System

Abstract

To provide a system that facilitates smooth communication between deaf-mute individuals and individuals without sign language proficiency.SOLUTION: A system according to an embodiment comprises a characterizing unit, a display unit, a speech conversion unit, a playback unit, and a control unit. The characterizing unit converts spoken input into text. The display unit displays the content converted into text by the characterizing unit. The speech conversion unit converts sign language into an audio signal. The playback unit plays back the audio converted by the speech conversion unit. The control unit controls the characterizing unit and the speech conversion unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that it is difficult for deaf-mute people and people who cannot sign to communicate smoothly.

[0005] The system according to the embodiment aims to enable smooth communication between deaf and mute people and people who cannot use sign language. [Means for solving the problem]

[0006] The system according to the embodiment includes a transcribing unit, a display unit, a voice conversion unit, a playback unit, and a control unit. The transcribing unit converts voice input into text. The display unit displays the content transcribed by the transcribing unit. The voice conversion unit converts sign language into a voice signal. The playback unit plays back the voice converted by the voice conversion unit. The control unit controls the transcribing unit and the voice conversion unit. Effect of the Invention

[0007] The system according to the embodiment can enable smooth communication between deaf and mute people and people who cannot use sign language. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The smart glasses system according to the embodiment of the present invention is a system that enables smooth communication between deaf-mute people and people who cannot sign using a generation AI. This system has a function of instantly converting the other person's voice into text and supporting multiple languages, and a function of instantly converting one's own sign language into audio. This enables smooth communication between deaf-mute people and people who cannot sign. For example, the microphone of the smart glasses picks up the other person's voice, and the generation AI analyzes the voice and converts it into text. The converted text is displayed on the display of the smart glasses. Next, when the deaf-mute person uses sign language, the camera of the smart glasses recognizes the sign language, and the generation AI analyzes the content, converts it into audio, and plays it from the speaker. In this way, smooth communication is possible between deaf-mute people and people who cannot sign. As a result, the smart glasses system can realize smooth communication between deaf-mute people and people who cannot sign.

[0029] The smart glasses system according to the embodiment includes a text conversion unit, a display unit, a voice conversion unit, a playback unit, and a control unit. The text conversion unit converts the voice of the other party into text. For example, the text conversion unit picks up the voice of the other party with a microphone of the smart glasses, and the generation AI analyzes the voice and converts it into text. The generation AI converts the voice into text, for example, using voice recognition technology. The display unit displays the content transcribed by the text conversion unit. For example, the display unit displays text on the display of the smart glasses. The display provides information without obstructing the view, for example, by displaying text on a transparent screen. The voice conversion unit converts sign language into a voice signal. For example, the voice conversion unit detects the movement of the sign language with a camera of the smart glasses, and the generation AI analyzes the movement and converts it into voice. The generation AI analyzes the movement of the sign language using, for example, sign language recognition technology, and generates a corresponding voice. The playback unit plays the voice converted by the voice conversion unit. For example, the playback unit plays the voice from the speaker of the smart glasses. The speaker may be, for example, an ear-mounted type or a bone conduction speaker. The control unit controls the text conversion unit and the voice conversion unit. For example, the control unit executes a control algorithm for managing the operation of each unit and realizing smooth operation. As a result, the smart glass system according to the embodiment can realize smooth communication between deaf and mute people and people who cannot use sign language.

[0030] The transcription unit transcribes the other person's voice. For example, the transcription unit picks up the other person's voice with the microphone of the smart glasses, and the generation AI analyzes the voice and converts it into text. The generation AI converts voice to text, for example, using voice recognition technology. Specifically, a high-sensitivity microphone built into the smart glasses collects surrounding voices and transmits the voice data to the generation AI in real time. The generation AI analyzes the voice data using a voice recognition engine and identifies phonemes and words. In addition, it uses natural language processing technology to understand the context and convert it into accurate text. In this process, noise reduction technology and echo cancellation technology are also used in combination, and by removing surrounding noise and reverberation, more accurate voice recognition is possible. The generation AI also supports multiple languages ​​and dialects, and can transcribe voice based on the language set by the user. For example, by supporting not only major languages ​​such as English, Japanese, and Spanish, but also regional dialects and accents, the system can be used by a wide range of users. In addition, the generation AI learns the user's speech patterns and voice characteristics and builds an individually optimized voice recognition model, improving recognition accuracy over time. This allows the transcription unit to quickly and accurately transcribe the other person's voice and provide information to the user in real time.

[0031] The display unit displays the contents that have been converted into text by the text conversion unit. For example, the display unit displays text on the display of the smart glasses. The display provides information without obstructing the view by displaying text on a transparent screen, for example. Specifically, a transparent OLED display or liquid crystal display built into the lens of the smart glasses is used. This allows the user to view the text information while maintaining normal visibility. The display unit can customize the size, color, and font of the text according to the user's preferences. For example, it is possible to display large text for a user with poor eyesight, and to display a specific color away from a user with color vision deficiency. The display unit also has a function for adjusting the display position of the text, and can move the position of the text according to the movement of the user's line of sight. This allows the user to comfortably read the text information without moving the line of sight significantly. Furthermore, the display unit can display not only text information, but also icons and graphical information. For example, by displaying an icon indicating the speaker's name and emotional state in addition to the text obtained as a result of voice recognition, it is possible to provide richer information. This allows the display unit to provide the user with visually easy-to-understand information and to support smooth communication.

[0032] The voice conversion unit converts sign language into a voice signal. For example, the voice conversion unit detects sign language movements with the camera of the smart glasses, and the generation AI analyzes the movements and converts them into voice. The generation AI, for example, analyzes the sign language movements using sign language recognition technology and generates the corresponding voice. Specifically, a high-resolution camera mounted on the smart glasses captures the sign language movements and transmits the video data to the generation AI in real time. The generation AI uses a sign language recognition algorithm to analyze the shape, movement, and position of the hand to understand the meaning of the sign language. In this process, a model using deep learning technology is used, and the sign language movements can be recognized with high accuracy. The generation AI generates the corresponding voice based on the results of analyzing the sign language movements. For example, if the sign language movements mean "hello," the generation AI generates a voice signal saying "hello" and transmits it to the playback unit. The generation AI can generate natural voice by taking into account the grammar and nuances of the expression of the sign language. In addition, the generation AI learns the characteristics and habits of the user's sign language and builds an individually optimized sign language recognition model, improving the recognition accuracy over time. This enables the speech conversion unit to quickly and accurately convert sign language into speech signals and provide information to the user in real time.

[0033] The playback unit plays the voice converted by the voice conversion unit. For example, the playback unit plays the voice from the speaker of the smart glasses. The speaker may be, for example, a type worn on the ear or a bone conduction speaker. Specifically, a small speaker built into the frame of the smart glasses is used to play the voice near the user's ear. This allows the user to clearly hear the voice information while listening to the surrounding sounds. The bone conduction speaker uses a technology that transmits sound to the inner ear through the bone, and since it is possible to hear the voice without blocking the ear, it is possible to simultaneously hear the surrounding environmental sounds. The playback unit can adjust the volume and sound quality according to the user's preferences. For example, optimal voice playback is possible by increasing the volume in a noisy environment and decreasing the volume in a quiet environment. In addition, the playback unit also has a function of adjusting the playback speed of the voice, and can play the voice at a speed that is easy for the user to understand. Furthermore, the playback unit can apply different voice profiles according to the type and content of the voice. For example, optimal voice playback is possible for different purposes, such as reading news and playing music. This enables the playback unit to provide high quality audio information to the user, helping to facilitate smooth communication.

[0034] The text conversion unit includes a language support unit for supporting multiple languages. The language support unit automatically recognizes multiple languages ​​using, for example, a generation AI and converts the multiple languages ​​into text in an appropriate language. For example, the language support unit supports major languages ​​such as English, Japanese, and Chinese. The generation AI automatically detects the language of the input voice using voice recognition technology and converts the input voice into text corresponding to the language. For example, when an English voice is detected, the voice is converted into English text, and when a Japanese voice is detected, the voice is converted into Japanese text. This enables communication with a person who speaks a different language. Furthermore, the language support unit can switch languages ​​in real time. For example, even if a different language is used during a conversation, the generation AI automatically switches the language and converts it into an appropriate text. This enables smooth communication with a plurality of people who speak different languages. A part or all of the above-mentioned processing in the language support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the language support unit can input voice data to the generation AI and cause the generation AI to detect the language and convert the voice data into text. This enables the language support unit to achieve multilingual support and smooth communication with a person who speaks a different language.

[0035] The voice conversion unit includes a sign language detection unit for detecting the movement of the sign language. The sign language detection unit detects the movement of the sign language, for example, using a camera of the smart glasses. For example, the sign language detection unit analyzes the image captured by the camera and recognizes the movement of the sign language. The generation AI analyzes the movement of the sign language using a sign language recognition technology and generates a corresponding voice. For example, the sign language detection unit analyzes the speed, direction, shape, etc. of the hand movement to identify the content of the sign language. For example, the sign language detection unit applies a detailed recognition algorithm when the movement of the sign language is large, and applies a simple recognition algorithm when the movement of the sign language is small. In addition, the sign language detection unit can also apply a recognition algorithm at a high speed when the movement of the sign language is fast. This makes it possible to accurately recognize the movement of the sign language and convert it into voice. A part or all of the above-mentioned processing in the sign language detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the sign language detection unit can input the image data acquired by the camera to the generation AI and cause the generation AI to recognize the movement of the sign language and convert it into voice. This enables the sign language detection unit to accurately recognize sign language movements and convert them into voice.

[0036] The display unit can display characters on the display of the smart glasses. The display unit, for example, displays characters on the display of the smart glasses. The display provides information without obstructing the view by, for example, displaying characters on a transparent screen. For example, the display unit displays the content that has been converted into characters by the characterization unit on the display in real time. The display realizes clear character display by, for example, using a high-resolution OLED display. The display unit also has a function of adjusting the font and size of the characters. For example, the display unit can change the font and size of the characters according to the user's preferences. Furthermore, the display unit has a function of changing the color of the characters. For example, the display unit can automatically adjust the color of the characters according to the background color to improve visibility. This allows the display unit to visually confirm the characterized content. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the character data acquired from the characterization unit to the generation AI and cause the generation AI to adjust the display content. This allows the display unit to visually confirm the characterized content.

[0037] The playback unit can play back the sound from the speaker of the smart glasses. The playback unit, for example, plays back the sound from the speaker of the smart glasses. The speaker can be, for example, a type worn on the ear or a bone conduction speaker. For example, the playback unit plays back the sound converted by the sound conversion unit from the speaker in real time. The speaker can achieve clear sound reproduction by using, for example, a high-quality speaker. The playback unit also has a function of adjusting the volume. For example, the playback unit can automatically adjust the volume according to the surrounding environmental sound and reproduce the sound at an optimal volume. Furthermore, the playback unit has a function of adjusting the tone and speed of the sound. For example, the playback unit can change the tone and speed of the sound according to the user's preference. This allows the playback unit to audibly confirm the content converted into the sound. A part or all of the above-mentioned processing in the playback unit may be performed, for example, using AI, or may be performed without using AI. For example, the playback unit can input the sound data acquired from the sound conversion unit to the generation AI and cause the generation AI to adjust the sound. This allows the playback unit to audibly confirm the content converted into the sound.

