice based on those emotions. The system described in Appendix 6, characterized by the features described herein. (Note 53) The aforementioned voice selection unit is When selecting a voice, the system refers to the user's past voice selection history to choose the most suitable voice tone. The system described in Appendix 6, characterized by the features described herein. (Note 54) The aforementioned voice selection unit is It estimates the user's emotions and determines the priority of voice messages based on the estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 55) The aforementioned voice selection unit is When selecting a voice, the system prioritizes selecting a voice that is highly relevant to the user's geographical location. The system described in Appendix 6, characterized by the features described herein. [Explanation of symbols]

[0223] 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 capture unit that captures information, An analysis unit that analyzes the information captured by the aforementioned capture unit, A translation unit that translates the information analyzed by the aforementioned analysis unit, A display unit that displays the information translated by the aforementioned translation unit, The system includes an audio output unit that outputs the audio translated by the translation unit. A system characterized by the following features.

2. It includes a selection section that allows users to choose the expression that best suits their needs. The system according to feature 1.

3. It is equipped with a multilingual support unit that handles multiple languages. The system according to feature 1.

4. It features a learning unit that learns user preferences and past conversation history. The system according to feature 1.

5. It features a bone conduction speaker unit with a built-in bone conduction speaker. The system according to feature 1.

6. It features a voice selection unit that allows users to choose the tone of voice. The system according to feature 1.

7. The aforementioned capture unit is It estimates the user's emotions and adjusts the timing of captures based on the estimated user emotions. The system according to feature 1.

8. The aforementioned capture unit is During capture, the system tracks the user's gaze and prioritizes capturing information in the direction of their gaze. The system according to feature 1.

9. The aforementioned capture unit is During capture, the system analyzes ambient sounds, removes noise, and captures the information. The system according to feature 1.

10. The aforementioned capture unit is It estimates the user's emotions and determines the priority of information to capture based on the estimated user emotions. The system according to feature 1.

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