[0038] The text conversion unit can adjust the level of detail of the text conversion based on the tone and speed of the voice. The text conversion unit adjusts the level of detail of the text conversion based on, for example, the tone and speed of the voice. For example, when the tone of the voice is high, the text conversion unit uses bold to emphasize the emotion when the generation AI converts the text. In addition, when the voice speed is fast, the text conversion unit can also use concise expressions when the generation AI converts the text. Furthermore, when the tone of the voice is low, the text conversion unit can also use calm expressions when the generation AI converts the text. As a result, the text conversion is performed with an appropriate level of detail according to the tone and speed of the voice. A part or all of the above-mentioned processing in the text conversion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the text conversion unit can input data on the tone and speed of the voice to the generation AI and cause the generation AI to adjust the level of detail. As a result, the text conversion unit can perform the text conversion with an appropriate level of detail according to the tone and speed of the voice.

[0039] The transcription unit can automatically filter background noise and improve the accuracy of transcription. The transcription unit can, for example, automatically filter background noise and improve the accuracy of transcription. For example, the transcription unit removes ambient noise and transcribes only the other party's voice. The transcription unit can also filter environmental sounds such as wind sounds and car sounds to perform clear transcription. Furthermore, when multiple people are speaking, the transcription unit can also prioritize the voice of the main speaker and transcribe it. This improves the accuracy of transcription by removing background noise. A part or all of the above-mentioned processing in the transcription unit may be performed, for example, using AI or may be performed without using AI. For example, the transcription unit can input voice data to the generation AI and cause the generation AI to filter the background noise. This allows the transcription unit to remove background noise and improve the accuracy of transcription.

[0040] The transcription unit can automatically detect the language of the voice and transcribing it into an appropriate language. The transcription unit can, for example, automatically detect the language of the voice and transcribing it into an appropriate language. For example, the transcription unit can detect an English voice and transcribing it into English. The transcription unit can also detect a Japanese voice and transcribing it into Japanese. Furthermore, the transcription unit can detect a Chinese voice and transcribing it into Chinese. This enables multilingual support by automatically detecting the language of the voice and transcribing it into an appropriate language. A part or all of the above-mentioned processing in the transcription unit may be performed, for example, using AI or may be performed without using AI. For example, the transcription unit can input voice data to a generation AI and cause the generation AI to detect the language and transcribing it. This allows the transcription unit to automatically detect the language of the voice and transcribing it into an appropriate language.

[0041] The transcription unit can adjust the expression of the transcription in consideration of the attribute information of the speaker of the voice. The transcription unit adjusts the expression of the transcription in consideration of, for example, the attribute information of the speaker of the voice. For example, the transcription unit uses a simple expression when detecting a child's voice and transcribing the voice. The transcription unit can also use a soft expression when detecting a woman's voice and transcribing the voice. Furthermore, the transcription unit can also use a polite expression when detecting an elderly person's voice and transcribing the voice. As a result, transcription is performed with an appropriate expression according to the attribute information of the speaker. A part or all of the above-mentioned processing in the transcription unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transcription unit inputs the attribute information of the speaker to the generation AI and causes the generation AI to adjust the expression of the transcription based on the attribute information. As a result, the transcription unit can perform transcription with an appropriate expression according to the attribute information of the speaker.

[0042] The display unit can adjust the display priority based on the importance of the display contents. The display unit adjusts the display priority based on, for example, the importance of the display contents. For example, the display unit displays important information in a large font. The display unit can also display low-priority information in a small font. Furthermore, the display unit can also display urgent information by highlighting it in red. As a result, the display is performed with an appropriate priority according to the importance of the display contents. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data of the display contents to the generation AI and cause the generation AI to adjust the display priority based on the importance. As a result, the display unit can display with an appropriate priority according to the importance of the display contents.

[0043] The display unit can apply different display styles depending on the category of the display content. The display unit applies different display styles depending on, for example, the category of the display content. For example, the display unit displays important notifications in bold. The display unit can also display general information in a normal font. Furthermore, the display unit can display supplementary information in italics. As a result, the display is performed in an appropriate style depending on the category of the display content. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data of the display content to the generation AI and cause the generation AI to apply a display style based on the category. As a result, the display unit can display in an appropriate style depending on the category of the display content.

[0044] The display unit can adjust the display order based on the submission time of the display contents. The display unit adjusts the display order based on, for example, the submission time of the display contents. For example, the display unit displays the latest information first. The display unit can also display old information later. Furthermore, the display unit can display information in chronological order based on the submission time. As a result, the display is performed in an appropriate order according to the submission time of the display contents. A part or all of the above-mentioned processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit inputs data on the submission time of the display contents to the generation AI, and causes the generation AI to adjust the display order based on the submission time. As a result, the display unit can display in an appropriate order according to the submission time of the display contents.

[0045] The display unit can adjust the display layout based on the relevance of the display contents. The display unit adjusts the display layout based on the relevance of the display contents, for example. For example, the display unit groups and displays related information. The display unit can also display information with low relevance in a separate section. Furthermore, the display unit can display information with high relevance arranged nearby. As a result, the display is performed in an appropriate layout according to the relevance of the display contents. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit inputs data on the relevance of the display contents to the generation AI, and can cause the generation AI to adjust the display layout based on the relevance. As a result, the display unit can display in an appropriate layout according to the relevance of the display contents.

[0046] The voice conversion unit can adjust the level of detail of the voice based on the level of detail of the sign language movement. The voice conversion unit adjusts the level of detail of the voice based on the level of detail of the sign language movement, for example. For example, when the sign language movement is large, the voice conversion unit generates a detailed voice. Also, when the sign language movement is small, the voice conversion unit can generate a concise voice. Furthermore, when the sign language movement is fast, the voice conversion unit can generate a voice at a fast speed. As a result, a voice is generated with an appropriate level of detail according to the sign language movement. A part or all of the above-mentioned processing in the voice conversion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the voice conversion unit inputs data of the sign language movement to the generation AI and causes the generation AI to adjust the level of detail. As a result, the voice conversion unit can generate a voice with an appropriate level of detail according to the sign language movement.

[0047] The voice conversion unit can apply different voice conversion algorithms depending on the category of the sign language. For example, the voice conversion unit applies different voice conversion algorithms depending on the category of the sign language. For example, the voice conversion unit applies a natural voice conversion algorithm to the sign language of everyday conversation. In addition, the voice conversion unit can also apply a specialized voice conversion algorithm to the sign language including technical terms. Furthermore, the voice conversion unit can also apply a voice conversion algorithm that emphasizes emotions to the sign language that expresses emotions. As a result, a voice is generated with an appropriate algorithm according to the category of the sign language. A part or all of the above-mentioned processing in the voice conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice conversion unit inputs data of the category of the sign language to the generation AI and causes the generation AI to apply the voice conversion algorithm based on the category. As a result, the voice conversion unit can generate a voice with an appropriate algorithm according to the category of the sign language.

[0048] The voice conversion unit can determine the priority of the voices based on the time of submission of the sign language. The voice conversion unit, for example, determines the priority of the voices based on the time of submission of the sign language. For example, the voice conversion unit converts the latest sign language into voice with priority. The voice conversion unit can also convert older sign language into voice with priority. Furthermore, the voice conversion unit can convert the sign language into voice in chronological order based on the submission time. As a result, voice is generated with an appropriate priority according to the submission time of the sign language. A part or all of the above-mentioned processing in the voice conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice conversion unit inputs data on the submission time of the sign language to the generation AI, and causes the generation AI to execute the determination of the priority of the voices based on the submission time. As a result, the voice conversion unit can generate voice with an appropriate priority according to the submission time of the sign language.

[0049] The speech conversion unit can adjust the order of the speech based on the relevance of the sign languages. The speech conversion unit, for example, adjusts the order of the speech based on the relevance of the sign languages. For example, the speech conversion unit groups related sign languages ​​and converts them into speech. The speech conversion unit can also convert less related sign languages ​​into speech in a separate section. Furthermore, the speech conversion unit can place more related sign languages ​​close together and convert them into speech. As a result, speech is generated in an appropriate order according to the relevance of the sign languages. A part or all of the above-mentioned processing in the speech conversion unit may be performed, for example, using AI, or may be performed without using AI. For example, the speech conversion unit can input data on the relevance of the sign languages ​​to the generation AI and cause the generation AI to adjust the order of the speech based on the relevance. As a result, the speech conversion unit can generate speech in an appropriate order according to the relevance of the sign languages.

[0050] The playback unit can adjust the priority of playback based on the importance of the playback content. The playback unit adjusts the priority of playback based on, for example, the importance of the playback content. For example, the playback unit gives priority to important information for playback. The playback unit can also postpone playback of information with a low priority. Furthermore, the playback unit can also play back urgent information first. As a result, playback is performed with an appropriate priority according to the importance of the playback content. A part or all of the above-mentioned processing in the playback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the playback unit can input data of the playback content to the generation AI and cause the generation AI to adjust the priority of playback based on the importance. As a result, the playback unit can play back with an appropriate priority according to the importance of the playback content.

[0051] The playback unit can apply different playback styles depending on the category of the playback content. The playback unit applies different playback styles depending on the category of the playback content, for example. For example, the playback unit plays important notifications with emphasis. The playback unit can also play general information in a normal tone. Furthermore, the playback unit can also play supplementary information discreetly. As a result, playback is performed in an appropriate style depending on the category of the playback content. Some or all of the above-mentioned processing in the playback unit may be performed using, for example, AI, or may be performed without using AI. For example, the playback unit can input data of the playback content to the generation AI and cause the generation AI to apply a playback style based on the category. As a result, the playback unit can play in an appropriate style depending on the category of the playback content.

[0052] The playback unit can adjust the order of playback based on the submission time of the playback content. The playback unit adjusts the order of playback based on, for example, the submission time of the playback content. For example, the playback unit plays the latest information first. The playback unit can also play old information later. Furthermore, the playback unit can also play information in chronological order based on the submission time. This allows playback to be performed in an appropriate order according to the submission time of the playback content. A part or all of the above-mentioned processing in the playback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the playback unit can input data on the submission time of the playback content to the generation AI and cause the generation AI to adjust the order of playback based on the submission time. This allows the playback unit to play in an appropriate order according to the submission time of the playback content.

[0053] The playback unit can adjust the layout of playback based on the relevance of the playback contents. The playback unit adjusts the layout of playback based on the relevance of the playback contents, for example. For example, the playback unit groups related information and plays it back. The playback unit can also play back information with low relevance in a separate section. Furthermore, the playback unit can also play back information with high relevance arranged nearby. As a result, playback is performed with an appropriate layout according to the relevance of the playback contents. A part or all of the above-mentioned processing in the playback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the playback unit can input data on the relevance of the playback contents to the generation AI and cause the generation AI to adjust the playback layout based on the relevance. As a result, the playback unit can play back with an appropriate layout according to the relevance of the playback contents.

[0054] The control unit can select the optimal control method by referring to the operation history of each unit. The control unit, for example, selects the optimal control method by referring to the operation history of each unit. For example, the control unit analyzes the past operation history of each unit and selects the optimal control method. The control unit can also prioritize and control frequently used functions from the operation history of each unit. Furthermore, the control unit can also select an efficient control method based on the operation history of each unit. As a result, the optimal control method based on the operation history of each unit is selected. A part or all of the above-mentioned processing in the control unit may be performed, for example, using AI, or may be performed without using AI. For example, the control unit can input data of the operation history of each unit to the generation AI and cause the generation AI to select the optimal control method based on the operation history. As a result, the control unit can select the optimal control method based on the operation history of each unit.

[0055] The control unit can optimize the control algorithm according to the operating status of each unit. The control unit optimizes the control algorithm according to, for example, the operating status of each unit. For example, the control unit monitors the current operating status of each unit in real time and optimizes the control algorithm. The control unit can also dynamically adjust the control algorithm according to the operating status of each unit. Furthermore, the control unit can analyze the operating status of each unit and apply the optimal control algorithm. As a result, the optimal control algorithm according to the operating status of each unit is applied. A part or all of the above-mentioned processing in the control unit may be performed, for example, using AI, or may be performed without using AI. For example, the control unit can input data on the operating status of each unit to the generation AI and cause the generation AI to optimize the control algorithm based on the operating status. As a result, the control unit can apply the optimal control algorithm according to the operating status of each unit.

[0056] The control unit can adjust the order of control based on the operation timing of each unit. The control unit adjusts the order of control based on, for example, the operation timing of each unit. For example, the control unit adjusts the order of control based on the latest operation timing of each unit. The control unit can also determine an efficient control order based on the operation timing of each unit. Furthermore, the control unit can set an optimal control order taking into account the operation timing of each unit. As a result, control is performed in an appropriate order according to the operation timing of each unit. A part or all of the above-mentioned processing in the control unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the control unit can input data on the operation timing of each unit to the generation AI and cause the generation AI to adjust the order of control based on the operation timing. As a result, the control unit can perform control in an appropriate order according to the operation timing of each unit.

[0057] The control unit can adjust the layout of the control based on the relevance of each part. The control unit adjusts the layout of the control based on the relevance of each part, for example. For example, the control unit groups and controls each related part. The control unit can also control each part with low relevance to a different section. Furthermore, the control unit can control each part with high relevance by arranging them close to each other. As a result, control is performed with an appropriate layout according to the relevance of each part. A part or all of the above-mentioned processing in the control unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the control unit can input data on the relevance of each part to the generation AI and cause the generation AI to adjust the layout of the control based on the relevance. As a result, the control unit can perform control with an appropriate layout according to the relevance of each part.

[0058] The multilingual support unit can select the optimal multilingual support method by referring to the frequency of use of each language. The multilingual support unit selects the optimal multilingual support method by referring to the frequency of use of each language, for example. For example, the multilingual support unit prioritizes languages ​​that are used frequently. The multilingual support unit can also prioritize languages ​​that are used less frequently. Furthermore, the multilingual support unit can select an efficient multilingual support method based on the frequency of use of each language. This allows the optimal multilingual support method based on the frequency of use of each language to be selected. A part or all of the above-mentioned processing in the multilingual support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the multilingual support unit can input data on the frequency of use of each language to the generation AI and cause the generation AI to select the optimal multilingual support method based on the frequency of use. This allows the multilingual support unit to select the optimal multilingual support method based on the frequency of use of each language.

[0059] The multilingual support unit can apply different multilingual support methods according to the characteristics of each language. The multilingual support unit can apply different multilingual support methods according to the characteristics of each language, for example. For example, the multilingual support unit applies a natural translation algorithm to English. The multilingual support unit can also apply a translation algorithm that takes into account the context to Japanese. Furthermore, the multilingual support unit can also apply a translation algorithm that emphasizes the meaning of characters to Chinese. This allows the optimal multilingual support method to be applied according to the characteristics of each language. A part or all of the above-mentioned processing in the multilingual support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the multilingual support unit can input data on the characteristics of each language to the generation AI and cause the generation AI to apply the multilingual support method based on the characteristics. This allows the multilingual support unit to apply the optimal multilingual support method according to the characteristics of each language.

[0060] The multilingual support unit can adjust the order of multilingual support based on the submission time of each language. The multilingual support unit adjusts the order of multilingual support based on, for example, the submission time of each language. For example, the multilingual support unit supports the newest language first. The multilingual support unit can also support older languages ​​later. Furthermore, the multilingual support unit can support languages ​​in chronological order based on the submission time. This allows multilingual support to be performed in an appropriate order according to the submission time of each language. A part or all of the above-mentioned processing in the multilingual support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the multilingual support unit can input data on the submission time of each language to the generation AI and cause the generation AI to adjust the order of multilingual support based on the submission time. This allows the multilingual support unit to perform multilingual support in an appropriate order according to the submission time of each language.

[0061] The multilingual support unit can adjust the multilingual layout based on the relevance of each language. The multilingual support unit adjusts the multilingual layout based on the relevance of each language, for example. For example, the multilingual support unit groups related languages ​​to support them. The multilingual support unit can also support languages ​​with low relevance in a different section. Furthermore, the multilingual support unit can also support languages ​​with high relevance by arranging them close to each other. As a result, multilingual support is performed with an appropriate layout according to the relevance of each language. A part or all of the above-mentioned processing in the multilingual support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the multilingual support unit can input data on the relevance of each language to the generation AI and cause the generation AI to adjust the multilingual layout based on the relevance. As a result, the multilingual support unit can support multilingual support with an appropriate layout according to the relevance of each language.

[0062] The sign language recognition unit can optimize the algorithm of the sign language recognition based on the level of detail of the sign language movement. The sign language recognition unit optimizes the algorithm of the sign language recognition based on the level of detail of the sign language movement, for example. For example, the sign language recognition unit applies a detailed recognition algorithm when the sign language movement is large. Also, the sign language recognition unit can apply a simple recognition algorithm when the sign language movement is small. Furthermore, the sign language recognition unit can apply a recognition algorithm at a high speed when the sign language movement is fast. As a result, the sign language recognition is performed with an optimal algorithm according to the sign language movement. A part or all of the above-mentioned processing in the sign language recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sign language recognition unit inputs data of the sign language movement to the generation AI, and causes the generation AI to optimize the recognition algorithm based on the level of detail of the movement. As a result, the sign language recognition unit can perform the sign language recognition with an optimal algorithm according to the sign language movement.

[0063] The sign language recognition unit can apply different sign language recognition algorithms depending on the category of the sign language. For example, the sign language recognition unit applies different sign language recognition algorithms depending on the category of the sign language. For example, the sign language recognition unit applies a natural recognition algorithm to the sign language of everyday conversation. In addition, the sign language recognition unit can also apply a specialized recognition algorithm to the sign language including technical terms. Furthermore, the sign language recognition unit can also apply a recognition algorithm that emphasizes emotions to the sign language that expresses emotions. As a result, the sign language recognition is performed with an optimal algorithm depending on the category of the sign language. A part or all of the above-mentioned processing in the sign language recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the sign language recognition unit can input data of the category of the sign language to the generation AI and cause the generation AI to apply the recognition algorithm based on the category. As a result, the sign language recognition unit can perform the sign language recognition with an optimal algorithm depending on the category of the sign language.

[0064] The sign language recognition unit can adjust the order of sign language recognition based on the time of submission of the sign language. The sign language recognition unit adjusts the order of sign language recognition based on, for example, the time of submission of the sign language. For example, the sign language recognition unit recognizes the latest sign language first. Also, the sign language recognition unit can recognize older sign languages ​​later. Furthermore, the sign language recognition unit can recognize sign languages ​​in chronological order based on the time of submission. As a result, the sign language recognition is performed in an appropriate order according to the time of submission of the sign language. A part or all of the above-mentioned processing in the sign language recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sign language recognition unit inputs data on the time of submission of the sign language to the generation AI, and causes the generation AI to adjust the order of sign language recognition based on the time of submission. As a result, the sign language recognition unit can perform sign language recognition in an appropriate order according to the time of submission of the sign language.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The smart glasses system may further include a health monitoring unit that monitors the health condition of the user. The health monitoring unit may, for example, use sensors built into the smart glasses to measure the user's vital signs, such as heart rate, blood pressure, and body temperature, in real time. This allows the user's health condition to be constantly monitored, and an alert may be issued if an abnormality is detected. For example, if the heart rate rises suddenly, the health monitoring unit may display a notification encouraging the user to take a rest. In addition, if the blood pressure is high, health advice may be provided. Furthermore, if the body temperature is abnormally high, a notification recommending a visit to a medical institution may be displayed. This supports the user's health management and reduces health risks.

[0067] The smart glasses system may further include a location information acquisition unit that acquires the user's location information. The location information acquisition unit acquires the user's current location in real time, for example, using a GPS. This makes it possible to provide optimal route guidance and surrounding information based on the user's location information. For example, when a user heads to a destination, the location information acquisition unit calculates an optimal route and displays it on the display of the smart glasses. In addition, when a user visits a tourist spot, the location information acquisition unit can also provide information on nearby tourist spots and restaurants. Furthermore, in the event of an emergency, a quick response is possible based on the user's location information. This makes it possible to improve the user's convenience.

[0068] The smart glasses system may further include an activity recording unit that records the user's activities. The activity recording unit may use, for example, an acceleration sensor or a gyro sensor built into the smart glasses to record the user's number of steps, distance traveled, calories burned, and the like. This allows the user's activity level to be understood and used for health management. For example, the activity recording unit may record the number of steps the user takes in a day and display the degree of goal achievement. It may also calculate the distance traveled and calories burned and provide feedback to the user. Furthermore, the activity recording unit may analyze the user's exercise pattern and provide advice to resolve lack of exercise. This may support the user's health maintenance.

[0069] The smart glasses system may further include an eye tracking unit that tracks the user's gaze. The eye tracking unit may use, for example, a camera built into the smart glasses to track the user's gaze movement in real time. This allows the user to identify the object that the user is paying attention to and provide related information. For example, when the user is looking at a particular building, the eye tracking unit may display information about the building on the display of the smart glasses. In addition, when the user is looking at a product, the eye tracking unit may display detailed information and reviews about the product. Furthermore, the eye tracking unit may analyze the user's gaze movement and provide advice to reduce visual fatigue. This may improve the user's convenience.

[0070] The smart glasses system may further include a voice recognition unit that recognizes voice commands from the user. The voice recognition unit recognizes the user's voice commands in real time, for example, using a microphone in the smart glasses. This allows the user to operate various functions of the smart glasses using voice commands. For example, when the user says "Tell me the weather forecast," the voice recognition unit recognizes the command and displays the weather forecast. Also, when the user says "Play music," the voice recognition unit can recognize the command and play music. Furthermore, the voice recognition unit can learn the user's voice commands and perform more accurate recognition. This can improve the operability for the user.

[0071] The process flow of the first embodiment will be briefly described below.

[0072] Step 1: The transcription unit transcribes the other person's voice. For example, the transcription unit picks up the other person's voice with the microphone of the smart glasses, and the generation AI analyzes the voice and converts it into text. The generation AI converts the voice into text, for example, using voice recognition technology. Step 2: The display unit displays the content that has been converted into text by the conversion unit. For example, the display unit displays text on a display of smart glasses. The display provides information without obstructing the view by displaying text on a transparent screen, for example. Step 3: The speech conversion unit converts the sign language into a speech signal. For example, the speech conversion unit detects sign language movements using the smart glasses' camera, and the generation AI analyzes the movements and converts them into speech. For example, the generation AI uses sign language recognition technology to analyze the sign language movements and generate the corresponding speech. Step 4: The playback unit plays the audio converted by the audio conversion unit. For example, the playback unit plays the audio from a speaker in the smart glasses. The speaker may be, for example, a type worn on the ear or a bone conduction speaker. Step 5: The control unit controls the text conversion unit and the voice conversion unit. For example, the control unit executes a control algorithm for managing the operations of each unit and realizing smooth operation.

[0073] (Example 2) The smart glasses system according to the embodiment of the present invention is a system that enables smooth communication between deaf-mute people and people who cannot sign using a generation AI. This system has a function of instantly converting the other person's voice into text and supporting multiple languages, and a function of instantly converting one's own sign language into audio. This enables smooth communication between deaf-mute people and people who cannot sign. For example, the other person's voice is picked up by the microphone of the smart glasses, and the generation AI analyzes the voice and converts it into text. The converted text is displayed on the display of the smart glasses. Next, when the deaf-mute person uses sign language, the camera of the smart glasses recognizes the sign language, and the generation AI analyzes the content, converts it into audio, and plays it from the speaker. In this way, smooth communication is possible between deaf-mute people and people who cannot sign. As a result, the smart glasses system can realize smooth communication between deaf-mute people and people who cannot sign.

[0074] The smart glasses system according to the embodiment includes a text conversion unit, a display unit, a voice conversion unit, a playback unit, and a control unit. The text conversion unit converts the voice of the other party into text. For example, the text conversion unit picks up the voice of the other party with a microphone of the smart glasses, and the generation AI analyzes the voice and converts it into text. The generation AI converts the voice into text, for example, using voice recognition technology. The display unit displays the content transcribed by the text conversion unit. For example, the display unit displays text on the display of the smart glasses. The display provides information without obstructing the view, for example, by displaying text on a transparent screen. The voice conversion unit converts sign language into a voice signal. For example, the voice conversion unit detects the movement of the sign language with a camera of the smart glasses, and the generation AI analyzes the movement and converts it into voice. The generation AI analyzes the movement of the sign language using, for example, sign language recognition technology, and generates a corresponding voice. The playback unit plays the voice converted by the voice conversion unit. For example, the playback unit plays the voice from the speaker of the smart glasses. The speaker may be, for example, an ear-mounted type or a bone conduction speaker. The control unit controls the text conversion unit and the voice conversion unit. For example, the control unit executes a control algorithm for managing the operation of each unit and realizing smooth operation. As a result, the smart glass system according to the embodiment can realize smooth communication between deaf and mute people and people who cannot use sign language.

[0075] The transcription unit transcribes the other person's voice. For example, the transcription unit picks up the other person's voice with the microphone of the smart glasses, and the generation AI analyzes the voice and converts it into text. The generation AI converts voice to text, for example, using voice recognition technology. Specifically, a high-sensitivity microphone built into the smart glasses collects surrounding voices and transmits the voice data to the generation AI in real time. The generation AI analyzes the voice data using a voice recognition engine and identifies phonemes and words. In addition, it uses natural language processing technology to understand the context and convert it into accurate text. In this process, noise reduction technology and echo cancellation technology are also used in combination, and by removing surrounding noise and reverberation, more accurate voice recognition is possible. The generation AI also supports multiple languages ​​and dialects, and can transcribe voice based on the language set by the user. For example, by supporting not only major languages ​​such as English, Japanese, and Spanish, but also regional dialects and accents, the system can be used by a wide range of users. In addition, the generation AI learns the user's speech patterns and voice characteristics and builds an individually optimized voice recognition model, improving recognition accuracy over time. This allows the transcription unit to quickly and accurately transcribe the other person's voice and provide information to the user in real time.

[0076] The display unit displays the contents that have been converted into text by the text conversion unit. For example, the display unit displays text on the display of the smart glasses. The display provides information without obstructing the view by displaying text on a transparent screen, for example. Specifically, a transparent OLED display or liquid crystal display built into the lens of the smart glasses is used. This allows the user to view the text information while maintaining normal visibility. The display unit can customize the size, color, and font of the text according to the user's preferences. For example, it is possible to display large text for a user with poor eyesight, and to display a specific color away from a user with color vision deficiency. The display unit also has a function for adjusting the display position of the text, and can move the position of the text according to the movement of the user's line of sight. This allows the user to comfortably read the text information without moving the line of sight significantly. Furthermore, the display unit can display not only text information, but also icons and graphical information. For example, by displaying an icon indicating the speaker's name and emotional state in addition to the text obtained as a result of voice recognition, it is possible to provide richer information. This allows the display unit to provide the user with visually easy-to-understand information and to support smooth communication.

[0077] The voice conversion unit converts sign language into a voice signal. For example, the voice conversion unit detects sign language movements with the camera of the smart glasses, and the generation AI analyzes the movements and converts them into voice. The generation AI, for example, analyzes the sign language movements using sign language recognition technology and generates the corresponding voice. Specifically, a high-resolution camera mounted on the smart glasses captures the sign language movements and transmits the video data to the generation AI in real time. The generation AI uses a sign language recognition algorithm to analyze the shape, movement, and position of the hand to understand the meaning of the sign language. In this process, a model using deep learning technology is used, and the sign language movements can be recognized with high accuracy. The generation AI generates the corresponding voice based on the results of analyzing the sign language movements. For example, if the sign language movements mean "hello," the generation AI generates a voice signal saying "hello" and transmits it to the playback unit. The generation AI can generate natural voice by taking into account the grammar and nuances of the expression of the sign language. In addition, the generation AI learns the characteristics and habits of the user's sign language and builds an individually optimized sign language recognition model, improving the recognition accuracy over time. This enables the speech conversion unit to quickly and accurately convert sign language into speech signals and provide information to the user in real time.

[0078] The playback unit plays the voice converted by the voice conversion unit. For example, the playback unit plays the voice from the speaker of the smart glasses. The speaker may be, for example, a type worn on the ear or a bone conduction speaker. Specifically, a small speaker built into the frame of the smart glasses is used to play the voice near the user's ear. This allows the user to clearly hear the voice information while listening to the surrounding sounds. The bone conduction speaker uses a technology that transmits sound to the inner ear through the bone, and since it is possible to hear the voice without blocking the ear, it is possible to simultaneously hear the surrounding environmental sounds. The playback unit can adjust the volume and sound quality according to the user's preferences. For example, optimal voice playback is possible by increasing the volume in a noisy environment and decreasing the volume in a quiet environment. In addition, the playback unit also has a function of adjusting the playback speed of the voice, and can play the voice at a speed that is easy for the user to understand. Furthermore, the playback unit can apply different voice profiles according to the type and content of the voice. For example, optimal voice playback is possible for different purposes, such as reading news and playing music. This enables the playback unit to provide high quality audio information to the user, helping to facilitate smooth communication.

[0079] The text conversion unit includes a language support unit for supporting multiple languages. The language support unit automatically recognizes multiple languages ​​using, for example, a generation AI and converts the multiple languages ​​into text in an appropriate language. For example, the language support unit supports major languages ​​such as English, Japanese, and Chinese. The generation AI automatically detects the language of the input voice using voice recognition technology and converts the input voice into text corresponding to the language. For example, when an English voice is detected, the voice is converted into English text, and when a Japanese voice is detected, the voice is converted into Japanese text. This enables communication with a person who speaks a different language. Furthermore, the language support unit can switch languages ​​in real time. For example, even if a different language is used during a conversation, the generation AI automatically switches the language and converts it into an appropriate text. This enables smooth communication with a plurality of people who speak different languages. A part or all of the above-mentioned processing in the language support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the language support unit can input voice data to the generation AI and cause the generation AI to detect the language and convert the voice data into text. This enables the language support unit to achieve multilingual support and smooth communication with a person who speaks a different language.

[0080] The voice conversion unit includes a sign language detection unit for detecting the movement of the sign language. The sign language detection unit detects the movement of the sign language, for example, using a camera of the smart glasses. For example, the sign language detection unit analyzes the image captured by the camera and recognizes the movement of the sign language. The generation AI analyzes the movement of the sign language using a sign language recognition technology and generates a corresponding voice. For example, the sign language detection unit analyzes the speed, direction, shape, etc. of the hand movement to identify the content of the sign language. For example, the sign language detection unit applies a detailed recognition algorithm when the movement of the sign language is large, and applies a simple recognition algorithm when the movement of the sign language is small. In addition, the sign language detection unit can also apply a recognition algorithm at a high speed when the movement of the sign language is fast. This makes it possible to accurately recognize the movement of the sign language and convert it into voice. A part or all of the above-mentioned processing in the sign language detection unit may be performed, for example, using AI, or may be performed without using AI. For example, the sign language detection unit can input the image data acquired by the camera to the generation AI and cause the generation AI to recognize the movement of the sign language and convert it into voice. This enables the sign language detection unit to accurately recognize sign language movements and convert them into voice.

[0081] The display unit can display characters on the display of the smart glasses. The display unit, for example, displays characters on the display of the smart glasses. The display provides information without obstructing the view by, for example, displaying characters on a transparent screen. For example, the display unit displays the content that has been converted into characters by the characterization unit on the display in real time. The display realizes clear character display by, for example, using a high-resolution OLED display. The display unit also has a function of adjusting the font and size of the characters. For example, the display unit can change the font and size of the characters according to the user's preferences. Furthermore, the display unit has a function of changing the color of the characters. For example, the display unit can automatically adjust the color of the characters according to the background color to improve visibility. This allows the display unit to visually confirm the characterized content. A part or all of the above-mentioned processing in the display unit may be performed, for example, using AI, or may be performed without using AI. For example, the display unit can input the character data acquired from the characterization unit to the generation AI and cause the generation AI to adjust the display content. This allows the display unit to visually confirm the characterized content.

[0082] The playback unit can play back the sound from the speaker of the smart glasses. The playback unit, for example, plays back the sound from the speaker of the smart glasses. The speaker can be, for example, a type worn on the ear or a bone conduction speaker. For example, the playback unit plays back the sound converted by the sound conversion unit from the speaker in real time. The speaker can achieve clear sound reproduction by using, for example, a high-quality speaker. The playback unit also has a function of adjusting the volume. For example, the playback unit can automatically adjust the volume according to the surrounding environmental sound and reproduce the sound at an optimal volume. Furthermore, the playback unit has a function of adjusting the tone and speed of the sound. For example, the playback unit can change the tone and speed of the sound according to the user's preference. This allows the playback unit to audibly confirm the content converted into the sound. A part or all of the above-mentioned processing in the playback unit may be performed, for example, using AI, or may be performed without using AI. For example, the playback unit can input the sound data acquired from the sound conversion unit to the generation AI and cause the generation AI to adjust the sound. This allows the playback unit to audibly confirm the content converted into the sound.

[0083] The text conversion unit can estimate the emotion of the other party and adjust the expression method of the text conversion based on the estimated emotion. The text conversion unit, for example, estimates the emotion of the other party and adjusts the expression method of the text conversion based on the estimated emotion. For example, when the other party is angry, the text conversion unit uses an emphasized expression when the generation AI converts the message into text. Also, when the other party is sad, the text conversion unit can use a soft expression when the generation AI converts the message into text. Furthermore, when the other party is excited, the text conversion unit can express the message by using exclamation marks and the like. As a result, the text conversion is performed using an appropriate expression method according to the emotion of the other party. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the text conversion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transcription unit can input the other party's emotional data to the generation AI and cause the generation AI to adjust the expression method of the transcription based on the emotion. This enables the transcription unit to perform transcription in an appropriate expression method according to the other party's emotion.

[0084] The text conversion unit can adjust the level of detail of the text conversion based on the tone and speed of the voice. The text conversion unit adjusts the level of detail of the text conversion based on, for example, the tone and speed of the voice. For example, when the tone of the voice is high, the text conversion unit uses bold to emphasize the emotion when the generation AI converts the text. In addition, when the voice speed is fast, the text conversion unit can also use concise expressions when the generation AI converts the text. Furthermore, when the tone of the voice is low, the text conversion unit can also use calm expressions when the generation AI converts the text. As a result, the text conversion is performed with an appropriate level of detail according to the tone and speed of the voice. A part or all of the above-mentioned processing in the text conversion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the text conversion unit can input data on the tone and speed of the voice to the generation AI and cause the generation AI to adjust the level of detail. As a result, the text conversion unit can perform the text conversion with an appropriate level of detail according to the tone and speed of the voice.

[0085] The transcription unit can automatically filter background noise and improve the accuracy of transcription. The transcription unit can, for example, automatically filter background noise and improve the accuracy of transcription. For example, the transcription unit removes ambient noise and transcribes only the other party's voice. The transcription unit can also filter environmental sounds such as wind sounds and car sounds to perform clear transcription. Furthermore, when multiple people are speaking, the transcription unit can also prioritize the voice of the main speaker and transcribe it. This improves the accuracy of transcription by removing background noise. A part or all of the above-mentioned processing in the transcription unit may be performed, for example, using AI or may be performed without using AI. For example, the transcription unit can input voice data to the generation AI and cause the generation AI to filter the background noise. This allows the transcription unit to remove background noise and improve the accuracy of transcription.

[0086] The transcription unit can estimate the emotion of the other party and determine the priority of transcription based on the estimated emotion. The transcription unit, for example, estimates the emotion of the other party and determines the priority of transcription based on the estimated emotion. For example, when the other party is angry, the transcription unit prioritizes important parts when the generation AI transcribs. In addition, when the other party is sad, the transcription unit can also prioritize parts expressing emotions when the generation AI transcribs. Furthermore, when the other party is excited, the transcription unit can also prioritize parts emphasizing emotions when the generation AI transcribs. As a result, transcription is performed with appropriate priority according to the emotion of the other party. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the transcription unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transcription unit can input the other party's emotional data to the generation AI and have the generation AI determine the priority of transcription based on the emotions. This enables the transcription unit to perform transcription in an appropriate order of priority according to the other party's emotions.

[0087] The transcription unit can automatically detect the language of the voice and transcribing it into an appropriate language. The transcription unit can, for example, automatically detect the language of the voice and transcribing it into an appropriate language. For example, the transcription unit can detect an English voice and transcribing it into English. The transcription unit can also detect a Japanese voice and transcribing it into Japanese. Furthermore, the transcription unit can detect a Chinese voice and transcribing it into Chinese. This enables multilingual support by automatically detecting the language of the voice and transcribing it into an appropriate language. A part or all of the above-mentioned processing in the transcription unit may be performed, for example, using AI or may be performed without using AI. For example, the transcription unit can input voice data to a generation AI and cause the generation AI to detect the language and transcribing it. This allows the transcription unit to automatically detect the language of the voice and transcribing it into an appropriate language.

[0088] The transcription unit can adjust the expression of the transcription in consideration of the attribute information of the speaker of the voice. The transcription unit adjusts the expression of the transcription in consideration of, for example, the attribute information of the speaker of the voice. For example, the transcription unit uses a simple expression when detecting a child's voice and transcribing the voice. The transcription unit can also use a soft expression when detecting a woman's voice and transcribing the voice. Furthermore, the transcription unit can also use a polite expression when detecting an elderly person's voice and transcribing the voice. As a result, transcription is performed with an appropriate expression according to the attribute information of the speaker. A part or all of the above-mentioned processing in the transcription unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the transcription unit inputs the attribute information of the speaker to the generation AI and causes the generation AI to adjust the expression of the transcription based on the attribute information. As a result, the transcription unit can perform transcription with an appropriate expression according to the attribute information of the speaker.

[0089] The display unit can estimate the user's emotion and adjust the font and color of the display based on the estimated emotion. The display unit, for example, estimates the user's emotion and adjusts the font and color of the display based on the estimated emotion. For example, the display unit uses a font with a calm color when the user is nervous. Also, the display unit can use a font with a bright color when the user is relaxed. Furthermore, the display unit can use a font with high visibility when the user is tired. As a result, display is performed in an appropriate font and color according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit inputs the user's emotion data to the generation AI and causes the generation AI to adjust the font and color based on the emotion. As a result, the display unit can display in an appropriate font and color according to the user's emotion.

[0090] The display unit can adjust the display priority based on the importance of the display contents. The display unit adjusts the display priority based on, for example, the importance of the display contents. For example, the display unit displays important information in a large font. The display unit can also display low-priority information in a small font. Furthermore, the display unit can also display urgent information by highlighting it in red. As a result, the display is performed with an appropriate priority according to the importance of the display contents. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data of the display contents to the generation AI and cause the generation AI to adjust the display priority based on the importance. As a result, the display unit can display with an appropriate priority according to the importance of the display contents.

[0091] The display unit can apply different display styles depending on the category of the display content. The display unit applies different display styles depending on, for example, the category of the display content. For example, the display unit displays important notifications in bold. The display unit can also display general information in a normal font. Furthermore, the display unit can display supplementary information in italics. As a result, the display is performed in an appropriate style depending on the category of the display content. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit can input data of the display content to the generation AI and cause the generation AI to apply a display style based on the category. As a result, the display unit can display in an appropriate style depending on the category of the display content.

[0092] The display unit can estimate the user's emotion and adjust the length of the display based on the estimated emotion. The display unit, for example, estimates the user's emotion and adjusts the length of the display based on the estimated emotion. For example, the display unit provides a short display when the user is in a hurry. The display unit can also provide a detailed display when the user is relaxed. Furthermore, the display unit can also provide a concise display when the user is tired. As a result, the display is performed at an appropriate length according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of the display based on the emotion. As a result, the display unit can perform display at an appropriate length according to the user's emotion.

[0093] The display unit can adjust the display order based on the submission time of the display contents. The display unit adjusts the display order based on, for example, the submission time of the display contents. For example, the display unit displays the latest information first. The display unit can also display old information later. Furthermore, the display unit can display information in chronological order based on the submission time. As a result, the display is performed in an appropriate order according to the submission time of the display contents. A part or all of the above-mentioned processing in the display unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the display unit inputs data on the submission time of the display contents to the generation AI, and causes the generation AI to adjust the display order based on the submission time. As a result, the display unit can display in an appropriate order according to the submission time of the display contents.

[0094] The display unit can adjust the display layout based on the relevance of the display contents. The display unit adjusts the display layout based on the relevance of the display contents, for example. For example, the display unit groups and displays related information. The display unit can also display information with low relevance in a separate section. Furthermore, the display unit can display information with high relevance arranged nearby. As a result, the display is performed in an appropriate layout according to the relevance of the display contents. A part or all of the above-mentioned processing in the display unit may be performed using, for example, AI, or may be performed without using AI. For example, the display unit inputs data on the relevance of the display contents to the generation AI, and can cause the generation AI to adjust the display layout based on the relevance. As a result, the display unit can display in an appropriate layout according to the relevance of the display contents.

[0095] The voice conversion unit can estimate the user's emotion and adjust the tone and speed of the voice based on the estimated emotion. The voice conversion unit, for example, estimates the user's emotion and adjusts the tone and speed of the voice based on the estimated emotion. For example, the voice conversion unit generates a voice with a calm tone when the user is nervous. The voice conversion unit can also generate a voice with a bright tone when the user is relaxed. Furthermore, the voice conversion unit can also generate a voice at a fast speed when the user is in a hurry. In this way, a voice is generated at an appropriate tone and speed according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the voice conversion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the voice conversion unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the tone and speed of the voice based on the emotion. This allows the voice conversion unit to generate voice with an appropriate tone and speed according to the user's emotions.

[0096] The voice conversion unit can adjust the level of detail of the voice based on the level of detail of the sign language movement. The voice conversion unit adjusts the level of detail of the voice based on the level of detail of the sign language movement, for example. For example, when the sign language movement is large, the voice conversion unit generates a detailed voice. Also, when the sign language movement is small, the voice conversion unit can generate a concise voice. Furthermore, when the sign language movement is fast, the voice conversion unit can generate a voice at a fast speed. As a result, a voice is generated with an appropriate level of detail according to the sign language movement. A part or all of the above-mentioned processing in the voice conversion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the voice conversion unit inputs data of the sign language movement to the generation AI and causes the generation AI to adjust the level of detail. As a result, the voice conversion unit can generate a voice with an appropriate level of detail according to the sign language movement.

[0097] The voice conversion unit can apply different voice conversion algorithms depending on the category of the sign language. For example, the voice conversion unit applies different voice conversion algorithms depending on the category of the sign language. For example, the voice conversion unit applies a natural voice conversion algorithm to the sign language of everyday conversation. In addition, the voice conversion unit can also apply a specialized voice conversion algorithm to the sign language including technical terms. Furthermore, the voice conversion unit can also apply a voice conversion algorithm that emphasizes emotions to the sign language that expresses emotions. As a result, a voice is generated with an appropriate algorithm according to the category of the sign language. A part or all of the above-mentioned processing in the voice conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice conversion unit inputs data of the category of the sign language to the generation AI and causes the generation AI to apply the voice conversion algorithm based on the category. As a result, the voice conversion unit can generate a voice with an appropriate algorithm according to the category of the sign language.

[0098] The voice conversion unit can estimate the user's emotion and adjust the length of the voice based on the estimated emotion. The voice conversion unit, for example, estimates the user's emotion and adjusts the length of the voice based on the estimated emotion. For example, the voice conversion unit generates a short voice when the user is in a hurry. In addition, the voice conversion unit can also generate a detailed voice when the user is relaxed. Furthermore, the voice conversion unit can also generate a voice with an emphasis on the emotion when the user is excited. As a result, a voice is generated with an appropriate length according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the voice conversion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the voice conversion unit inputs the user's emotion data to the generation AI and causes the generation AI to adjust the length of the voice based on the emotion. As a result, the voice conversion unit can generate a voice with an appropriate length according to the user's emotion.

[0099] The voice conversion unit can determine the priority of the voices based on the time of submission of the sign language. The voice conversion unit, for example, determines the priority of the voices based on the time of submission of the sign language. For example, the voice conversion unit converts the latest sign language into voice with priority. The voice conversion unit can also convert older sign language into voice with priority. Furthermore, the voice conversion unit can convert the sign language into voice in chronological order based on the submission time. As a result, voice is generated with an appropriate priority according to the submission time of the sign language. A part or all of the above-mentioned processing in the voice conversion unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice conversion unit inputs data on the submission time of the sign language to the generation AI, and causes the generation AI to execute the determination of the priority of the voices based on the submission time. As a result, the voice conversion unit can generate voice with an appropriate priority according to the submission time of the sign language.

[0100] The speech conversion unit can adjust the order of the speech based on the relevance of the sign languages. The speech conversion unit, for example, adjusts the order of the speech based on the relevance of the sign languages. For example, the speech conversion unit groups related sign languages ​​and converts them into speech. The speech conversion unit can also convert less related sign languages ​​into speech in a separate section. Furthermore, the speech conversion unit can place more related sign languages ​​close together and convert them into speech. As a result, speech is generated in an appropriate order according to the relevance of the sign languages. A part or all of the above-mentioned processing in the speech conversion unit may be performed, for example, using AI, or may be performed without using AI. For example, the speech conversion unit can input data on the relevance of the sign languages ​​to the generation AI and cause the generation AI to adjust the order of the speech based on the relevance. As a result, the speech conversion unit can generate speech in an appropriate order according to the relevance of the sign languages.

[0101] The playback unit can estimate the user's emotion and adjust the playback volume and tone based on the estimated emotion. The playback unit, for example, estimates the user's emotion and adjusts the playback volume and tone based on the estimated emotion. For example, the playback unit plays back in a calm tone when the user is nervous. The playback unit can also play back in a bright tone when the user is relaxed. Furthermore, the playback unit can also play back at a fast speed when the user is in a hurry. As a result, playback is performed at an appropriate volume and tone according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the playback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the playback unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the playback volume and tone based on the emotion. As a result, the playback unit can play back at an appropriate volume and tone according to the user's emotion.

[0102] The playback unit can adjust the priority of playback based on the importance of the playback content. The playback unit adjusts the priority of playback based on, for example, the importance of the playback content. For example, the playback unit gives priority to important information for playback. The playback unit can also postpone playback of information with a low priority. Furthermore, the playback unit can also play back urgent information first. As a result, playback is performed with an appropriate priority according to the importance of the playback content. A part or all of the above-mentioned processing in the playback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the playback unit can input data of the playback content to the generation AI and cause the generation AI to adjust the priority of playback based on the importance. As a result, the playback unit can play back with an appropriate priority according to the importance of the playback content.

[0103] The playback unit can apply different playback styles depending on the category of the playback content. The playback unit applies different playback styles depending on the category of the playback content, for example. For example, the playback unit plays important notifications with emphasis. The playback unit can also play general information in a normal tone. Furthermore, the playback unit can also play supplementary information discreetly. As a result, playback is performed in an appropriate style depending on the category of the playback content. Some or all of the above-mentioned processing in the playback unit may be performed using, for example, AI, or may be performed without using AI. For example, the playback unit can input data of the playback content to the generation AI and cause the generation AI to apply a playback style based on the category. As a result, the playback unit can play in an appropriate style depending on the category of the playback content.

[0104] The playback unit can estimate the user's emotion and adjust the length of playback based on the estimated emotion. The playback unit, for example, estimates the user's emotion and adjusts the length of playback based on the estimated emotion. For example, the playback unit provides short playback when the user is in a hurry. The playback unit can also provide detailed playback when the user is relaxed. Furthermore, the playback unit can also provide playback with emphasized emotion when the user is excited. This allows playback to be performed at an appropriate length according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the playback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the playback unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the length of playback based on the emotion. This allows the playback unit to perform playback at an appropriate length according to the user's emotion.

[0105] The playback unit can adjust the order of playback based on the submission time of the playback content. The playback unit adjusts the order of playback based on, for example, the submission time of the playback content. For example, the playback unit plays the latest information first. The playback unit can also play old information later. Furthermore, the playback unit can also play information in chronological order based on the submission time. This allows playback to be performed in an appropriate order according to the submission time of the playback content. A part or all of the above-mentioned processing in the playback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the playback unit can input data on the submission time of the playback content to the generation AI and cause the generation AI to adjust the order of playback based on the submission time. This allows the playback unit to play in an appropriate order according to the submission time of the playback content.

[0106] The playback unit can adjust the layout of playback based on the relevance of the playback contents. The playback unit adjusts the layout of playback based on the relevance of the playback contents, for example. For example, the playback unit groups related information and plays it back. The playback unit can also play back information with low relevance in a separate section. Furthermore, the playback unit can also play back information with high relevance arranged nearby. As a result, playback is performed with an appropriate layout according to the relevance of the playback contents. A part or all of the above-mentioned processing in the playback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the playback unit can input data on the relevance of the playback contents to the generation AI and cause the generation AI to adjust the playback layout based on the relevance. As a result, the playback unit can play back with an appropriate layout according to the relevance of the playback contents.

[0107] The control unit can estimate the user's emotion and adjust the operation of the entire system based on the estimated emotion. The control unit, for example, estimates the user's emotion and adjusts the operation of the entire system based on the estimated emotion. For example, the control unit adjusts the operation of the entire system to a calm pace when the user is nervous. The control unit can also adjust the operation of the entire system smoothly when the user is relaxed. Furthermore, the control unit can also quickly adjust the operation of the entire system when the user is in a hurry. In this way, the entire system is adjusted with an appropriate operation according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the control unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the control unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the operation of the entire system based on the emotion. In this way, the control unit can adjust the entire system with an appropriate operation according to the user's emotion.

[0108] The control unit can select the optimal control method by referring to the operation history of each unit. The control unit, for example, selects the optimal control method by referring to the operation history of each unit. For example, the control unit analyzes the past operation history of each unit and selects the optimal control method. The control unit can also prioritize and control frequently used functions from the operation history of each unit. Furthermore, the control unit can also select an efficient control method based on the operation history of each unit. As a result, the optimal control method based on the operation history of each unit is selected. A part or all of the above-mentioned processing in the control unit may be performed, for example, using AI, or may be performed without using AI. For example, the control unit can input data of the operation history of each unit to the generation AI and cause the generation AI to select the optimal control method based on the operation history. As a result, the control unit can select the optimal control method based on the operation history of each unit.

[0109] The control unit can optimize the control algorithm according to the operating status of each unit. The control unit optimizes the control algorithm according to, for example, the operating status of each unit. For example, the control unit monitors the current operating status of each unit in real time and optimizes the control algorithm. The control unit can also dynamically adjust the control algorithm according to the operating status of each unit. Furthermore, the control unit can analyze the operating status of each unit and apply the optimal control algorithm. As a result, the optimal control algorithm according to the operating status of each unit is applied. A part or all of the above-mentioned processing in the control unit may be performed, for example, using AI, or may be performed without using AI. For example, the control unit can input data on the operating status of each unit to the generation AI and cause the generation AI to optimize the control algorithm based on the operating status. As a result, the control unit can apply the optimal control algorithm according to the operating status of each unit.

[0110] The control unit can estimate the user's emotion and determine the priority of control based on the estimated emotion. The control unit, for example, estimates the user's emotion and determines the priority of control based on the estimated emotion. For example, when the user is nervous, the control unit prioritizes and controls important functions. Also, when the user is relaxed, the control unit can smoothly perform overall control. Furthermore, when the user is in a hurry, the control unit can quickly control necessary functions. As a result, control is performed with appropriate priority according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the control unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the control unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of control based on the emotion. As a result, the control unit can perform control with appropriate priority according to the user's emotion.

[0111] The control unit can adjust the order of control based on the operation timing of each unit. The control unit adjusts the order of control based on, for example, the operation timing of each unit. For example, the control unit adjusts the order of control based on the latest operation timing of each unit. The control unit can also determine an efficient control order based on the operation timing of each unit. Furthermore, the control unit can set an optimal control order taking into account the operation timing of each unit. As a result, control is performed in an appropriate order according to the operation timing of each unit. A part or all of the above-mentioned processing in the control unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the control unit can input data on the operation timing of each unit to the generation AI and cause the generation AI to adjust the order of control based on the operation timing. As a result, the control unit can perform control in an appropriate order according to the operation timing of each unit.

[0112] The control unit can adjust the layout of the control based on the relevance of each part. The control unit adjusts the layout of the control based on the relevance of each part, for example. For example, the control unit groups and controls each related part. The control unit can also control each part with low relevance to a different section. Furthermore, the control unit can control each part with high relevance by arranging them close to each other. As a result, control is performed with an appropriate layout according to the relevance of each part. A part or all of the above-mentioned processing in the control unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the control unit can input data on the relevance of each part to the generation AI and cause the generation AI to adjust the layout of the control based on the relevance. As a result, the control unit can perform control with an appropriate layout according to the relevance of each part.

[0113] The multilingual support unit can estimate the user's emotion and determine the priority order of multilingual support based on the estimated emotion. The multilingual support unit, for example, estimates the user's emotion and determines the priority order of multilingual support based on the estimated emotion. For example, when the user is nervous, the multilingual support unit prioritizes the main language. In addition, the multilingual support unit can also support multiple languages ​​when the user is relaxed. Furthermore, when the user is in a hurry, the multilingual support unit can also quickly support the main language. In this way, multilingual support is performed with appropriate priority order according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the multilingual support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the multilingual support unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority order of multilingual support based on the emotion. This allows the multilingual support unit to provide multilingual support with appropriate priorities according to the user's feelings.

[0114] The multilingual support unit can select the optimal multilingual support method by referring to the frequency of use of each language. The multilingual support unit selects the optimal multilingual support method by referring to the frequency of use of each language, for example. For example, the multilingual support unit prioritizes languages ​​that are used frequently. The multilingual support unit can also prioritize languages ​​that are used less frequently. Furthermore, the multilingual support unit can select an efficient multilingual support method based on the frequency of use of each language. This allows the optimal multilingual support method based on the frequency of use of each language to be selected. A part or all of the above-mentioned processing in the multilingual support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the multilingual support unit can input data on the frequency of use of each language to the generation AI and cause the generation AI to select the optimal multilingual support method based on the frequency of use. This allows the multilingual support unit to select the optimal multilingual support method based on the frequency of use of each language.

[0115] The multilingual support unit can apply different multilingual support methods according to the characteristics of each language. The multilingual support unit can apply different multilingual support methods according to the characteristics of each language, for example. For example, the multilingual support unit applies a natural translation algorithm to English. The multilingual support unit can also apply a translation algorithm that takes into account the context to Japanese. Furthermore, the multilingual support unit can also apply a translation algorithm that emphasizes the meaning of characters to Chinese. This allows the optimal multilingual support method to be applied according to the characteristics of each language. A part or all of the above-mentioned processing in the multilingual support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the multilingual support unit can input data on the characteristics of each language to the generation AI and cause the generation AI to apply the multilingual support method based on the characteristics. This allows the multilingual support unit to apply the optimal multilingual support method according to the characteristics of each language.

[0116] The multilingual support unit can estimate the user's emotion and adjust the multilingual display method based on the estimated emotion. The multilingual support unit, for example, estimates the user's emotion and adjusts the multilingual display method based on the estimated emotion. For example, the multilingual support unit provides a display method with calm colors when the user is nervous. The multilingual support unit can also provide a display method with bright colors when the user is relaxed. Furthermore, the multilingual support unit can also provide a display method with high visibility when the user is in a hurry. In this way, multilingual support is performed with an appropriate display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the multilingual support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the multilingual support unit can input the user's emotion data to the generation AI and cause the generation AI to execute adjustment of the multilingual display method based on the emotion. This allows the multilingual support unit to provide multilingual support using an appropriate display method according to the user's emotions.

[0117] The multilingual support unit can adjust the order of multilingual support based on the submission time of each language. The multilingual support unit adjusts the order of multilingual support based on, for example, the submission time of each language. For example, the multilingual support unit supports the newest language first. The multilingual support unit can also support older languages ​​later. Furthermore, the multilingual support unit can support languages ​​in chronological order based on the submission time. This allows multilingual support to be performed in an appropriate order according to the submission time of each language. A part or all of the above-mentioned processing in the multilingual support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the multilingual support unit can input data on the submission time of each language to the generation AI and cause the generation AI to adjust the order of multilingual support based on the submission time. This allows the multilingual support unit to perform multilingual support in an appropriate order according to the submission time of each language.

[0118] The multilingual support unit can adjust the multilingual layout based on the relevance of each language. The multilingual support unit adjusts the multilingual layout based on the relevance of each language, for example. For example, the multilingual support unit groups related languages ​​to support them. The multilingual support unit can also support languages ​​with low relevance in a different section. Furthermore, the multilingual support unit can also support languages ​​with high relevance by arranging them close to each other. As a result, multilingual support is performed with an appropriate layout according to the relevance of each language. A part or all of the above-mentioned processing in the multilingual support unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the multilingual support unit can input data on the relevance of each language to the generation AI and cause the generation AI to adjust the multilingual layout based on the relevance. As a result, the multilingual support unit can support multilingual support with an appropriate layout according to the relevance of each language.

[0119] The sign language recognition unit can estimate the user's emotion and adjust the accuracy of the sign language recognition based on the estimated emotion. The sign language recognition unit, for example, estimates the user's emotion and adjusts the accuracy of the sign language recognition based on the estimated emotion. For example, the sign language recognition unit increases the accuracy of the sign language recognition when the user is nervous. Also, the sign language recognition unit can keep the accuracy of the sign language recognition normal when the user is relaxed. Furthermore, the sign language recognition unit can quickly adjust the accuracy of the sign language recognition when the user is in a hurry. This allows the sign language recognition to be performed with an appropriate accuracy according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the sign language recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sign language recognition unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the accuracy of the sign language recognition based on the emotion. This enables the sign language recognition unit to perform sign language recognition with appropriate accuracy according to the user's emotions.

[0120] The sign language recognition unit can optimize the algorithm of the sign language recognition based on the level of detail of the sign language movement. The sign language recognition unit optimizes the algorithm of the sign language recognition based on the level of detail of the sign language movement, for example. For example, the sign language recognition unit applies a detailed recognition algorithm when the sign language movement is large. Also, the sign language recognition unit can apply a simple recognition algorithm when the sign language movement is small. Furthermore, the sign language recognition unit can apply a recognition algorithm at a high speed when the sign language movement is fast. As a result, the sign language recognition is performed with an optimal algorithm according to the sign language movement. A part or all of the above-mentioned processing in the sign language recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sign language recognition unit inputs data of the sign language movement to the generation AI, and causes the generation AI to optimize the recognition algorithm based on the level of detail of the movement. As a result, the sign language recognition unit can perform the sign language recognition with an optimal algorithm according to the sign language movement.

[0121] The sign language recognition unit can apply different sign language recognition algorithms depending on the category of the sign language. For example, the sign language recognition unit applies different sign language recognition algorithms depending on the category of the sign language. For example, the sign language recognition unit applies a natural recognition algorithm to the sign language of everyday conversation. In addition, the sign language recognition unit can also apply a specialized recognition algorithm to the sign language including technical terms. Furthermore, the sign language recognition unit can also apply a recognition algorithm that emphasizes emotions to the sign language that expresses emotions. As a result, the sign language recognition is performed with an optimal algorithm depending on the category of the sign language. A part or all of the above-mentioned processing in the sign language recognition unit may be performed using, for example, AI, or may be performed without using AI. For example, the sign language recognition unit can input data of the category of the sign language to the generation AI and cause the generation AI to apply the recognition algorithm based on the category. As a result, the sign language recognition unit can perform the sign language recognition with an optimal algorithm depending on the category of the sign language.

[0122] The sign language recognition unit can estimate the user's emotion and determine the priority of sign language recognition based on the estimated emotion. The sign language recognition unit, for example, estimates the user's emotion and determines the priority of sign language recognition based on the estimated emotion. For example, when the user is nervous, the sign language recognition unit prioritizes recognition of important sign language. In addition, when the user is relaxed, the sign language recognition unit can also smoothly recognize the overall sign language. Furthermore, when the user is in a hurry, the sign language recognition unit can also quickly recognize the necessary sign language. Thereby, the sign language recognition is performed with an appropriate priority according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the sign language recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sign language recognition unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of sign language recognition based on the emotion. This allows the sign language recognition unit to perform sign language recognition with an appropriate priority order according to the user's emotions.

[0123] The sign language recognition unit can adjust the order of sign language recognition based on the time of submission of the sign language. The sign language recognition unit adjusts the order of sign language recognition based on, for example, the time of submission of the sign language. For example, the sign language recognition unit recognizes the latest sign language first. Also, the sign language recognition unit can recognize older sign languages ​​later. Furthermore, the sign language recognition unit can recognize sign languages ​​in chronological order based on the time of submission. As a result, the sign language recognition is performed in an appropriate order according to the time of submission of the sign language. A part or all of the above-mentioned processing in the sign language recognition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the sign language recognition unit inputs data on the time of submission of the sign language to the generation AI, and causes the generation AI to adjust the order of sign language recognition based on the time of submission. As a result, the sign language recognition unit can perform sign language recognition in an appropriate order according to the time of submission of the sign language.

[0124] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0125] The smart glasses system may further include a health monitoring unit that monitors the health condition of the user. The health monitoring unit may, for example, use sensors built into the smart glasses to measure the user's vital signs, such as heart rate, blood pressure, and body temperature, in real time. This allows the user's health condition to be constantly monitored, and an alert may be issued if an abnormality is detected. For example, if the heart rate rises suddenly, the health monitoring unit may display a notification encouraging the user to take a rest. In addition, if the blood pressure is high, health advice may be provided. Furthermore, if the body temperature is abnormally high, a notification recommending a visit to a medical institution may be displayed. This supports the user's health management and reduces health risks.

[0126] The smart glasses system may further include a location information acquisition unit that acquires the user's location information. The location information acquisition unit acquires the user's current location in real time, for example, using a GPS. This makes it possible to provide optimal route guidance and surrounding information based on the user's location information. For example, when a user heads to a destination, the location information acquisition unit calculates an optimal route and displays it on the display of the smart glasses. In addition, when a user visits a tourist spot, the location information acquisition unit can also provide information on nearby tourist spots and restaurants. Furthermore, in the event of an emergency, a quick response is possible based on the user's location information. This makes it possible to improve the user's convenience.

[0127] The smart glasses system may further include an activity recording unit that records the user's activities. The activity recording unit may use, for example, an acceleration sensor or a gyro sensor built into the smart glasses to record the user's number of steps, distance traveled, calories burned, and the like. This allows the user's activity level to be understood and used for health management. For example, the activity recording unit may record the number of steps the user takes in a day and display the degree of goal achievement. It may also calculate the distance traveled and calories burned and provide feedback to the user. Furthermore, the activity recording unit may analyze the user's exercise pattern and provide advice to resolve lack of exercise. This may support the user's health maintenance.

[0128] The smart glasses system may further include an eye tracking unit that tracks the user's gaze. The eye tracking unit may use, for example, a camera built into the smart glasses to track the user's gaze movement in real time. This allows the user to identify the object that the user is paying attention to and provide related information. For example, when the user is looking at a particular building, the eye tracking unit may display information about the building on the display of the smart glasses. In addition, when the user is looking at a product, the eye tracking unit may display detailed information and reviews about the product. Furthermore, the eye tracking unit may analyze the user's gaze movement and provide advice to reduce visual fatigue. This may improve the user's convenience.

[0129] The smart glasses system may further include a voice recognition unit that recognizes voice commands from the user. The voice recognition unit recognizes the user's voice commands in real time, for example, using a microphone in the smart glasses. This allows the user to operate various functions of the smart glasses using voice commands. For example, when the user says "Tell me the weather forecast," the voice recognition unit recognizes the command and displays the weather forecast. Also, when the user says "Play music," the voice recognition unit can recognize the command and play music. Furthermore, the voice recognition unit can learn the user's voice commands and perform more accurate recognition. This can improve the operability for the user.

[0130] The transcription unit can estimate the user's emotions and adjust the transcription speed based on the estimated emotions. For example, when the user is nervous, the transcription unit can slow down the transcription speed to make it easier for the user to understand the content. When the user is relaxed, the transcription unit can also speed up the transcription speed to promote smooth communication. Furthermore, when the user is in a hurry, the transcription unit can maximize the transcription speed to provide information quickly. This allows transcription to be performed at an appropriate speed according to the user's emotions.

[0131] The characterization unit can estimate the user's emotion and adjust the font style of the characterization based on the estimated emotion. For example, if the user is nervous, the characterization unit can use a calm font style to relieve the user's tension. Also, if the user is relaxed, the characterization unit can use a bright font style to maintain the user's sense of relaxation. Furthermore, if the user is excited, the characterization unit can use an emphasized font style to express the user's excitement. In this way, characterization is performed in an appropriate font style according to the user's emotion.

[0132] The text conversion unit can estimate the user's emotion and adjust the color of the text conversion based on the estimated emotion. For example, if the user is nervous, the text conversion unit can use a calm color to relieve the user's tension. Also, if the user is relaxed, the text conversion unit can use a bright color to maintain the user's sense of relaxation. Furthermore, if the user is excited, the text conversion unit can use an emphasized color to express the user's excitement. In this way, text conversion is performed in an appropriate color according to the user's emotion.

[0133] The transcription unit can estimate the user's emotions and summarize the transcription content based on the estimated emotions. For example, if the user is nervous, the transcription unit can highlight important points and summarize briefly. If the user is relaxed, the transcription unit can provide detailed content to allow the user to fully understand the information. Furthermore, if the user is in a hurry, the transcription unit can summarize only the most important information and provide information quickly. This allows for an appropriate summary according to the user's emotions.

[0134] The text conversion unit can estimate the user's emotion and adjust the text conversion expression method based on the estimated emotion. For example, if the user is nervous, the text conversion unit uses a soft expression to relieve the user's tension. Also, if the user is relaxed, the text conversion unit can use a bright expression to maintain the user's sense of relaxation. Furthermore, if the user is excited, the text conversion unit can use an emphasized expression to express the user's excitement. In this way, text conversion is performed using an appropriate expression method according to the user's emotion.

[0135] The process flow of the second embodiment will be briefly described below.

[0136] Step 1: The transcription unit transcribes the other person's voice. For example, the transcription unit picks up the other person's voice with the microphone of the smart glasses, and the generation AI analyzes the voice and converts it into text. The generation AI converts the voice into text, for example, using voice recognition technology. Step 2: The display unit displays the content that has been converted into text by the conversion unit. For example, the display unit displays text on a display of smart glasses. The display provides information without obstructing the view by displaying text on a transparent screen, for example. Step 3: The speech conversion unit converts the sign language into a speech signal. For example, the speech conversion unit detects sign language movements using the smart glasses' camera, and the generation AI analyzes the movements and converts them into speech. For example, the generation AI uses sign language recognition technology to analyze the sign language movements and generate the corresponding speech. Step 4: The playback unit plays the audio converted by the audio conversion unit. For example, the playback unit plays the audio from a speaker in the smart glasses. The speaker may be, for example, a type worn on the ear or a bone conduction speaker. Step 5: The control unit controls the text conversion unit and the voice conversion unit. For example, the control unit executes a control algorithm for managing the operations of each unit and realizing smooth operation.

[0137] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0138] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0139] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0140] For example, the text conversion unit picks up the other party's voice with the microphone of the smart device 14, and the specific processing unit 290 of the data processing device 12 causes the generation AI to analyze the voice and convert it into text. For example, the display unit displays text on the display of the smart device 14. For example, the voice conversion unit detects sign language movements with the camera of the smart device 14, and the specific processing unit 290 of the data processing device 12 causes the generation AI to analyze the movements and convert them into voice. For example, the playback unit plays voice from the speaker of the smart device 14. For example, the control unit executes a control algorithm to integrate the operation of each unit by the control unit 46A of the smart device 14 and realize smooth operation. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0148] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0149] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0151] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0153] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0155] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0156] For example, the text conversion unit picks up the other person's voice with the microphone of the smart glasses 214, and the generation AI analyzes the voice and converts it into text by the specific processing unit 290 of the data processing device 12. For example, the display unit displays text on the display of the smart glasses 214. For example, the voice conversion unit detects sign language movements with the camera of the smart glasses 214, and the generation AI analyzes the movements and converts them into voice by the specific processing unit 290 of the data processing device 12. For example, the playback unit plays voice from the speaker of the smart glasses 214. For example, the control unit executes a control algorithm to integrate the operation of each unit by the control unit 46A of the smart glasses 214 and realize smooth operation. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0157] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0158] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0159] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0164] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0167] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0171] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0172] For example, the text conversion unit picks up the other party's voice with the microphone of the headset terminal 314, and the specific processing unit 290 of the data processing device 12 causes the generation AI to analyze the voice and convert it into text. For example, the display unit displays text on the display of the headset terminal 314. For example, the voice conversion unit detects sign language movements with the camera of the headset terminal 314, and the specific processing unit 290 of the data processing device 12 causes the generation AI to analyze the movements and convert them into voice. For example, the playback unit plays voice from the speaker of the headset terminal 314. For example, the control unit executes a control algorithm to integrate the operation of each unit by the control unit 46A of the headset terminal 314 and realize smooth operation. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0173] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0174] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0175] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0176] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0177] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0179] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0180] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0181] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0182] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0183] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0184] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0185] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0186] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0187] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0188] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0189] For example, the text conversion unit picks up the other person's voice with the microphone of the robot 414, and the generation AI analyzes the voice and converts it into text by the specific processing unit 290 of the data processing device 12. For example, the display unit displays text on the display of the robot 414. For example, the voice conversion unit detects sign language movements with the camera of the robot 414, and the generation AI analyzes the movements and converts them into voice by the specific processing unit 290 of the data processing device 12. For example, the playback unit plays voice from the speaker of the robot 414. For example, the control unit executes a control algorithm to integrate the operation of each unit by the control unit 46A of the robot 414 and realize smooth operation. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0190] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0191] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0192] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0193] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0194] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0195] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0196] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0197] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0198] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0199] In addition, 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 upon request from the data processing device 12.

[0200] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0201] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0202] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0203] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0204] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0205] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0206] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0207] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0208] (Appendix 1) a transcription unit for converting voice input into text; a display unit that displays the contents that have been converted into text by the conversion unit; a speech conversion unit for converting sign language into a speech signal; a playback unit that plays back the voice converted by the voice conversion unit; A control unit that controls the character conversion unit and the voice conversion unit. A system characterized by: (Appendix 2) The character conversion unit includes: Equipped with a language support section to support multiple languages 2. The system of claim 1, (Appendix 3) The voice conversion unit is Equipped with a sign language detection unit for detecting sign language movements 2. The system of claim 1, (Appendix 4) The display unit is Displaying text on a smart glasses display 2. The system of claim 1, (Appendix 5) The reproducing unit is Play audio through the smart glasses speaker 2. The system of claim 1, (Appendix 6) The character conversion unit includes: Estimate the other person's emotions and adjust the way the text is expressed based on those emotions 2. The system of claim 1, (Appendix 7) The character conversion unit includes: Adjust transcription detail based on tone and speed of voice 2. The system of claim 1, (Appendix 8) The character conversion unit includes: Automatically filters background noise to improve transcription accuracy 2. The system of claim 1, (Appendix 9) The character conversion unit includes: Estimate the other person's emotions and determine the priority of transcription based on the estimated emotions 2. The system of claim 1, (Appendix 10) The character conversion unit includes: Automatically detects voice language and transcribes into the appropriate language 2. The system of claim 1, (Appendix 11) The character conversion unit includes: Adjusting the transcription based on the speaker's voice attributes 2. The system of claim 1, (Appendix 12) The display unit is Infer the user's emotions and adjust the font and color of the display based on the estimated emotions. 2. The system of claim 1, (Appendix 13) The display unit is Adjust display priority based on the importance of the displayed content 2. The system of claim 1, (Appendix 14) The display unit is Apply different visual styles depending on the category of content displayed 2. The system of claim 1, (Appendix 15) The display unit is Infer user emotion and adjust display length based on the estimated emotion 2. The system of claim 1, (Appendix 16) The display unit is Adjust the order of display based on when the display content was submitted 2. The system of claim 1, (Appendix 17) The display unit is Adjust the layout of your display based on the relevance of what you're viewing 2. The system of claim 1, (Appendix 18) The voice conversion unit is Infer the user's emotions and adjust the tone and speed of the speech based on the inferred emotions 2. The system of claim 1, (Appendix 19) The voice conversion unit is Adjust audio detail based on sign movement detail 2. The system of claim 1, (Appendix 20) The voice conversion unit is Applying different speech conversion algorithms depending on sign language categories 2. The system of claim 1, (Appendix 21) The voice conversion unit is Estimate the user's emotion and adjust the length of the audio based on the estimated emotion 2. The system of claim 1, (Appendix 22) The voice conversion unit is Prioritize audio based on when signs were submitted 2. The system of claim 1, (Appendix 23) The voice conversion unit is Adjust the order of audio based on sign relevance 2. The system of claim 1, (Appendix 24) The reproducing unit is Estimate the user's emotions and adjust the playback volume and tone based on the estimated emotions 2. The system of claim 1, (Appendix 25) The reproducing unit is Adjust playback priorities based on the importance of what's being played 2. The system of claim 1, (Appendix 26) The reproducing unit is Apply different playback styles depending on the category of playback content 2. The system of claim 1, (Appendix 27) The reproducing unit is Estimate the user's emotion and adjust the playback length based on the estimated emotion 2. The system of claim 1, (Appendix 28) The reproducing unit is Adjust the order of playback based on when playback content was submitted 2. The system of claim 1, (Appendix 29) The reproducing unit is Adjust playback layout based on playback content relevance 2. The system of claim 1, (Appendix 30) The control unit is Infer user emotions and adjust the behavior of the entire system based on the inferred emotions 2. The system of claim 1, (Appendix 31) The control unit is Select the optimal control method by referring to the operation history of each part 2. The system of claim 1, (Appendix 32) The control unit is Optimize the control algorithm according to the operating conditions of each part 2. The system of claim 1, (Appendix 33) The control unit is Estimate the user's emotions and determine control priorities based on the estimated emotions. 2. The system of claim 1, (Appendix 34) The control unit is Adjust the control sequence based on the operation timing of each part 2. The system of claim 1, (Appendix 35) The control unit is Adjust the layout of controls based on their relationship 2. The system of claim 1, (Appendix 36) The multilingual support unit includes: Estimate user emotions and determine priorities for multilingual support based on the estimated emotions 3. The system of claim 2, (Appendix 37) The multilingual support unit includes: Select the optimal multilingual method by referring to the frequency of use of each language 3. The system of claim 2, (Appendix 38) The multilingual support unit includes: Apply different multilingual methods according to the characteristics of each language 3. The system of claim 2, (Appendix 39) The multilingual support unit includes: Infer user emotions and adjust multilingual display methods based on the inferred emotions 3. The system of claim 2, (Appendix 40) The multilingual support unit includes: Adjust the order of multilingual support based on when each language was submitted 3. The system of claim 2, (Appendix 41) The multilingual support unit includes: Adjusting multilingual layout based on the relevance of each language 3. The system of claim 2, (Appendix 42) The sign language recognition unit is Estimate the user's emotions and adjust the accuracy of sign language recognition based on the estimated emotions. 4. The system of claim 3. (Appendix 43) The sign language recognition unit is Optimizing sign language recognition algorithms based on the level of detail of sign movements 4. The system of claim 3. (Appendix 44) The sign language recognition unit is Apply different sign language recognition algorithms depending on the sign language category 4. The system of claim 3. (Appendix 45) The sign language recognition unit is Estimate the user's emotions and determine the priority of sign language recognition based on the estimated emotions. 4. The system of claim 3. (Appendix 46) The sign language recognition unit is Adjust the order of sign recognition based on when the sign was submitted 4. The system of claim 3. [Explanation of symbols]

[0209] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a transcription unit that converts voice input into text; a display unit that displays the contents that have been converted into text by the conversion unit; a speech conversion unit for converting sign language into a speech signal; a playback unit that plays back the voice converted by the voice conversion unit; a control unit that controls the characterization unit and the voice conversion unit; A system characterized by:

2. The character conversion unit includes: Equipped with a language support section to support multiple languages 2. The system of claim 1 .

3. The voice conversion unit is Equipped with a sign language detection unit for detecting sign language movements 2. The system of claim 1 .

4. The display unit is Displaying text on a smart glasses display 2. The system of claim 1 .

5. The reproducing unit includes: Play audio through the smart glasses speaker 2. The system of claim 1 .

6. The character conversion unit includes: Estimate the other person's emotions and adjust the way the text is expressed based on those emotions 2. The system of claim 1 .

7. The character conversion unit includes: Adjust transcription detail based on tone and speed of voice 2. The system of claim 1 .

8. The character conversion unit includes: Automatically filters background noise to improve transcription accuracy 2. The system of claim 1 .

9. The character conversion unit includes: Estimate the other person's emotions and determine the priority of transcription based on the estimated emotions 2. The system of claim 1 .

10. The character conversion unit includes: Automatically detects voice language and transcribes into the appropriate language 2. The system of claim 1 .

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